Agricultural appraisal - Defra, UK

INVESTIGATE THE LONGER-TERM EFFECTS ON FARM BUSINESSES OF A bTB BREAKDOWN Project SE3120 Draft final report for Defra Draft: 17 October 2008 Contact: Martin Turner, Department of Geography, University of Exeter Laver Building, North Park Road, Exeter, EX4 4QE Tel: 01392 263833 Email: [email protected]

University of Exeter/ADAS UK Ltd

Longer-term effects of a bTB breakdown: draft report 17 October 2008

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Acknowledgements and disclaimer

This research project was led by Martin Turner, University of Exeter and Mark

Temple, ADAS UK Ltd. The study was designed by a small team comprising Martin

Turner, Dr Keith Howe and Dr Emma Jeanes (University of Exeter), Mark Temple

and David Boothby (ADAS UK Ltd) and Paul Watts (Somerset Partnership NHS and

Social Care Trust). Martin Turner has acted as the principal author and editor of this

report, and carries the responsibility for any errors; but a number of people made

specific contributions to the report and particular acknowledgements are due to:

Dr Keith Howe, University of Exeter (Centre for Rural Policy Research): Literature

review (Chapter 2);

David Boothby, ADAS UK Ltd: management of the ADAS team for the farmers’

interview survey;

Dr Emma Jeanes, University of Exeter (Department of Management): Case studies of

GPs’ practices (part of Chapter 5);

Professor Richard Bennett and colleagues, University of Reading: Choice experiment

looking at farmers’ WTP for a bTB vaccine (Chapter 6 and Appendix E);

Lucy Wilson, ADAS UK Ltd (Environment Group): Analysis and reporting on the

GIS study of cattle populations (Appendix E);

Paul Watts, Somerset Partnership NHS and Social Care Trust: Consultant for the

GHQ-12 study.

Farm interviews were carried out by Nicola Buckingham, Dennis Chapple, Nerys

Davies, Susie Felix, Marc Jones, Fiona McVicar, Giles Martin, Andrew Sheppard and

Karen Wheeler.

Data management and analysis has been undertaken by Donald Barr, Theo Economou

and Kevin Mawdesley for the University of Exeter.

The authors acknowledge with thanks those farmers who contributed their time to

help in various stages of the research, and in some cases to more than one of the

following research activities: the stakeholders’ consultation, the face-to-face interview

survey, the postal survey and the choice experiment study; without their help,

willingly given, this research could not have been completed. In addition the

contributions made by (a) a wide range of stakeholders and (b) a number of GPs in

busy rural practices are also acknowledged with thanks, as also are the helpful

responses received from two insurance firms offering bTB cover. Particular thanks

are due to the following people and institutions for their invaluable help in significant

ways:

Birgit Austin and colleagues at the UK Data Archive

Andrew Woodend, Selina Matthews and colleagues, Defra’s Farm Economics Unit

Andy Mitchell and colleagues at the Veterinary Laboratories Agency

Phil Robertson and FBS Centre colleagues, Rural Business Research

Tony O’Regan and FBS team colleagues at Aberystwyth University

The views expressed in this report are those of the authors and are not necessarily

shared by other members of the University of Exeter or the University as a whole;

other members of ADAS UK Ltd or ADAS UK Ltd as a whole; other members of the

Somerset Partnership NHS and Social Care Trust or the Somerset Partnership as a

whole; other members of the University or Reading or the University as a whole; or

by Defra.



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Contents

Page

Acknowledgements iii List of tables vii List of figures ix List of farm case studies xi Glossary of abbreviations x Executive summary 1 1 Introduction 11

Background 11 The study brief 12 The study method 13 The research team 19

2 Review of the evidence from other research 20

Defining the longer term 20 Trends in cattle farming 21 Effects of stress in farming 24 Longer-term effects of a bTB breakdown 27 Badgers and bio-security 30 Conclusions 32

3 Factors which result in longer-term impacts 33

Introduction 33 Regional changes in cattle farming: the GIS study 33 Farm-level evidence for bTB as a driver of strategic change 36 Insuring against a bTB breakdown: practical issues 45 The human dimension: key findings 49 Discussion and conclusions 50

4 Estimated longer-term economic effects 55

Introduction 55 The stakeholders’ consultation: pointers to farm economic problems 60 The longer-term economic impacts from bTB: evidence from the FBS 62 Evidence from the farm interview survey 90



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Summary of responses from the postal survey 104 Conclusions 105

5 Understanding health and social effects 109

Introduction 109 The stakeholder consultation: pointers to farming stress 111 The postal survey: exploring the human impacts of a bTB breakdown 113 Interviews with General Practitioners: some case studies 131 The farmers’ view: results from the interview survey 138 Conclusions 144

6 Exploring farmers’ WTP for a bTB vaccine 146

Background 146 Study objectives and methodology 146 Study results 157 Discussion and conclusions 159

7 Discussion and conclusions 161

Review and reflection 161 Concluding summary 159

Bibliography 166

Appendices 171

A Stakeholder consultation: overall summary 172

B Farmers’ interview survey: summary results 177

C Desk study using FBA data: summary results 213

D Postal survey: the GHQ-12 questionnaire 221

E The GIS study of changes in cattle populations 224

F The choice experiment bTB cattle vaccine questionnaire 245



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List of tables

Page

3.1 The GIS study: summary of the effects of time and bTB on outcome variables for each area studied, and all areas 34

3.2 Attitude to farming, ‘bTB driver’ farms and ‘non-bTB driver’ farms

compared 37

3.3 Farmers and their communities, ‘bTB driver’ farms and all sample

farms compared 38

3.4 Farming values and objectives, ‘bTB driver’ farms and all sample

farms compared 39

3.5 Expectations on succession: ‘bTB driver’ farms and all sample

farms compared 40

3.6 The farm interview sample: ‘bTB driver’ and ‘non-bTB driver’ farms,

by farm type and bTB category 41

3.7 Interviewed dairy farms: ‘bTB driver’ and ‘non-bTB driver’ farms,

by bTB category 42

3.8 Interviewed beef farms: ‘bTB driver’ and ‘non-bTB driver’ farms,

by bTB category 43

3.9 Incidence of bTB insurance, initial and subsequent breakdowns 45

3.10 Farmers’ views of the role of bTB insurance in reducing business

risk at farm level 46

3.11 Interviewed farms: ‘bTB driver’ and ‘non-bTB driver’ farms, by

farm type and bTB category 52

3.12 Factors which result in longer term impacts from a bTB breakdown:

a summary of the research findings 53

4.1 The national incidence of bTB breakdowns: an initial analysis of

the VetNet database, July 2007 56

4.2 Adverse effects of a bTB breakdown on the financial vitality of farm businesses: summary of the stakeholders’ consultation findings 62

4.3 The FBS bTB sample: numbers of years in the survey over the

period 2002/03 to 2006/07 64

4.4 The FBS bTB sample, by farm type, 2002/03 to 2006/07 65

4.5 The FBS bTB sample, by period of movement restrictions 65

4.6 Sample sizes, FBS bTB dairy farms, 2002/03 to 2006/07 67

4.7 Sample sizes, FBS bTB suckler beef farms, 2002/03 to 2006/07 79

4.8 The role of bTB as a driver of past business change 90

4.9 Farmers’ identification of the main long-term effects of a

bTB breakdown 91

4.10 Self-declared net profit from farming: interviewed farms by

bTB category 92



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4.11 Farmers’ attitudes to level of borrowing under present circumstances 93

4.12 Investment in recent bio-security improvements, by type of

improvement 94

4.13 Use of funds received for compensation/insurance 96

4.14 The farm-level impacts and financial importance of movement

restrictions 97

4.15 Wider implications of purchasing replacement breeding stock 100

4.16 Significant effects of a bTB breakdown on farm business

development plans 100

4.17 On-going problems: anticipated significant future impacts from

the past bTB breakdown 103

5.1 Adverse effects of a bTB breakdown on human health and well-being: summary of the stakeholders’ consultation findings 112

5.2 Postal survey using the GHQ-12: sample stratification 114

5.3 Illustrative example of alternative GHQ-12 scoring approaches 116

5.4 Postal survey response rate, by category of person 117

5.5 Total postal survey responses by bTB category and farm type 117

5.6 Postal survey response at farm level: farm type and bTB group 118

5.7 Detailed composition of postal survey respondents, by

responses per farm 120

5.8 Farms with two responses: analysis of responses by category 120

5.9 Farms with three responses: analysis of responses by category 121

5.10 Postal survey respondents: statistical analysis of cattle herds 122

5.11 Analysis of postal response: cattle herds by type and bTB category 123

5.12 Postal survey: years under movement restriction, mean and

median herd sizes compared 123

5.13 Periods of cattle movement restrictions by farm type 124

5.14 Years under movement restriction, by farm type 124

5.15 Percentage of respondents scoring a ‘high GHQ score’ by

category of bTB breakdown 125

5.16 ‘High GHQ scores’ by farm type and category of bTB breakdown 126

5.17 Proportions of GHQ ‘cases’ using the truncated sample of farms

with 20 or more head of cattle 128

5.18 Proportions of GHQ ‘cases’ for family members, by category of bTB breakdown 128

5.19 Proportions of GHQ ‘cases’ for farmers, by farm type and category

of bTB breakdown 129

5.20 Analysis using the Likert scale: proportions of GHQ ‘cases’ by

farm type and category of bTB breakdown 130

5.21 The non-economic impacts of a bTB breakdown: the farmers view 139



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6.1 Example CE choice set showing attributes and their levels 150

6.2 Percentage of farmers scoring at each level of agreement to

the attitude statements presented to them and their mean score 153

6.3 Farmers’ perceptions of the risk of their herd testing positive to bTB

in any one year 154

6.4 Farmers’ perceptions of the likelihood of their herds testing bTB

positive within the next three years 154

6.5 Farmers’ perceptions as to the likelihood of a severe breakdown

within the next 5 years 155

6.6 Adoption of standard bTB bio-security practices by sample farmers 156

6.7 CE estimates of WTP for a bTB cattle vaccine 157

6.8 CV estimation of WTP for a bTB cattle vaccine 158

6.9 Farmer responses to attitudinal questions 159



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List of figures

Page

1.1 Relationship between research activities (RA) and project objectives 14

3.1 The GIS study: rate of change in the mean numbers of cattle farms

per parish stratified by region and bTB incidence category 35

3.2 Interviewed dairy farms: ‘bTB driver’ and ‘non-bTB driver’ farms,

by bTB category 42

3.3 Interviewed beef farms: ‘bTB driver’ and ‘non-bTB driver’ farms,

by bTB category 44

4.1 Dairy farms income indicators: cash income and farm

family income, 2002/03 to 2006/07 69

4.2 Dairy farms income indicators: net farm income and

management and investment income, 2002/03 to 2006/07 70

4.3 Dairy farms financial indicators: farm output and off-farm

income, 2002/03 to 2006/07 72

4.4 Dairy farms investment indicators: buildings and net asset

purchases, 2002/03 to 2006/07 74

4.5 Dairy farms liability indicator: total loans, 2002/03 to 2006/07 75

4.6 Dairy farms technical indicators: milk production and farmer

and spouse AWUs, 2002/03 to 2006/07 76

4.7 Dairy farms technical indicators: farmed area and cattle

numbers, 2002/03 to 2006/07 77

4.8 Suckler beef farms income indicators: cash income and farm

family income, 2002/03 to 2006/07 81

4.9 Suckler beef farms income indicators: net farm income and

management and investment income, 2002/03 to 2006/07 82

4.10 Suckler beef farms financial indicators: farm output and

off-farm income, 2002/03 to 2006/07 84

4.11 Suckler beef farms investment indicators: buildings and net

asset purchases, 2002/03 to 2006/07 86

4.12 Suckler beef farms liability indicator: total loans, 2002/03 to

2006/07 87

4.13 Suckler beef farms technical indicator: farmer and spouse

AWUs, 2002/03 to 2006/07 88

4.14 Suckler beef farms technical indicators: farmed area and

cattle numbers, 2002/03 to 2006/07 89

5.1 Postal survey responses by category of respondent 119



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5.2 Postal survey respondents: distribution by size and type of

cattle herd 122

5.3 Postal survey farms: movement restrictions by herd type 124

5.4 ‘High GHQ scores’: comparison between farm types, by

category of bTB breakdown 127

List of farm case studies

Page

Case Study A Dairying (economics/future plans) 44

Case Study B Suckler beef and beef rearing (economics/diversification) 58

Case Study C Beef rearing and deer (economics/knock-on

consequences) 59

Case Study D Dairying (economics/business plan) 98

Case Study E Dairying (economics/SPS effects) 101

Case Study F Suckler beef (economics/consequential losses) 102

Case Study G Dairying (economics/pedigree) 105

Case Study H Dairying (health/stress and social impacts) 140



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Glossary of abbreviations

AWU Annual Work Unit BSE Bovine Spongiform Encephalopathy bTB Bovine Tuberculosis CAP Common Agricultural Policy CE Choice Experiment CLA Country Land and Business Association CV Contingent Valuation Defra Department for Food and Rural Affairs FBS Farm Business Survey FFI Family Farm Income FMD Foot and Mouth Disease GIS Geographical Information Systems GHQ-12 General Health Questionnaire (12 question version) GP General Practitioner IR Inconclusive Reactor (to the SICCT test) LUAM Land Use Allocation Model M&II Management and Investment Income NFI Net Farm Income NFU National Farmers’ Union NHS National Health Service ONI Occupiers’ Net Income RA Research Activity SFP Single Farm Payment SICCT test Single Intra-dermal Comparative Cervical Tuberculin test VetNet Cattle TB Data System (the VLA database) VLA Veterinary Laboratories Agency WTP Willingness-to-pay


Longer-term effects of a bTB breakdown: draft report 30 September 2008

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EXECUTIVE SUMMARY

Research background and purpose

E1 Over the past decade the steadily rising incidence of bovine TB (bTB) has

resulted in recognised economic and social impacts on the agricultural industry.

Moreover, during this period the farming industry has gone through a period of

considerable economic and policy pressures for change. Bovine TB is acknowledged

as one of the most difficult animal health problems facing UK farmers and is a major

source of uncertainty, and hence higher costs, in cattle production. The research

programme used a variety of approaches, from empirical research to use of existing

databases, in investigating these issues.

E2 The overall aim of the study was to provide an evidence base on the longer-

term effects of a bTB breakdown on farm businesses, and so to inform policy

development in the future control of bTB. It focussed on two principal areas of

longer-term impacts: those exhibited in adverse social/human health effects, and

those economic effects within the farm business which extend over a period of many

months or years. The specific objectives of the project were:

(1) To make a summary review of the evidence from previous studies of the range

and incidence of longer-term effects of a bTB breakdown.

(2) To identify the main factors associated with a bTB breakdown which are

likely to result in long-term effects (for both farm business and people).

(3) To estimate the longer-term economic effects of a bTB breakdown in the

context of a range of ‘farm system and bTB breakdown’ scenarios.

(4) To provide a sound evidence base for a better understanding of the social and

human health effects of a bTB breakdown at farm-level.

(5) To explore cattle farmers' willingness to pay for a vaccine to avoid a bTB

breakdown, using a choice experiment approach.



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Evidence from previous studies

E3 The main purpose of the literature review was to identify existing evidence on

the range and scope of potential longer-term effects of a bTB breakdown,

encompassing both economic/business related issues and also human health effects.

Overall, trends in cattle production, especially dairy farming, are not explained by

reference to conventional economic variables alone. Neither is it easy to identify and

quantify other factors affecting cattle farmers’ decisions, including their emotional

reactions to outbreaks of animal disease, that to date have mostly been excluded from

consideration.

E4 The stress effects of BSE and foot and mouth disease epidemics on farmers

and their families have been investigated, but bTB is largely neglected. Conceivably,

farmers’ perceptions of the consequences of animal disease are inextricably linked

with their attitudes towards authorities, and particularly government, who are seen

more as a source of problems than their solution. Conspicuous by its absence is any

substantive evidence in the literature of the financial implications of investment in

bio-security measures aimed at keeping cattle from contact with badgers. There is no

evidence of any significant long-term adjustments in farm resource use to improve

bio-security on cattle farms.

Factors which result in longer-term impacts

E5 In an analysis that focussed on the areas most severely affected by bTB, the

South West and West Midlands regions of England, and Wales, the GIS study of

regional trends in cattle farming identified some significant differences between bTB

parishes (parishes with a bTB incident or incidents) and other parishes. There was a

broad consistency in the findings across regions, with an established and consistent

trend away from dairying towards beef cattle over the decade to 2004.

E6 High bTB incidence parishes have more beef female cattle than other parishes.

There is strong evidence that it is the high bTB incidence which is itself driving the

rate of change. The rate of decrease in the number of cattle farms was greater in the

high bTB incidence parishes. In Wales high numbers of dairy cattle in a parish may

be associated with the high bTB incidence. A high bTB incidence appears to be a



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significant influence on the trends over time (in cattle numbers, in the numbers of

cattle farms, in the relative balance between dairy and beef cattle).

E7 Following the identification of farms on which strategic decisions in farm

planning have been influenced by bTB, it was found that farmers on ‘bTB driver’

farms are consistently less optimistic about the future of farming, less likely to see

their own futures, and those of their family, in farming, and more negative in their

attitudes to their local and farming communities. They also differ from other farmers

on a wide range of farming values and objectives, are much more likely to have made

reductive adjustments to their businesses in recent years, and are much less likely to

expect a successor to take over the family farm in due course. Distinguishing

between cause and effect in these characteristics would require further specific

research.

E8 On dairy farms, the evidence is that the loss of a large number of animals is

most likely to be linked with an impact on farm decision-making. This is closely

followed in importance by spending a long period under movement restrictions. Even

on some lightly affected farms, bTB may be an influence of decision-making.

Lightly affected beef farms, in contrast, are rather more likely to take bTB into

account in their decision-making. As with dairy farms, ‘large number of cattle taken’

is ranked above ‘long period of restrictions’ in terms of decision-making impacts,

although both proportions are lower than for dairy farms.

E9 One important way of tackling business risk is the use of insurance to mitigate

the effects of a specific event, and bTB insurance has long been available to farmers.

Its availability, however, appears to be more limited than once was the case. It is

evident that the spread of bTB has already caused revisions both to the cost of bTB

insurance and the level of cover offered by insurers, an adverse economic effect as far

as farm businesses are concerned. While the firms consulted remain committed to

providing the best terms to their clients, and being as fair as possible in the

circumstances, the market reality is of rising claims. Further adjustments to

premiums and cover, driven by both claims experience and statistics on the incidence

of bTB, cannot be ruled out for the future.



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E10 There is evidence that bTB is associated with raised stress levels for farming

people - the farmer, the spouse, other family members and farm workers; these issues

are covered under ‘understanding health and social effects’ below.. In terms of the

factors most closely associated with a bTB breakdown having longer term effects on

farm businesses, the principal findings of this research are summarised in table below.

Two distinct areas of impact are distinguished: those within the area of farm business

economics, as identified by the effects on strategic decision-making; and those

directly affecting the people involved on the farm, in terms of human mental health

(psychiatric morbidity).

Factors which result in longer term impacts from a bTB breakdown: a summary of the research findings

Farm type

Dairy farms Beef farms

Area and

nature of

impact ‘Lightly

affected’

‘Long

period

under

restriction’

‘Large

number

of cattle

taken’

‘Lightly

affected’

‘Long

period

under

restriction’

‘Large

number of

cattle

taken’

Farm business

economics

(strategic

decision-making)

+ ++ +++ + ++ +++

Human mental

health

(psychiatric

morbidity)

++ +++ ++++ + +++ ++

Note: Plus signs (+) indicate a positive effect as a factor associated with longer term impacts,

the more (+) the stronger the association.

E11 Although completely different samples of farmers were used, there is a very

good fit between the two estimates. The two sets of estimate are not mutually

incompatible, of course, and it’s likely that if sample sizes had been large enough to

distinguish between beef suckler herds and beef trading units further finessing of

these findings would have resulted.

Estimated longer-term economic effects

E12 At Great Britain level, most cattle farms (more than 60%) either have never

had a bTB breakdown or have been clear for many years; and two thirds of those

which have had a breakdown have been only lightly affected. The proportion of all



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bTB breakdowns resulting in more serious farm-level effects is small but not

insignificant: more than a quarter lose significant numbers of cattle and a further 4%

of cases are under livestock movement restrictions for a long period. The proportions

of farms with bTB breakdowns, both light and heavier, are substantially greater in the

‘bTB endemic’ areas. Most bTB breakdowns would not be expected to result in

longer-term effects on the farm business - identifiable longer-term economic effects

are likely to be seen on perhaps 10 – 15% of all affected farms.

E6 The stakeholder consultation identified reduced farm profitability, higher

costs, adverse impacts on calving patterns and inhibited business development. More

widely, loss of confidence and adverse effects on the environment were cited.

E13 Key findings from the FBS study of dairy farms are that differences in cash

flow and FFI between bTB farms and the control group increased over time, with

bTB-affected farms falling behind the others. In terms of NFI, however, the two bTB

groups had a broadly similar income position to the control group but for M&II the

trends are more volatile. Overall, in most years and for most income indicators, bTB

farms performed less well than the control group, although the differences are not

large in most years. The data suggest bTB farms generally have a higher level of off-

farm income.

E14 Starting from a similar level, average milk yield on bTB farms fell behind the

steady increase in yields seen in the control group, with a 6-8% gap by 2006/07. The

evidence is that farmers (and their spouses) work consistently longer hours on bTB

farms, supporting other findings that a bTB breakdown can cause considerable

additional work over a long period. Total cattle numbers increased markedly on ‘long

period of restriction’ bTB farms towards the end of the period; and these farms also

saw an increase in their farmed area.

E15 Key findings from the FBS study of suckler beef farms are that the control

group (all suckler beef farms) experienced a steady decline in profitability over the

five years from 2002/03. Farm incomes on bTB farms are more variable than for the

control sample; and incomes on ‘long period of restriction’ farms are lower than for

‘all bTB farms’. Incomes data are variable: for cash incomes a significantly lower

level of economic performance on the bTB-affected farms, but there is no consistent

pattern for NFI or M&II.



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E16 Overall, farms with a bTB breakdown performed less well in most years,

those under movement restrictions being least profitable. The bTB farms exhibit

much greater annual variability in incomes. In most years investment in buildings

and net asset purchases was substantially lower on bTB farms than on the control

group of farms. The labour input of ‘farmer plus spouse’ was generally lower on bTB

farms than on others, throughout the five year period studied. With a smaller average

farm size, bTB-affected farms have a significantly higher stocking rate, consistent

with a general increase in cattle numbers while under movement restrictions.

E17 The farm interview survey involved 152 farms from across the bTB-endemic

areas of England and Wales. Its main findings are that strategic business decisions by

farmers typically take a range of factors into account, of which bTB may be one. Just

over a third of the surveyed farms listed bTB as a driver of a specific business

change, and in these cases it was dominantly listed as either the first (80%) or second

(16%) driver. Similarly, in terms of business threats, bTB was seen very much as one

threat among a number: it was the third most commonly listed main threat and was

ranked much the same for second and third level threats.

E18 Just over half of the sample farms had made improvements to bio-security

during the previous ten years; some of these had required very substantial investment,

particularly for fencing and physical improvements to feed storage and feeding areas.

Funds received as compensation for bTB losses, from government or insurer, were

almost all used within the farm business; in addition to replacing stock, a common

use was to reduce external borrowings.

E19 Movement restrictions give rise to significant adverse effects, but these vary

greatly. Being unable to sell cattle when planned generates many of the specific

impacts, especially extra feeding costs; also major disruption of the farming system is

common. Adverse financial impacts are widespread, especially higher interest charges. The

loss of breeding stock is identified as a particularly serious consequence of a bTB

breakdown; and if replacements are purchased these often result in other problems:

adverse effects on calving patterns, the introduction of ‘new’ diseases, a decrease in

herd equanimity, a general fall in yields or milk quality (dairy herds).



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E20 Many farmers expected future consequences from the recorded bTB

breakdown(s). These included needing to replace breeding stock and a slowdown in

business growth and development, sometimes far beyond the initial breakdown.

Understanding health and social effects

E21 The research project aimed to provide a sound evidence base for a better

understanding of the social and human health effects of a bTB breakdown at farm

level. The postal survey involved 468 farms from across the bTB-endemic areas of

England and Wales, and its principal findings with respect to the implications of a

bTB breakdown for human health augment previous research on both (a) the short-

term effects of a bTB breakdown and (b) other studies of the effects of livestock

crises on the farming community.

E22 While a bTB breakdown is less dramatic than some other livestock disease

crises (such as FMD and BSE) by its nature the persistence and pervasiveness of the

control programme represents a clear source of on-farm stress, typically over an

extended period.

E23 The stakeholder consultation reflected perceptions of a range of human health

effects as a consequence of bTB, including physical, mental, emotional and social

changes. Many highlighted ‘uncertainty’ and ‘lack of control’ as powerful drivers of

human health problems.

E24 The GHQ-12 study provides objective evidence of the scale of the longer-term

effects of a bTB breakdown. In general terms, a much greater proportion of farmers

exhibit signs of psychiatric morbidity that the population as a whole. Overall, the

highest stress levels are seen on farms which have been under livestock movement

restrictions for a long period.

E25 However, on dairy farms, where people are generally more stressed than on

beef farms, the greatest stressor is the loss of a large number of cattle. There is

evidence that bTB is associated with raised stress levels for people other than the

farmer, particularly spouses; but the sample was too small for reliable results for farm

workers. For farmers, a bTB breakdown causes significant stress: this is highest for

dairy farmers, especially where they have lost a large number of cattle; while for both



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trading beef and suckler beef farmers, a long period under restriction is the greater

stressor.

E26 The study of rural GPs identified that farmers typically present late with

symptoms and, for mental health issues in particular, GPs probably only see a small

proportion of cases. Several GPs pointed to the range of pressures affecting farmers,

particularly livestock farmers, of which bTB is only one; and the consequent

difficulty in direct attribution of cause and effect.

E27 The lack of control experienced as a result of a bTB breakdown was identified

as an important stressor; even so, bTB was thought to be a less serious problem than

the FMD epidemic of 2001. A range of moderating factors include age, farming

experience, farm ownership, diversification, the availability of support and the

significance of other crises.

E28 At interview, most farmers identified a range of adverse effects on their daily

lives as a result of a bTB breakdown, and often on those of their families and others.

Key effects relate to uncertainty, stress, time pressures and financial worries. Knock-

on effects on the local and farming communities do not appear in general to be very

significant.

Exploring farmers’ WTP for a bTB vaccine

E29 The results of the choice experiment survey show that cattle farmers have a

substantial WTP for a bTB cattle vaccine. Farmers primarily value the ability of a

vaccine to prevent a breakdown (or rather to reduce the probability of a breakdown).

However, they also recognise that a vaccine is unlikely to be 100% efficacious and so

they also value insurance backing that would pay compensation to farmers should

vaccinated cattle test bTB positive.

E30 Farmers appear to value the ability of a vaccine to reduce the severity of a

breakdown somewhat less, and their prime concern is to prevent a bTB breakdown in

the first place. This is not surprising, given that some of the most intrusive

consequences of bTB, such as movement restrictions, re-testing etc. are incurred

regardless of the severity (i.e. number of reactors) of a breakdown.



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E31 Both the CE and CV instruments used were carefully developed and tested

prior to their application. The WTP estimated by the CE method seemed reasonable

and was backed up by appropriate reasoning given by respondents. The CV estimate

was of broadly the same order of magnitude as the CE estimate, despite the two

valuation methods being fundamentally different and the payment vehicle used also

being different, and with a different vaccine scenario presented to respondents. This

provides reassurance on the robustness of the WTP estimates.

E32 Many cattle farmers in the survey felt at high risk of their cattle having a bTB

breakdown. Indeed, their (subjective) perception of risk is likely to be somewhat

higher than the actual (objective) risk that they face. This perception of risk was

found to be positively correlated with their WTP. Despite this, farmers often did not

adopt all possible bio-security measures against bTB, although it is likely that not all

are equally relevant on every farm, or for every farming system. Many farmers felt

that there was little they could do to prevent their herds getting bTB.

E33 Overall, cattle farmers did not think that development of a cattle vaccine was,

by itself, the answer to the bTB problem but that a combined strategy was necessary

with a number of elements, including wildlife control.

Discussion and conclusions

E34 This research has focussed on the longer-term effects of a bTB breakdown and

has considerably extended the evidence base. There are two aspects of the research as

a whole that deserve final mention here: the identification of the key factors which are

associated with longer-term effects, and the exploration of farmers; WTP for a bTB

vaccine. The key factors were identified through studies of impact (a) within the

farm business and (b) on the people associated with the farm, while the WTP study

was essentially an independent RA.

E35 The key factors were identified through studies of impact (a) within the farm

business and (b) on the people associated with the farm. Although the two estimates

were derived independently, there is a good fit between them. Overall, the factor

most likely to result in longer-term impacts is the loss of a large number of cattle,

closely followed by the farm being under movement restrictions for a long period.



10

However, for beef farms as a whole the most stressful factor was being under

movement restrictions for a long period.

E36 The work undertaken by Reading University shows that cattle farmers have a

substantial WTP for a bTB cattle vaccine. Moreover, it finds that farmers primarily

value the ability of a vaccine to prevent a breakdown (or rather to reduce the

probability of a breakdown). However, farmers also recognise that a vaccine is

unlikely to be 100% efficacious and so they also value insurance backing that would

pay compensation to farmers should vaccinated cattle test bTB positive. Farmers

appear to value the ability of a vaccine to reduce the severity of a breakdown

somewhat less, and their prime concern is to prevent a bTB breakdown in the first

place. The broad comparability of the two WTP estimates, from the CE and CV

approaches respectively, provides some reassurance of their robustness.

E37 The issue of risk perceptions deserves further study, since farmers’

(subjective) perceptions of risk appear to be higher than the actual (objective) risk

that they face. Not only is this perception of risk positively correlated with their WTP

for a bTB vaccine, it seems possible that at least some of the observed structural

adjustment in cattle farming is being driven by inaccurate perceptions of risk by some

farmers. Importantly, cattle farmers did not think that the development of a cattle

vaccine was, by itself, the answer to the bTB problem; rather, its role was as part of a

combined strategy with a number of elements, including wildlife control.



11

1 INTRODUCTION

Background

Over the last decade or so the rising incidence of bovine TB (bTB) has resulted in

recognised economic and social impacts on the agricultural industry. The economic

impacts result from the nature of the disease, the government’s control measures

(restrictions on movement of cattle on and off the farm, repeat testing and compulsory

cleaning) and the impact of test ‘failures’ on the normal marketing of livestock and

product. The human impacts (on farmers, their families and farm staff) arise from the

loss of animals, significant changes to normal routines, financial worries and the

perceived increase in risk and uncertainty.

Moreover, the worsening of the disease situation - and hence the increasing economic

and social impacts - of bTB has coincided with a period of considerable economic

and policy pressures for the agricultural industry. Principal among these are (a) the

recent major changes in the CAP, (b) established long-term changes in the food chain

(many of which are detrimental to primary producers), (c) new requirements for

increasingly environmentally-friendly farming systems and (d) increased competitive

pressures from the enlargement of the EU and greater exposure to global markets.

These pressures, which arise directly or indirectly from significant long-term

adjustments in the food chain and in society’s expectations of farmers, have been

well-documented (Curry Commission, 2002) as have the appropriate policy responses

(Defra, 2002a).

Bovine TB is acknowledged as one of the most difficult animal health problems

facing UK farmers (ISG, 2007) and is a major source of uncertainty, and hence higher

costs, in cattle production. Uncertainty always increases costs as it forces decision-

makers to change how they allocate and manage existing resources, and to

supplement them with new or additional resources. Moreover, although farmers are

compensated for animals culled, statutorily government compensation cannot reflect



12

broader, long-term losses. Several studies have pointed to longer-term economic

impacts which have to be borne by the farm businesses affected (NAO Wales, 2003;

Sheppard and Turner, 2005; Bennett, 2004). These studies also identified important

social impacts on the farm family and farm staff, with personal pressures arising from

a number of sources including emotional responses to the loss of animals, concerns

about the welfare implications of retaining stock on the farm, greater business

uncertainty, the ‘hassle factor’ and frustration in the apparent lack of progress in

controlling the disease. A further factor of particular relevance here concerns the

changes recently made to the government’s compensation policy and the introduction

of pre-movement testing for cattle (Defra, 2006).

The study brief

The overall aim of the study was to provide an evidence base on the longer-term

effects of a bTB breakdown on farm businesses, and so to inform policy development

in the future control of bTB. As such, it was concerned with two principal areas of

longer-term impacts: those exhibited in adverse social/human health effects, and

those economic effects within the farm business which extend over a period of many

months or years, or even result in permanent changes (e.g. to farming system). The

research was pursued through a wide range of research activities. The research was

designed to provide additional knowledge of the farm-level implications of bTB

breakdowns through focussing on identifying those longer term effects which have

largely been ignored in previous studies. The specific objectives of the project were

agreed as follows:

1. To make a summary review of the evidence from previous studies of the range

and incidence of longer-term effects of a bTB breakdown, encompassing both

socio-economic and human medical studies to provide a clear analysis of the

major long-term problems so far identified.

2. Through primary research, to identify and, if possible, to rank the main factors

associated with a bTB breakdown which are likely to result in long-term

effects, on both the farm business and the people involved.

3. To provide broad estimates of the longer-term economic effects of a bTB

breakdown in the context of a range of ‘farm system and bTB breakdown’

scenarios.



13

4. To provide a sound evidence base for a better understanding of the social and

human health effects of a bTB breakdown at farm-level, including both the

range and typical incidence of such adverse impacts.

5. Through the use of a choice experiment approach, to explore cattle farmers'

willingness to pay for a vaccine to avoid a bTB breakdown, under a number of

vaccine characteristics/scenarios.

The study method

Approaches and research plan

Our research plan was structured around the overall objectives above, and involving a

series of eight distinct ‘Research Activities’ (RAs). While the research plan does not

map research objective : research activity neatly, most of the RAs provide the

primary source of information for at least one objective and contribute to the

achievement of others (Figure 1.1). The study was supra-national rather than regional

in scope, covering the bTB-affected areas in both England and Wales, but with a

particular emphasis on the bTB-endemic areas1.

The nature of the study was such that identifying good quantitative information was

far from straightforward, and this was known from the outset. This is not only

because of the nature of the study topic (longer term economic effects may be so

significant as to effectively remove a potential respondent from the sampling frame,

for example) but also because the impacts may resist neat classification and preclude

the estimation of average or typical impacts. Not only do farm businesses often have

quite distinctive characteristics (and the study looked at a wide range of possible bTB

impacts within this population) but also, as expected, the human health impacts were

at least equally varied. Further, by definition, longer-term impacts emerge over a

period of time which may not in all instances sit easily within the framework and

timespan of a single research study.

Hence, our research approach relied on a number of distinct RAs to build as

comprehensive a set of information as possible, from as wide a range of sources as

possible. Moreover, it was structured to provide a foundation for possible future

research exploring the longer term impacts of bTB (e.g. through the use of a standard

1 Cornwall, Devon, Dorset, Somerset, Wiltshire, Gloucestershire, Worcestershire, Herefordshire,

Shropshire, Cheshire, Staffordshire, Derbyshire, Lancashire and Wales



14

Figure 1.1 Relationship between research activities (RA) and project objectives

Objective 1

Review

evidence on

longer-term

impacts from

previous

studies

Objective 2

Identify main

factors

associated with

a bTB

breakdown

likely to result

in long-term

effects

Objective 3

Provide broad

estimates of the

longer-term

economic

effects under

alternative

scenarios

Objective 4

Provide an

evidence base

regarding

human health

consequences

of a bTB

breakdown

Objective 5

Conduct a

choice

experiment to

explore cattle

farmers' wtp

for a vaccine to

avoid a bTB

breakdown

RA1:

Literature review a (a)

RA2:

Stakeholder consultation (a) (a) (a) (a)

RA3:

Farmers’ interview

survey

(a) a a (a)

RA4:

Desk study using FBS

data

(a) (a) a (a)

RA5:

Farmers’ postal survey (a) (a) a

RA6:

GP Case Studies (a) a

RA7:

GIS study of cattle

population

(a) (a) (a)

RA8:

Choice experiment study a

a Primary information source (a) Secondary information source

questionnaire). We carried out a comprehensive review of all existing data sources

that could be identified as having potentially useful information on the topic; while at

the same time conducting focussed empirical studies to augment existing information.

This approach to research is less academically satisfying than what might be termed

‘pure’ research, in that it lacks a clear conceptual coherence. Nevertheless, it is

difficult to identify alternative ways in which this particular topic could have been

pursued, and the study is best regarded as investigative research as, indeed, its title

suggests2.

2 Defra’s research call SE3120 was titled ‘Investigate the longer term impacts on farm businesses of a

bTB breakdown’.



15

One of the problems was limitations with the usefulness of most if not all of the

existing data sources. For example, we were aware that there are limitations with the

VetNet database as a research tool, but it was an invaluable resource nevertheless

because of the information on the timing and scale of bTB breakdowns. Similarly,

we were also aware that the scope of the Agricultural Census database, though

providing the possibility of a unique insight on spatial aspects of farm systems

development, imposed some limitations on what could be achieved; while the Farm

Business Survey (FBS) database, though also invaluable for this research, had its

limitations particularly in respect of completing a more comprehensive longitudinal

study.

Some aspects of the research programme were innovative: for the FBS-based desk

study, for example, the identification of a study sample, and the linking of data drawn

from the VetNet database with the FBS database, required close liaison with Defra’s

Farm Economics Unit, the UK Data Archive and Rural Business Research (the FBS

Consortium), to ensure the maintenance of confidentiality protocols. Similarly, the

longitudinal study of Agricultural Census data using GIS techniques, undertaken by

the ADAS team, required the development of new approaches. As part of our work

we believed it was important to ‘ground-truth’ the findings of the mental health

survey (RA5) and so we carried out a series of case studies based on GP practices

situated in, or close to, known bTB ‘hotspots’. The aim was to gather evidence on the

wider impacts of bTB breakdowns (on farm and rural dwellers’ social and mental

well-being) so as to provide an independent source against which the findings of RA5

can be assessed.

Research activities

In total eight separate RAs ranging from desk studies to specific empirical research

were carried out, the research extending over the period April 2007 through

September 2008. The RAs, with a brief outline of their focus in each case, are set out

below.

RA1 Literature review

The literature review focussed on two aspects of a bTB breakdown, the business-

related economic effects and the people-related social and medical effects; it had the

following aims:



16

(a) To produce a summary of range of the potential longer-term economic and

social/human health effects of bTB breakdowns, at least to the extent that these

have been identified by earlier work, in order to provide a context for the

findings of the present study.

(b) To review the existing literature on human stress in rural areas, specifically to

identify current evidence on the possible links, if any, between bTb breakdowns

and human stress and distress.

(c) To update Defra’s recent review with the findings of any other research

published before the start of the present study in 2007.

RA2 Stakeholder consultation

An extensive stakeholder consultation was conducted during the summer of 2007 in

order to identify, if possible, the full range of longer-term impacts arising from bTB

breakdowns and so inform later stages of the research. There were two components

to this research activity:

• a broad request to a wide range of bodies and institutions inviting written

submissions on the incidence, nature and impact of the longer-term adverse

impacts from bTB breakdowns, at the level of the affected farm business; and

• a limited number of personal interviews with key stakeholders which will

follow up on the written submissions and aim to collect evidence, probe claims

in more detail and inform later stages of the research.

A total of 40 stakeholders (71%) responded (of 56 initially mailed) and a full

summary of the findings is given in Appendix A.

RA3 Farmers’ interview survey

The types of longer-term impact identified by the first two research activities

informed the design of the farmers’ interview survey. In particular, considerable

attention was given to avoiding any (inadvertent) bias in the responses which, it might

be expected, could result in an over-emphasis on the economic significance of bTB as

a driver of business change. All farms sampled had experienced at least one bTB

herd breakdown. To minimise the bias inherent in asking farmers directly about the

longer term impacts of bTB on the farm business, the questionnaire was structured so

as to elicit information about changes that already had taken place, or were in train,

without making an explicit link between the changes and bTB. Instead, the role of



17

bTB in relation to strategic business planning on the farm was identified in the

context of a structured discussion about change, rather than in response to questions

about bTB as such. After the discussion had been completed, there was an

opportunity for verification. It is a testament to the design of the questionnaire and

the skill of the interviewers that very few ex post adjustments were made.

The VetNet database was used to select a stratified sample of farms in England (in the

main bTB-endemic areas) and Wales which have experienced a bTB breakdown, and

which reflect key characteristics of the target population. The sample structure was

based on a preliminary analysis of the VetNet data (see Table 4.1) and included a

range of enterprise types (dairy, beef sucklers, beef rearing) and a range of bTB

breakdown scenarios (single small, multiple small, single large, multiple large,

successive breakdowns, etc.) from the principal bTB areas of the West Country, the

Marches and Wales. A total of 152 farms completed the survey and the main results

are given in Appendix B.

RA4 Desk study using FBS information

A desk study primarily using data drawn from the annual FBS was undertaken, as

part of the research into the longer-term economic impacts of a bTB breakdown on

far, businesses. The principal problem in using the FBS database is it has inadequate

information regarding the incidence of bTB breakdowns, particularly prior to

2005/06; prior to 2002/03 there is no way of identifying farms which have had a bTB

breakdown. Having identified a target sample, Rural Business Research contacted

farmers to gain their written agreement to linking their FBS information with specific

data on the timing and scale of the breakdown from the VetNet database. The study

focussed on tracking the business development of several cohorts of ‘bTB

breakdown’ farms over about the five years since 2002/03, and comparing these with

a ‘non-bTB breakdown’ cohort of otherwise similar farms for the period. Key results

are given in Appendix C.

RA5 Farmer’ postal survey

This RA focussed on the impacts of a bTB breakdown on the social well-being and

mental health of the farm family and farm staff and used the established GHQ-12

questionnaire. It was administered through a postal survey to a sample of farmers

drawn from the VetNet database, which included the three categories of ‘bTB



18

breakdown’ farms identified for the farmers’ interview survey (‘lightly affected’,

large numbers of cattle lost’ and ‘long period of movement restrictions’) and a control

group (no bTB breakdown). The only way of approaching people other than the

farmer was through the farmer, and we enclosed three copies of the questionnaire and

separate reply-paid envelopes (for completion by the farmer, and two of the

following: their spouse, adult children and staff) and invited requests for more forms

as necessary. The study was conducted during March and April 2008 and responses

were received from 468 farms, 52% of the effective sample. The use of this

established questionnaire meant that the results could be compared with studies of the

general morbidity of the adult population, for which the questionnaire is regularly

used, and also with other studies of farmers which have used the same questionnaire.

The questionnaire is given in Appendix D, and the results are presented in Chapter 5.

RA6 GP case studies

Information from the GHQ-12 questionnaire (RA5) was augmented with a limited

broader review using six case studies of rural GP practices located in, or adjacent to,

established bTB ‘hotspots’. The primary aim of this RA was to ground-truth some of

the key findings and to identify possible wider, or more diffuse, effects at community

level. We used a semi-structured interview questionnaire which probe GPs’

experience of the implications of a bTB breakdown on the health of their patients,

including social well-being, physical and mental health, both in the immediate

aftermath of a bTB incident and into the longer term. This RA also explored the

nature of the support that is provided and considered any gaps in provision. This RA

was carried out in the autumn of 2007 and the findings are given in Chapter 5.

RA7 GIS study of cattle populations

This RA examined information from the Agricultural Census for evidence on the

nature and extent of the impact of bTB on cattle populations. One possible long term

cost of bTB and its control is that cattle farmers give up cattle production or switch to

less profitable cattle systems (for example, moving from dairying to beef). The

objective of this work was to examine the data on cattle populations and farm

holdings to see whether there is any connection between bTB ‘hotspots’ and relative

divergence in trend reductions in cattle populations over the longer term. The parish

testing interval was used as an indicator of bTB risk and this information was



19

integrated with data from the Agricultural Census database, to determine the

existence and strength of any correlations, within both regions and sub-regions,

between changes in cattle populations and the testing interval. Where data allowed,

an investigation into the number of cattle holdings was also carried out, determining

trends in farm types within affected areas. The full results are given in Appendix E.

RA8 Choice experiment

The final RA was based on a choice experiment questionnaire, designed with

appropriate choice sets to elicit cattle farmers' willingness to pay (WTP) to avoid a

bTB breakdown, with a bTb vaccine being the 'payment vehicle'. The choice

experiment was used to estimate the WTP for a vaccine depending upon a number of

vaccine characteristics including, for example, the efficacy of the vaccine, whether

subsequent breakdown losses (i.e. even with vaccination) are covered by an insurance

programme, etc. The choice experiment was administered to a sample of 287 cattle

farmers, with the sample split according to those that have suffered a bTB breakdown

and those that have not but are situated within bTB 'hotspot' areas. The questionnaire

used is given in Appendix F and the study methods and findings are the subject of

Chapter 6.

The research team

The research programme was undertaken by an inter-disciplinary team which

included, inter alia, expertise in animal health and welfare policy, economics, market

research, farm business economics, organisational behaviour, rural stress and mental

health. The research was led by Martin Turner, University of Exeter and Mark

Temple, ADAS UK Ltd. The study was designed by a small team comprising Martin

Turner, Dr Keith Howe and Dr Emma Jeanes (University of Exeter), Mark Temple

and David Boothby (ADAS UK Ltd) and Paul Watts (Somerset Partnership NHS and

Social Care Trust). Professor Richard Bennett and colleagues at the University of

Reading were responsible for RA8. Full details of the wide range of people who

worked on this research, or contribute in other ways, are given in the

Acknowledgements (page ii).



20

2 REVIEW OF THE EVIDENCE FROM OTHER RESEARCH

Defining the longer-term

A literature review to find out what is already known about the longer-term effects of

bTB on farm businesses needs ‘longer-term’ to be defined precisely. One approach is

to adopt an essentially arbitrary and pragmatic definition, such as any effects

discernible for at least one or more years after a herd breakdown. This is implicitly

the perspective taken by Sheppard and Turner (2005). Economic principles provide a

more rigorous foundation. In economic terms, the long term is strictly the period over

which all resources are considered variable. Thus a decision to start up, or quit, cattle

production is a long-term consideration. When changes are less extreme, as is more

usual, and some farm resources are unaffected while others are changed in kind or

quantity, those may be regarded as medium term. So, in the context of the present

study, longer term is defined as both long and medium term impacts of bTB

breakdown on farm resource use. It follows that a decision to quit milk or beef

production as a result of bTB, or investment in bio-security to protect cattle from

contact with badgers, or a change from buying-in herd replacements to home rearing,

are all examples of the type of longer-term adjustments with which the study is

concerned.

Yet there is a potential confounding factor that must be taken into account in the

interpretation of cause and effect. It is trends in cattle production completely

unrelated to bTB. For everyday commercial reasons, farmers constantly readjust their

resource use in response to changes in output and input prices, technology, and their

plans and expectations about the future. Changing relative prices affect farm profits.

So does technological change, by enhancing productivity and widening the

production choices available to farmers. How a farm business is organised also

reflects a farm family’s needs and aspirations, including how the business is prepared

for handing over to a successor. Thus the literature review begins with a look at



21

general trends in UK cattle farming. On that foundation, any contribution of bTB to

structural change can be assessed.

Trends in cattle farming

Bovine TB is mainly seen as a problem for milk producers, though also for beef

producers not least because of the complementary nature of milk and beef production.

There are very few recent publications about the economics of UK beef production,

and these pay little or no attention to the impacts of cattle disease (e.g. Williams et al.,

2005, Fogerty and Robbins, 2007). Substantive research into structural change in

milk production for recent years is mainly from Manchester University, while from

Nottingham University since 2005-06 the analysis of Farm Business Survey data

provides a detailed picture of the most recent changes in financial aspects.

Colman et al. (2002) estimated long-run cost functions for a representative sample of

UK dairy producers allowing future re-structuring of the industry to be simulated.

The model incorporates producers’ differential costs and milk prices under different

policy scenarios. Their general conclusion was that existing trends towards

increasing average herd size and fewer producers would continue. The authors noted

that of the 50,625 active UK milk producers in 1984, when milk quotas were

introduced, almost half had ceased production by 2001, an average decline of 1,375

producers each year. The rate of exit varied over the period, increasing between 1996

and 2001 when almost 1,700 farmers quit each year. The most recent published data

show that in June 2007 there remained 17,915 UK milk producers, a reduction of

almost 65% since 1984 (NDC, 1997; Dairy Council, 2001; DairyCo, 2008).

Yet despite evidence of such a radical decline in UK producer numbers, a

combination of increasing average herd size, smaller national herd, and sustained

increases in annual milk yield per cow has kept national output relatively stable

(except for year-to-year fluctuations) ever since the 1980s. However, a declining

trend in aggregate milk delivered to dairies began in 2004 (DairyCo, 2008, page 8).

Clearly, farm gate prices and the profits from milk production are key factors

underlying such changes. Detailed analyses for England are provided by Robertson

and Wilson (2007, 2008).



22

Survey data has revealed an optimistic outlook by many continuing milk producers.

Reporting on the declared intentions of a representative sample of 369 specialised

milk producers in England and Wales at the end of 2002/03, Colman et al. (2004,

page 61) note a more positive outlook than might have been expected in the light of

recent very poor returns from milk production and the rapid rate of exodus observed

in previous years. Some 86% of producers declared an intention to continue. This

finding was closely comparable with the results from a 1996/97 survey which

identified 87% of producers planning to expand or continue production, with only 4%

planning to quit and 3% to cut production (Farrar and Franks, 1998, Table 9.1). By

May-July 2007 only 219 of the original sample of 369 milk producers still provided

usable records for Colman (2008) to investigate further patterns of structural change

in UK milk production. Of these, yet again 85% expected to be in production in five

years’ time.

An intriguing aspect of these findings is that, in practice, many farmers have behaved

contrary to their stated intentions. Having excluded six of the original 369 farms

because no more recent information could be obtained, Colman and Zhuang (2005)

found that over 45 of the 363 remaining ceased dairy farming between April 2003 and

April 2005. As expected, the majority of these had a herd size of fewer than 70 cows

in 2002/03, and the lowest average margins. Less explicable were those 13 farms that

quit with herds ranging from over 100 to over 150 cows. Another 17% of continuing

milk producers either stated an intention to quit over the next five years or regarded

their futures as uncertain.

These outcomes are perplexing precisely because so many milk producers actually

did the opposite of what they said they intended to do. Also, it raised questions in the

authors’ minds about the reliability of their predictions about the future structure of

dairy farming in England and Wales based on the estimated long-run cost functions.

Predictions for 2007/08 and 2014/15 are given in Colman et al. (2005), where the

authors note that “ there are reasons for thinking that the model is persistently

retaining perhaps 2000 more herds than it should ...” (page 18). In particular “no

allowance can be made in the model for the personal factors affecting dairy farmers’

decisions about whether to stay in production, or of a whole range of unforeseeable

factors” (page 19).



23

Those farmers reported by Colman and Zhuang (2005) as having intended to continue

dairy farming but had quit by early 2005 were asked to give their main reasons for

doing so (page 18). Change in milk profitability, personal or family reasons, and

CAP policy changes as well as ‘other’ were categories cited. None of the questions

explicitly invited information about animal health issues, nor seems to have been

mentioned in reply. The fact that enterprises replacing dairy cows typically did not

involve giving up cattle production entirely may be taken as an indication that disease

was not considered important. This view is reinforced by evidence that the majority

of farms either expanded an existing beef enterprise or started a new one, while others

mostly expanded or established a new sheep enterprise. Substituting cattle by arable

production occurred in a negligible number of cases (Table 15, page 19).

In their study of the future of UK dairy farming, Colman and Harvey (2004) asked the

question, ‘How rational are farmers?’ Based on the same sample data to which

reference has been made, they concluded that the conventional economic assumption

of profit maximisation by farmers is often unrealistic. Importantly, that in itself

compromises “the ability of their businesses to withstand commercial and business

pressures. As and when these pressures become sufficiently strong, they will be

obliged to give up production.”(page 18).

Drawing together the various strands of evidence presented from the above limited

but authoritative sources, the following can be asserted. First, an ongoing decline in

the number of UK milk producers continues at a rate that exceeds predictions from

estimated economic models, and the intentions of milk producers revealed in

representative farm surveys. Second, quitting milk production mainly leads to an

expansion of other animal production, most frequently beef cattle, and less often

sheep production. Third, based on the same raw data it appears that many milk

producers do not behave rationally from an economic point of view. Fourth, either

these latter conclusions are incompatible, or some other exogenous factor (or factors)

is being overlooked. On the one hand, dairy farmers keep producing, maybe sub-

optimally, while aiming to continue or even expand, but then instead quit milk in

unexpected numbers for other animal production.

Such behaviour is not necessarily irrational. It might be evidence that dairy farmers

reach a critical point that triggers a shift out of milk production. But the cause might



24

not show up in conventional economic assumptions and the analysis of relationships

between prices, costs, technology and profits. Its origins might be non-economic,

rooted in other factors that influence how farmers behave. In the light of farmers’

experience of major disease epidemics in recent years, especially foot and mouth

disease (FMD), bovine spongiform encephalopathy (BSE), and also bTB, any

unrecorded factors merit investigation, including the stress arising from major disease

outbreaks.

Effects of stress on farmers

A striking feature of the literature on factors other than normal economic influences

affecting farmers and their families is the focus on stress. Also, if unsurprisingly,

BSE and FMD outbreaks feature prominently as causes of stress, with occasional

references to bTB. Historically, BSE and FMD were both major epidemics of

consequence throughout the UK. In contrast, bTB has had a more regional or local

focus in ‘hot spots’ of south west England and south Wales, although there is concern

to limit its spread far beyond. Irrespective of the specific disease, it is informative to

look at cattle disease as a problem in a generic sense with the potential to cause stress

in people.

A Policy Studies Institute report (PSI, 2005) is based on interviews with farm families

and workers about sources of stress, including the effects of animal disease. It reports

a number of interviewees diversifying their businesses away from conventional

agriculture as a direct response to a crisis, thus aiming to become less reliant on

farming as their main source of income. Both BSE in the mid-1990s and the FMD

outbreak of 2001 were frequently mentioned inducements. But opportunities for

diversification were tempered, for example, by the fact that the economic viability of

tourist enterprises already in place were also undermined by disruption of the rural

economy caused by FMD. The regional impacts were uneven, as might be expected.

In the areas worst affected by these diseases, whole communities were severely

affected. Market prices fell, and not just local markets but whole counties were

virtually closed down with major disruption of normal life.

Not least important is evidence of many interviewees being extremely critical of the

way both BSE and FMD crises were handled by the government. Lack of trust and



25

suspicion became hallmarks of farmers’ attitude towards Defra. This occurred in an

environment where, as is in the nature of the problem, a disease outbreak occurs

suddenly and unexpectedly. Unremarked in both this and other studies reviewed

below is that BSE initially involved an apparently new and completely unknown

pathogen. At least FMD is understood. Allowing for such differences in their

specific characteristics, both diseases create uncertainty for farmers. Either disease is

an occasional threat yet always lurking in the background, or an imminent threat once

an outbreak occurs. Uncertainty about a potentially catastrophic effect on a farm

business allied to lack of trust in authorities’ competence to help is a recipe for stress.

Obviously this can affect farmers and their families, but other farm workers are not

immune either.

Although the period covered by the research meant that PSI (2005) is particularly

concerned with BSE and FMD, it also mentions the impact of bTB at the time the

interviews took place. Such ‘stock crises’ were identified as a source of stress for the

whole of farms’ workforces, and in particular for farmers’ spouses, adult children

working on the farm, and farm workers. Often this was because their farm work

involved developing a close emotional bond with animals they had frequently raised

from birth. Premature death of an animal was seen by one farmer’s wife as

representing a kind of failure for farmers (page 48). Another farmer spoke of dairy

cattle as families going on for generations, and their loss as a result of disease

something irretrievable.

Such emotional damage inflicted on people quoted and most closely concerned with

animals include nervous breakdown, on-going anxiety at recollection of working with

the carcasses of dead animals, inability of a worker to face returning to his place of

work for several weeks after FMD, fear of job loss by a full-time employed farm

worker, and farmers’ anxiety about being able to provide a livelihood for the longer

term. Pertinent to the present study is that although BSE and FMD were described as

dramatic diseases, bTB “just drags on and on” with infected animals lacking any

obvious symptoms to alert farmers to the existence of their problem. All such effects

directly linked with disease outbreaks add to a perception that livestock farming is

anyway inherently stressful (page 80). The constant demands of tending animals,

busy and vulnerable times such as at lambing and calving, and the unpredictability of



26

the work all add to a perception that the demands of livestock farming are often non-

negotiable. There is less opportunity to reschedule the ordering of tasks in ways that

suited workers better.

Similar problems have been documented in other countries. A Canadian survey

(Walker and Walker, 1987) found that farmers scored higher than non-farmers on a

range of stress-related symptoms, including chronic tiredness, difficulty relaxing,

forgetfulness, loss of temper, problems concentrating, back pain and sleep disruption.

Mixed and dairy farmers scored higher on these symptoms than arable ones.

McGregor et al. (1995) identified a similar catalogue of problems in the UK. Their

survey of farmers attending agricultural shows similarly found that livestock farmers

suffered higher levels of stress than arable farmers, with dairy farmers having

particularly raised levels. With specific reference to FMD in the Netherlands, Van

Haaften et al. (2004) investigated its psychological impact on farmers. A perspective

not mentioned in comparable UK literature was that the Dutch authorities were seen

by farmers as denying the bonds between them and their animals, only looking at

animals as a means to production. Closer to UK conditions was that during the Dutch

FMD crisis farmers felt they had lost all autonomy. Thus farmers felt hopeless and

helpless during culling. To some extent this complements the finding by Peck et al.

(2002) and Peck (2005) that UK farmers affected by FMD scored higher than other

groups on psychiatric morbidity (73% as against 33% of farmers unaffected by

FMD). As a result, farmers were more likely to turn to their own communities and to

local vets than to any officials for support.

As regards the severity of stress, Watts (2002) shows apparently anomalous

behaviour, admittedly for a sample of 530 farmers in the single county of Somerset,

in that arable, not livestock, farmers exhibited the highest rate of psychiatric

morbidity. But also revealing was that foot and mouth disease was not cited as a

major issue for any of those farmers whose mental well-being was worst affected.

That was in marked contrast with those farmers who had no significant problems of

mental health. They did cite FMD as high on their scale of concerns. More important

for everyone, both worst and least affected, was having to put up with form filling,

regulations, and government bureaucracy. Though limited as a source of evidence,

this result does suggest that an explanation for dairy farmers’ apparently irrational



27

decisions from a narrow economic perspective might not be explained by the

damaging costs of animal disease outbreaks. Rather, it might be one source amongst

many others that creates feelings in farmers of being overburdened by the demands of

impersonal bureaucracy.

Yet this does not mean that the direct effects of animal disease on people are

unimportant. For example, again in relation to the 2001 epidemic, Deauville and

Jones (2001) surveyed various help providers (e.g. primary care and mental health

services, advice and support agencies) for farmers affected by FMD in Wales. They

found a broad consensus that the situation was having a health impact on those

affected. This was mainly on the mental health of individuals, but GPs emphasised

that consistent stress over a period of time would lead to physical health impacts.

One helpline noted that 50% of calls related to the foot and mouth situation were

showing symptoms of mental health impacts. The general consensus was that the

impacts on people’s mental health and well-being may be longer term, possibly over a

period of 1 to 2 years. There was also a perception that many farmers would leave

farming and then face stresses related to finding new employment and a new way of

life. For those who remain in farming there would be stress related to starting again.

The above review leaves no doubt about the deleterious effects of stress on cattle

farmers, their families and workers. Inevitably, given the catastrophic consequences

of both BSE and FMD for society far beyond farming itself, the literature emphasises

those diseases. There is very little mention of bTB. Nevertheless, to reiterate the

point made at the start of this section, it is animal disease in general that merits study.

It has adverse impacts on the economics of livestock production and on the decision

processes of livestock farmers subject to consequent stress. Each disease has its

particular technical characteristics, as a pathogen, for the severity and distribution of

its effects on people in farming and beyond, and for the strategies available for its

control. Thus information about any effects specifically associated with bTB will add

to current knowledge.

Longer term effects of a bTB breakdown

The longer-term effects of bTB on farm businesses are not well understood. Few

publications touch on the issue. A partial exception is Temple and Tuer (2000).



28

Their analysis of the financial costs of disruption because of bTB is the only source

found that analyses beef and dairy herds in detail. It considers the economic

consequences of making forced adjustments to a farming system, for example on the

demand for housing, labour and feed. Also analysed are the implications of forced

retention of cattle for their quality and hence market price. Aside from slaughtering,

for which compensation anyway is paid to offset a major source of direct cost, the

main costs of bTB control measures listed are from restrictions on movement of cattle

on and off the farm, repeat testing, and compulsory cleaning.

Superficially, the consequences of restrictions and measures for disease management

are short run. They might appear to endure on a farm only as long as bTB is present.

In practice, short-term adjustments can have longer-term effects. For example, the

authors quote typical replacement rates in beef suckler herds at about 17%. Based on

realistic assumptions about the timing of movement restrictions, buying in

replacements, and their age, for a beef flying herd selling suckler calves, their

calculations show that in a 50 cow herd a single bTB reactor could reduce calvings in

following years by 8.5. Thus short-term adjustments unintentionally can become

‘fixed’ in the sense that a farm’s configuration of resource use, once changed,

imposes a degree of inflexibility on what happens in the longer-term.

Bennett (2004) touches on longer-term issues as part of his study of the economic

impact of bTB and alternative control policies. Longer-term costs are seen as far

reaching. For example, valuable bloodlines can be lost. Diversification from milk

into beef and sheep production is also mentioned by farmers surveyed, as is stress as a

reason why a farmer’s wife gave up work, presumably on the farm. An implication

of this is that even if her work had been in some off-farm occupation, the loss of

money income might represent a significant loss to the farm household, not for

consumption purposes but as a source of finance for investment in the farm business.

A further dimension of Bennett’s work was his application of the Reading Land-Use

Allocation Model (LUAM), a linear programming model of agriculture in England

and Wales, to investigate the effect of veterinary testing costs and levies on dairy and

beef farms in bTB hotspot areas. The results showed little effect on milk production

in those areas, but some shift in beef production and increase in sheep production.

Such an approach is informative when new policies for bTB control are under



29

consideration, because the potential effects for longer term farm adjustments can be

simulated. Bennett notes that the relatively small impacts predicted reflect limited

viable alternatives to dairy and beef production in the areas studied.

Others of Bennett’s findings echo other sources. For example, one of Bennett’s

respondents increased his sheep enterprise and decreased his beef enterprise as a

result of bTB. Other farmers deliberately kept excess cattle numbers to a minimum.

No evidence was found supporting any negative effects on a tourist or property letting

enterprise. Two farmers specifically mentioned devaluation of the farm business,

because a history of bTB might reduce a farm’s market value and make it more

difficult to sell. Open-ended questions revealed that bTB could cause cash flow

problems and loss of control of the farm business. Also, under movement restrictions

farmers would be forced to keep store cattle, in effect having to create a new

enterprise. This last observation recalls outcomes of Temple and Tuer’s detailed

analysis.

Finally, Sheppard and Turner (2005) closely complement other authors’ findings.

But additionally, their survey of 61 farms showed that most farmers reported some

long-term effects bTB breakdowns, though the severity varied with individual

circumstances. Also, one-third of farmers affected said they had cancelled or

postponed planned investment in livestock, premises or equipment. Almost one-third

had been forced to cancel or postpone their expansion plans as a direct result of bTB.

One in six farms had diversified away from cattle breeding and production in order to

reduce the potential business risks of further bTB incidents. Importantly, the authors

concluded not only that they had evidence of widespread personal impacts on

farmers, their families and farm staff, but also that there appeared to be a general

reluctance to highlight this.

This finding is broadly consistent with Thomas et al. (2003), who found in the wake

of the BSE crisis for a representative cohort of 606 farmers, family members and

farm workers, that British farmers have a lower prevalence of psychiatric morbidity

than the British household population as a whole. But this seemed to be contradicted

by the relatively high male suicide rate amongst farmers (1% of all male suicides in

the age range 16-64). The authors attribute the apparent inconsistency to farmers’

fatalistic attitude towards their own life, plus their access to lethal methods.



30

Importantly, the authors stress the need for future studies to assess the characteristics

of farmers that might make them particularly susceptible to thoughts that life is not

worth living. Not admitting to the existence of a problem does not mean that a

problem does not exist. At the time of writing, the authors recommended

investigating the effect of extra stress likely to be experienced by British farmers as a

result of the then recent FMD outbreak. Reference has been made to relevant work as

part of this review. In view of the current growth of concern about bTB, a similar

comment could be made about that.

Badgers and bio-security

Investigations of badger ecology and the various ways by which badgers come into

contact with cattle can, in principle, yield information that helps farmers find ways to

modify their husbandry practices to protect cattle from bTB infection. For example,

Phillips et al. (2000, 2003) have reviewed ways in which husbandry changes, such as

changing from set-stocking to strip grazing, or avoiding feeding cattle in the field, or

off the ground, could be beneficial. As part of their own study of badger movements

around farm buildings, Ward et al. (2008) summarise other evidence pointing to the

wisdom of investing in bio-security. They quote Cheeseman and Mallinson (1981)

and Garnett et al. (2005) who consider that bTB prevalence might be higher than the

norm amongst badgers that visit farm yards, because their behaviour is influenced by

their infection. Remote surveillance of badger behaviour, the focus of the Ward

study, showed that when badgers excreted and scent-marked inside buildings it took

place mostly in feed stores. Therefore the presence of infectious badgers behaving in

this way suggests a serious threat to cattle who consume contaminated feed.

Defra publishes general guidelines for improving husbandry3, but Tolhurst et al.

(2008) is a rare example of research into the specific means by which badgers and

cattle can be kept apart, and the difficulties of so doing. Their focus is on the scope

for keeping badgers away from farm buildings by using an electric fence. On balance,

the evidence is that badger behaviour is changed when they are challenged by an

electric fence. But a problem for farmers is the sheer inconvenience of having to

create secure areas by such fences without the nuisance factor of impeded normal

3 http://www.defra.gov.uk/animalh/tb/publications/index.htm



31

access. The authors conclude that new designs for electric fences should be

considered to minimise such problems.

In a study of latrine distribution, Delahay et al. (2007) illustrate the sheer complexity

of badgers’ social behaviour, though with some cause for optimism that the natural

environment they inhabit can be conducive to limiting badger-to-cattle contact. For

example, there is evidence that woodlands or thick hedgerows are more attractive

locations for badger latrines that harbour a risk of cross-infection. Thus there appears

to be scope for managing cattle accordingly so as to limit the possibilities of contact

with contaminated locations on or adjoining pasture. As the authors conclude,

“Augmentation of hedges or double fencing of walls and hedges with

appropriate stock-proof (but badger-permeable) fencing may effectively exclude

cattle from associated latrines. However, such measures require financial

investment and maintenance. For example, vegetation in fenced-off areas needs

to be cut to avoid cattle grazing at their edges and to maximize continued use by

badgers. Management of high-risk areas and manipulating habitat composition

as suggested here, and altering grazing regimes may represent practical steps

that farmers can take to reduce cattle exposure to badger latrines. However,

until the potential disease control benefits of such measures can be

demonstrated experimentally, there may be little incentive in the farming

community to adopt them.” (Delahay et al., 2007, page 318)

Ward et al. (2006, 2008) concur that currently there is scant evidence on quantified

risks or the cost-benefit considerations to enable farmers to make properly informed

investment decisions. The majority of farmers who co-operated with the remote

surveillance study had not invested in bio-security, while Bennett and Cook (2005)

observed that even farmers who had experienced a herd breakdown did nothing by

way of new bio-security measures to protect their cattle in future. Ward et al. (2008)

speculate that such reluctance may be partly explained by inaccurate advice given

earlier by the Ministry of Agriculture, Fisheries and Food (1999). The recommended

minimum height for locating cattle troughs was too low to stop badgers getting

access. Thus farmers’ trust in the reliability of official information was undermined.

Also, most farmers in the Ward study were unaware of just how frequently badgers

visited their farms.



32

On the evidence of the above sources, currently there is a dearth of information on the

extent to which farmers can, or do, adapt their farming systems to minimize

potentially hazardous contact between badgers and cattle. A conclusion arising from

the information presented is that having a detailed knowledge of local conditions,

both as regards badger behaviour in the natural environment and the configuration of

farm buildings and related equipment, is essential to define the technical possibilities

for farmers to keep cattle satisfactorily isolated. In turn, the possibilities have

economic implications for farmers that need to be quantified. It is evident that the

issue of bio-security for bTB control merits closer analysis.

Conclusions

The main conclusions arising from the above are as follows:

• Overall, trends in cattle production, especially dairy farming, are not explained

by reference to conventional economic variables alone.

• Neither is it easy to identify and quantify other factors affecting cattle farmers’

decisions, including their emotional reactions to outbreaks of animal disease,

that to date have mostly been excluded from consideration.

• The stress effects of BSE and foot and mouth disease epidemics on farmers and

their families have been investigated, but bTB is largely neglected.

• Conceivably, farmers’ perceptions of the consequences of animal disease are

inextricably linked with their attitudes towards authorities, and particularly

government, who are seen more as a source of problems than their solution.

• Conspicuous by its absence is any substantive evidence in the literature of the

financial implications of investment in bio-security measures aimed at keeping

cattle from contact with badgers.

• There is no evidence of any significant long-term adjustments in farm resource

use to improve bio-security on cattle farms.



33

3 FACTORS WHICH RESULT IN LONGER-TERM IMPACTS

Introduction

Although the primary focus of this chapter is on the factors which are most likely to

result in longer-term impacts on farm businesses if a bTB breakdown occurs, it is

helpful to begin with some contextual analyses. One of the key findings of the

literature review (Chapter 2) is that, from the standpoint of conventional economic

research looking at the performance of the cattle industry, the overall trends in cattle

production, and particularly dairying, over recent years are not adequately explained

by reference to conventional economic variables alone. Under RA7 the research

programme used a range of information, principally drawn for the Agricultural

Census in conjunction with additional data from VetNet, in a statistical examination

of trends in cattle numbers in the bTB-endemic areas of England and Wales.

One possible longer term cost of bTB and its control is that cattle farmers give up

cattle production or switch to less profitable cattle systems (for example, moving

from dairying to beef). The objective of the work done for this part of the research

study was to examine the evidence relating to temporal changes in cattle populations

(as recorded by Defra’s June Census/survey) to see whether there is a connection

between bTB endemic areas and relative divergence in trend changes in cattle

populations over the longer term. This work aimed to examine the available data to

establish the existence, or otherwise of dissonance in cattle population trends for

‘bTB endemic’ areas and ‘bTB-free’ areas. The detailed methodology and results are

presented in Appendix E.

Regional changes in cattle farming: the GIS study

This section reviews the findings on regional changes in cattle numbers, and cattle

farming systems from RA7, the GIS study of changes in cattle populations. A

summary of the effects of (a) time (1995-2004) and (b) being in a high bTB incidence

parish compared to being in a parish with no bTB incidents during the study period is



34

shown in Table 3.1. A plus or minus sign shows that there was a significant effect (at

P≤0.05) and the direction of the effect. An equals sign shows that there was no

statistically significant effect of time or bTB incidence category.

Table 3.1 The GIS study: summary of the effects of time and bTB on outcome variables for each area studied, and all areas

Area

West Midlands South West Wales All three areas

Outcome

variable

Time bTB Time bTB Time bTB Time bTB

Total cattle - = - - - = - =

Dairy females - = = = = + - +

Beef females + + + + + + + =

Ratio dairy: beef - = - - - + - +

Cattle farms - = -* -* - + -* +*

Minus signs (-) indicate a significant negative effect; plus signs (+) indicate a significant

positive effect and equals signs (=) indicate no significant effect. * indicates that the

interaction between census year and bTB category was significant.

An example of the data for the mean number of cattle farms per parish by census year

and stratified by region and bTB incidence category is shown in Figure E1. The

difference in the rate of decrease between Categories 0 and 1 over time (interaction

term) in the South-West can be seen.

Overall, all of the outcome measures decreased over time apart from beef females,

whose numbers increased in each region and over all areas. This indicates a move

from dairy to beef in all three areas studied over the period 1995-2004. Parishes with

a high bTB incidence compared to those without had higher mean numbers of beef

females in all three areas when analysed separately. This was the case even when

taking into consideration 1980 census counts, suggesting that these parishes either

have a higher number of beef cattle due to the effect of bTB, or have a higher

incidence of bTB due to having a greater number of beef cattle. However, in the ‘all

areas’ combined analysis, the incidence category had no effect on the mean number

of beef females in the parish. This suggests that the parish sample in the ‘no bTB

group’ category across all three regions had higher mean numbers of beef females

than the ‘no bTB group’ category parishes in the separate analyses.



35

Figure 3.1 The GIS study: rate of change in the mean numbers of cattle farms per parish stratified by region and bTB incidence category

0

5

10

15

20

25

30

35

1995 2000 2004

Census year

Mean number of cattle farm

s per parish

WM 0

SW 0

Wales 0

WM 1

SW 1

Wales 1

Regions are represented by a single colour and incidence categories by a single symbol.

The numbers of dairy females and the ratio of dairy to beef females were greater in

parishes with a history of bTB in Wales and in the ‘all areas’ combined analysis.

Since the mean number of beef females was increasing over time in Wales while total

cattle numbers were diminishing, this suggests a definite move from dairy to beef.

However, the higher numbers of dairy cattle in bTB endemic areas in Wales for all

census years indicates that high number of dairy cattle may be associated with the

high bTB incidence in Wales, an effect that is also significant in the ‘all areas’

combined analysis.

The mean number of cattle farms was significantly greater in parishes in the high

incidence category in the South-West, however the significant interaction between

census year and incidence category was negative, showing that the rate of decrease in

the mean number of cattle farms from 1995-2004 was faster for parishes with higher

bTB incidence. This effect appears to be stronger between 2000 and 2004 (Figure

1E). This interaction is similarly significant when all three areas are analysed

together, suggesting that this increased rate of decline of cattle farms in high bTB



36

incidence parishes (i.e. using a cut-off of 6-7%) is an effect common to all hotspot

regions. This appears to provide objective evidence that bTB is having a long term

negative effect on the numbers of cattle farms in the UK.

In conclusion, where time has a significant effect when controlling for incidence

category, there is an overall increase or decrease in cattle or farm numbers over time,

which could be due to a variety of reasons. Where incidence category affiliation has

a significant effect when controlling for time, but the interaction is not significant, the

mean numbers of cattle and cattle farms are greater in one group of parishes

compared to the other. This is more likely to be a cause rather than an effect of high

bTB incidence or, conceivably, a correlate of a farming practice that is more

conducive to bTB, since there is no differential rate of change over time (i.e. with

increasing severity of bTB). Where the interaction term is significant, this represents

a differential time effect between parish groups, which suggests that it is the high

bTB incidence itself that is driving the rate of change.

Farm-level evidence for bTB as a driver of strategic business change

This spatial analysis has identified clear links between the level and incidence of bTB

and statistically significant changes in cattle farming, over and above the general

trends over the lest two or thee decades. In particular, this provides support for the

conclusion from the literature review, that analyses of economic variables alone have

not been able to fully explain the observed changes. The discussion turns now to

some of the findings of the farmer interview survey, RA3, regarding the role played

by a bTB breakdown4 as a driver of strategic change within their farm businesses.

The general approach used to identify farms where bTB has been a driver is

introduced in Chapter 1, and the results represent as objective an assessment of the

situation as was possible in a socio-economic survey of this nature. A summary of

the farms where bTB was identified as a driver of change is given in Chapter 4 (Table

4.8, see also the accompanying discussion) and more detailed results are given in

Appendix B. Drawing more widely on the results of the farmers’ interview survey,

responses to a series of questions looking at farmers’ outlook and attitudes were

4 This should be understood to include also the influence of successive bTB breakdowns, and also the

more subtle influence of perceptions of risk in bTB-endemic areas, and where localised ‘hotspots’ have

developed: all will have played a part in influencing farmers’ thinking and decision-making.



37

considered separately for ‘bTB-driver’ farmers. In Table 3.2 gives the results for

farmers’ attitudes to statements about British farming and their own farm.

Table 3.2 Attitude to farming, ‘bTB driver’ farms and ‘non-bTB driver’ farms compared

Current attitude towards

British farming

All farms ‘bTB driver’

farms

‘Non-bTB

driver’ farms

Farming has no future – I

intend to give up

10 (7%) 8 (14%) 2 (2%)

Farming has a limited

future – I need to diversify

9 (6%) 5 (9%) 4 (4%)

I see my future in farming

and I want to increase the

size of my farm business

25 (16%) 8 (14%) 17 (18%)

I am happy to stay farming

as I am now and for the

foreseeable future

57 (38%) 15 (26%) 42 (44%)

I am worried about my

future in farming but I

don’t know what else I can

do

22 (14%) 10 (18%) 12 (13%)

I see my future in farming

but I expect that I will have

to change my farming

practice

25 (16%) 8 (14%) 17 (18%)

Declined to answer 4 (3%) 3 (5%) 1 (1%)

It will be seen that, by comparison with those farmers whose strategic decisions had not

been influenced at some time by bTB, the ‘bTB-driver’ farmers are fairly consistently

less optimistic about the future of farming, and less likely to see their own futures, and

those of their family, in farming. Traces of this more negative attitude are evident also

in their answers to a series of questions looking at their attitudes to their local and

farming community (Table 3.3).



38

Table 3.3 Farmers and their communities, ‘bTB driver’ farms and all sample farms compared

Strongly

agree

Agree Disagree Strongly

disagree

All farms 19 (13%) 96 (63%) 34 (22%) 3 (2%) As a farmer, I am a

respected member of the

local community ‘bTB driver’

farms 4 (7%) 31 (54%) 20 (35%) 2 (4%)

All farms 25 (16%) 78 (51%) 48 (32%) 1 (1%) Bad press has

undermined farmers’

standing in the local

community

‘bTB driver’

farms 11 (19%) 32 (56%) 13 (23%) 1 (2%)

All farms 13 (9%) 53 (35%) 81 (53%) 5 (3%) Local residents are not

sympathetic to farmers

and their needs ‘bTB driver’

farms 5 (9%) 19 (33%) 33 (58%) 0 (0%)

All farms 27 (17%) 76 (50%) 47 (31%) 1 (1%) Local Authorities do not

understand farmers and

their farming needs ‘bTB driver’

farms 11 (19%) 23 (40%) 23 (40%) 0 (0%)

All farms 26 (17%) 100 (66%) 26 (17%) 0 Farmers should more

actively promote

farming interests ‘bTB driver’

farms 6 (11%) 37 (65%) 14 (25%) 0 (0%)

The data suggest that ‘bTB driver’ farmers are less likely to consider themselves ‘a

respected member of the local community’ (although less likely to blame this on the

‘bad press’), more likely to regard local residents as unsympathetic to farmers and the

needs of the industry, more likely to view Local Authorities as lacking in

understanding of farmers and farming, and less supportive of the proposition that

‘farmers should more actively promote farming interests’. Although it might be

argued that the differences between the two groups are not so great, taken together

there is a clear suggestion of a less optimistic outlook.

Turning now to the farmers’ values and objectives, and comparing the responses of

farmers on ‘bTB driver’ farms with all sample farms, the findings from the interview

survey are summarised in Table 3.4. While the data are largely self-explanatory,

several key themes emerge. First, the farmers on ‘bTB driver’ farms are very much

less likely to regard profit maximisation as the key objective in running a farm



39

Table 3.4 Farming values and objectives, ‘bTB driver’ farms and all sample farms compared

Strongly

agree

Agree Disagree Strongly

disagree

All farms 28 (18%) 86 (57%) 36 (24%) 0 (0%) In running a farm as a

business, maximising

profit is most important ‘bTB driver’

farms 1 (2%) 6 (11%) 33 (58%) 17 (30%)

All farms 9 (6%) 63 (41%) 70 (46%) 9 (6%) Farmers should

conserve/improve farm

landscape/habitats,

regardless of profits

‘bTB driver’

farms 2 (4%) 28 (49%) 24 (42%) 3 (5%)

All farms 36 (23%) 98 (64%) 16 (11%) 1 (1%) Beyond earning a

reasonable income, the

main joy of farming is

the lifestyle

‘bTB driver’

farms 11 (19%) 41 (72%) 5 (9%) 0 (0%)

All farms 22 (15%) 101 (66%) 26 (17%) 1 (1%) The most important

thing to me is to

maintain an attractive

lifestyle for my family

‘bTB driver’

farms 7 (12%) 41 (72%) 8 (14%) 1 (2%)

All farms 29 (19%) 73 (48%) 41 (27%) 7 (5%) My objective is to

ensure there is a viable

business for my

successors when I retire

‘bTB driver’

farms 5 (9%) 26 (46%) 22 (39%) 4 (7%)

All farms 59 (39%) 82 (54%) 10 (6%) 1 (1%) Farming today depends

on forces beyond

farmers’ control, all they

can do is to adjust to the

situation

‘bTB driver’

farms 21 (37%) 33 (58%) 3 (5%) 0 (0%)

All farms 10 (7%) 107 (70%) 30 (20%) 5 (3%) Farmers should only be

eligible to claim the

Single Payment if their

farms meet the required

standards for cross-

compliance

‘bTB driver’

farms 3 (5%) 37 (65%) 16 (28%) 1 (2%)



40

business than the sample farmers as a whole. Secondly, there are small but important

differences between the two groups on environmental issues, with ‘bTB driver’

farmers more likely to agree that farmers have some responsibility to conserve and

even improve landscape and habitats ‘regardless of profits’. Thirdly, there is little

difference between the two groups on questions of ‘farming lifestyle’, but ‘bTB

driver’ farmers appear less focussed on handing on a viable business. This may be

linked with the possibilities of succession presented in Table 3.5, which shows ‘bTB

driver’ farmers as much less likely to expect a successor to take over the family farm

in due course.

Table 3.5 Expectations on succession: ‘bTB driver’ farms and all sample farms compared

Definitely/

very

likely Possibly

Unlikely/

definitely

not N/A

All farms 54 (35%) 38 (25%) 57 (38%) 3 (2%) Do you expect a

member of your

family to take on the

farm business after

you?

‘bTB

driver’

farms 16 (28%) 13 (23%) 27 (47%) 1 (2%)

A much more detailed table of famers’ responses to a wide range of questions

concerning changes made to their farms over the preceding ten years will be found in

Appendix B (Question 7(b)). By comparison with ‘all farms’, the ‘bTB driver’ farms

were consistently more likely to have reduced their cattle farming activities:

• More likely to have decreased the size of their farmed area.

• More likely to have decreased their owner-occupied area.

• More likely to have stopped an enterprise.

• More likely to have decreased their dairy enterprise.

• More likely to have decreased their beef enterprise.

• More likely to have stopped their beef enterprise.

• More likely to have increased their arable enterprise.

• More likely to have decreased the number of people working on the farm.

These findings paint a picture of a group of farms which is, at best, marking time, and

at worst actually going backwards. Broadly, the findings of this part of the research

are that farmers who have had to modify their farm businesses in some way because



41

of awareness of the dangers (to their businesses) of bTB and its possible longer-term

consequences – strictly, those who have made past strategic business decisions where

bTB has been one of the drivers5 - tend to be less positive about farming in general

and their farm in particular, less positive about their standing in the community and

less likely to see a future for their family in farming.

Characteristics of’ bTB driver’ farms

The discussion turns now to a brief overview of some of the characteristics of ‘bTB

driver’ farms, in relation to both farm type (broadly, dairy vs beef) and bTB

breakdown category. The latter classification uses the analytical framework adopted

after an initial analysis of the VetNet database looking at the incidence and scale of

breakdown of bTB in Great Britain in the decade to July 2007 (see Table 4.1 and

accompanying discussion). This distinguished three broad groups of farms in terms

of the scale of their bTB breakdown: ‘light’ (Group A, three or fewer cattle taken, and

less than six months under restriction), ‘long period under restriction’ (Group B, more

than twelve months) and ‘large number of cattle taken (Group C, more than 10 per

cent of cattle herd taken). Table 3.6 presents the sample details.

Table 3.6 The farm interview sample: ‘bTB driver’ and ‘non-bTB driver’ farms, by farm type and bTB category

All farms by farm type

Beef Dairy Total

'non-bTB driver' 48 (62%) 47 (63%) 95 (62%)

'bTB driver' 29 (38%) 28 (37%) 57 (38%)

Totals 77 (100%) 75 (100%) 152 (100%)

All farms by bTB category

Group A Group B Group C Total

'non-bTB driver' 31 (84%) 52 (58%) 12 (46%) 95 (62%)

'bTB driver' 6 (16%) 37 (42%) 14 (54%) 57 (38%)

Totals 37 (100%) 89 (100%) 26 (100%) 152 (100%)

Note: Group A = ‘lightly affected’, Group B = ‘long period under restriction’ and Group C = ‘large

number of cattle taken’

Rather more than a third of the sample farms self-identified as ‘bTB driver’ farms,

using the approach outlined in Chapter 1. As would be expected, few of the farms in

5 Note that for those farms where bTB had been a driver of change, in 80% of cases it was the primary

driver and 16% the second most important driver (see Table 4.8).



42

the lightly affected group had made decisions because of bTB. Given the scale of

their bTB breakdowns, the fact that some even of this group have taken bTB into

account in making business changes is of interest and might imply a number of

different things, such as early investment in bio-security or merely a subjective (and

possibly pessimistic) assessment of risk based on the local bTB situation.

This analysis is extended to the two identified farm types, ‘dairy’ and ‘beef’, and the

results are presented in Tables 3.7 and Figure 3.2, and Table 3.8 and Figures 3.3

respectively. These are important analyses since they point to one of the key factors

Table 3.7 Interviewed dairy farms: ‘bTB driver’ and ‘non-bTB driver’ farms, by bTB category

Group A Group B Group C Totals

'non-bTB driver' 19 (86%) 23 (56%) 5 (42%) 47 (63%)

'bTB driver' 3 (14%) 18 (44%) 7 (58%) 28 (37%)

Totals 22 (100%) 41 (100%) 12 (100%) 75 (100%)



Figure 3.2 Interviewed dairy farms: ‘bTB driver’ and ‘non-bTB driver’ farms, by bTB category

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Group A Group B Group C

'non-bTB driver'

'bTB driver'





43

associated with longer-term effects from a bTB breakdown. Taking dairy farms first,

it will be seen that few of the Group A (lightly affected) farms were subsequently

defined as ‘bTB driver’ farms; in other words, few had cited bTB as an influence on a

past strategic decision for their business. In contrast, nearly half of Group B (long

period under restriction) were ‘bTB driver’ farms, and nearly two thirds of Group C

farms (large number of cattle taken) were ‘bTB driver’ farms.

For dairy farms, this points to the loss of a large number of animals as being in

general most likely to be linked with an impact on farm decision-making. Clearly,

this is closely followed by farms which have a bTB incident which leads to a long

period under movement restrictions, as occurs where there are successive breakdowns

or an initial breakdown is followed by an extended period when the herd fails to go

clear: Group B comprises farms which have been under restriction for more than one

year.

In Table 3.8 and Figure 3.3 the results for beef farms are given on a similar basis.

Although the pattern is generally very similar it will be noted that, by comparison

with the dairy farms, Group A (lightly affected) beef farms are slightly more likely to

be ‘bTB drivers’ while those in the more heavily hit farms (Groups B and C) are

slightly less likely to be ‘bTB drivers’. These results point to a second finding, which

is that dairy farms, especially those which suffer the loss of a large number of cattle,

are particularly susceptible to the consequential pressures of a bTB breakdown. As

will be seen in the detailed analysis in Chapter 5, the findings of which are

summarised later in this chapter, these results are corroborated by the findings of the

farmers’ postal survey, which used the GHQ-12 to assess the mental health of

farmers, their families and farm staff.

Table 3.8 Interviewed beef farms: ‘bTB driver’ and ‘non-bTB driver’ farms, by bTB category

Group A Group B Group C Total

'non-bTB driver' 12 (80%) 29 (60%) 7 (50%) 48 (62%)

'bTB driver' 3 (20%) 19 (40%) 7 (50%) 29 (38%)

Totals 15 (100%) 48 (100%) 14 (100%) 77 (100%)





44

Figure 3.3 Interviewed beef farms: ‘bTB driver’ and ‘non-bTB driver’ farms, by bTB category

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Group A Group B Group C

'non-bTB driver'

'bTB driver'



A short case study (Case Study A), drawn from one the respondents to the farmers’

interview survey, may serve to highlight the sorts of impacts a bTB breakdown can

bring, in this case on a dairy farm.

Case Study A - dairying The current partners in business moved to this farm some years ago, and bought a second farm two years ago. The partners’ business principle, on which this farm was founded, is to steadily and continuously expand both the amount of land owned and the size of the dairy herd. Their eventual aim is to be able to reduce their involvement in the day to day running of the business and allow their two daughters (and their families) to take on a viable business. However, each time the business has begun to expand the dairy herd, they get a positive bTB test result and a number of cows are taken for slaughter. Since 1995 at least 64 cows have been taken. This may not seem a large number of cattle over a 12 year period, but the movement restrictions that are placed in the business each time there is a positive test result represent a real setback to their plans: it means that replacement cattle for the herd cannot be purchased, so herd growth is delayed; and the farm becomes overstocked with rearing other cattle (e.g. calves etc) that are not part of the partners’ business plan. The partners are beginning to believe that their plans for expanding the business will not be implemented completely.



45

Insuring against a bTB breakdown: practical issues

It was clearly important in assessing the longer-term economic affects of a bTB

breakdown to consider the issue of farm businesses being covered against the risks by

taking out insurance. This was explored as part of the farmers’ interview survey, and

even at the stage of piloting the questionnaire it became apparent that this was not a

straightforward issue. Moreover, it is consistent with the general direction of

government policy that the farming industry should be prepared to take a greater

share of the responsibility for ensuring business continuation under adverse livestock

disease situations; the effective use of insurance is one important way in which this

can be achieved. The research findings on this issue are reported here.

Farmers’ views on bTB insurance

As mentioned above, during piloting the questionnaire for the farmers’ interview

survey the issue of bTB insurance was raised, and in particular the difficulty at least

some farmers had experienced of continuing their original level of bTB insurance

after a couple of breakdowns (and claims). The main difficulty was that the insurance

premium became too high to justify, given the production economics of their farm

business. The survey recorded the proportion of respondents with bTB cover and

found that two thirds (68%) had never had such insurance and the proportions appear

to fall over time and with successive claims (Table 3.1).

Table 3.9 Incidence of bTB insurance, initial and subsequent breakdowns

bTB Event

1

Event

2

Event

3

Event

4

Event

5

Event

6

Not insured for

any bTB

breakdowns

Insured 32% 14% 7% 3% 2% 2% 68%

Note: proportions for events 2 to 6 based on total sample only

Of this sample of 152 farms less than one in five (18%) were currently insured

against a bTB breakdown, although the follow-up question about the reasons for non-

insurance was not well completed. Farmers were asked about their view of the role of

bTB insurance in reducing farm business risk, and their responses are given in Table

3.2. These responses illustrate a wide divergence of opinion on this issue, ranging

from those who regard such insurance as ‘too expensive to justify’ (42%) or ‘it’s



46

never worth insuring against animal disease’ (12%) to those who are well aware of its

role as a business tool (28%). One in three farmers was recorded as being ‘unable to

get insurance because of bTB history’.

Table 3.10 Farmers’ views of the role of bTB insurance in reducing business risk at farm level

It’s a useful way to reduce business risk 28%

It’s never worth insuring against animal disease 12%

It’s always been too expensive to justify 42%

I’ve been unable to get insurance because of bTB history 33%

Since the bTB breakdown, insurance is too expensive 28%

Other 18%

*Note: percentages add to more than 100% since more than one box could be ticked

While these tentative findings point to (a) a widespread lack of appreciation of the

potential role of insurance in reducing the financial risk a farm business faces with a

bTB breakdown, it is also clear that (b) there appears to be a less than universal

availability of bTB insurance after having had a bTB breakdown; and (c) there is a

real likelihood that escalating premiums price farmers hit by bTB out of the insurance

market, at least in the longer term.

The insurance industry: some current views

In order to provide a broader perspective to farmer’s views on the availability and

cost bTB insurance, two insurance companies offering insurance in the agricultural

sector were approached with some specific questions. The aim was to provide a more

balanced view of the issue, broadly reflecting the position of the insurance industry

rather than identifying differences between the individual firms. It should be noted

that this does not purport to be in any sense a comprehensive, or even representative,

review of the provision of bTB insurance at the current time.

First, the firms were asked whether their policy towards insuring against the risks of a

bTB breakdown had been reassessed in the light of adverse claims experience. The

responses confirmed that this was the case, with much stricter acceptance criteria than

previously (.e.g. ten years ago). Normally, bTB insurance is now only available as

part of an overall farm business insurance package (bTB cover is part of a combined

policy), and issuing new cover is likely to be dependent on a good recent history (e.g.



47

a clean loss history over the past three years or, in certain areas, the herd must have

recently had a clear routine test). Commonly, all bTB cases are reviewed every year

and if there is adverse claims experience there is a possibility of a loading being

applied. Specifically, it is clear that both firms still accept new business insuring

against bTB (e.g. from a potential new customer who wishes to have this cover, or

from an existing customer who hasn’t previously insured against bTB), provided that

the application falls within the strict acceptance criteria and with the premium subject

to any loading that may be appropriate.

Secondly, the two firms were asked about their approach to bTB insurance in the case

of farms which have not yet made a bTB claim. For example, does the premium vary

according to perceived risk based on location (e.g. comparing a farm in a known bTB

‘hotspot’ with a farm further away)? On this issue there appears to be a divergence in

approach. One firm currently rates according to (a) the parish testing interval and (b)

their own loss experience within a region. An example given was that a farm in an

annual testing parish in Cornwall would be charged more than someone in an annual

testing parish in Lancashire (see also comment below on monitoring the pattern of

bTB breakdowns). In the case of the other firm, it was stated that the premium does

not vary according to perceived risk; rather, the insurance risks are assessed in their

entirety. Both firms offer the same level of cover irrespective of area, although it was

pointed out that the premium charged may have an influence on the level of cover

chosen. One firm stated that the availability of cover may well differ from farm to

farm.

Thirdly, the situation in relation to farms which have made a bTB claim was

explored. Both firms were unequivocal on this: a bTB claim will not automatically

trigger a higher renewal premium; rather, any subsequent premium loading will be

based on the emergence of an adverse claims pattern. One firm indicated that in these

circumstances claim payments in comparison to premiums for an individual client are

taken into account, together with other factors such as the length of time the cover has

been in force, and the performance of the ‘combined’ policy as a whole. It is clear

that both firms do still provide bTB cover after a farmer has made a first claim,

although continued losses will increase the likelihood of the premium increasing.

With regard to the level of cover available, one firm stated that following a bTB claim



48

the level of cover now available is lower than it used to be. While the basic level of

cover will still be available, the higher level of cover (against the consequences of

movement restrictions, for example) previously offered will not – this has not been

available as a new cover for some years. Moreover, movement restriction cover will

not be renewed (for those who already have it) if the herd is not on routine testing

following the discovery of reactors.

Fourthly, it follows from the above that neither firm has reached the point where it

does not offer cover against a bTB breakdown, but the two firms were asked about

the broader issues involved. Although this hasn’t happened yet, both firms appeared

to accept that this was always a possibility. In such (hypothetical) circumstances, the

aggregate value (and the total number) of claims would be the major driver, but as

mentioned above the performance of the policy as a whole always has a strong

influence. It was clear that both companies make every effort to offer renewal terms

to their policyholders, while recognising that underwriting exigencies may result in a

premium judged unacceptably high by the policy-holder (‘it may sometimes become

a pound-swapping exercise’).

Both firms were asked whether they monitor the pattern of bTB breakdowns in

assessing risk and setting premium levels. While both firms are moving in this

direction, only one currently has this approach fully implemented, using increasingly

sophisticated statistics in assessing bTB insurance rates. This has resulted in

differential rates for different areas and regions, based on bTB breakdown patterns.

Meanwhile the other firm is implementing a new system which will accomplish this

in the near future, although it will take several years for consequences of this

approach to be fully evident. This change in approach from the use of national rates

governed solely by parish testing frequency was blamed on the spread of bTB-

endemic areas.

Finally, it is clear that both firms remain committed to offering the best terms

available to their clients, typically using regional, even local, knowledge in assessing

premiums, and being as fair as possible under the circumstances. Following a claim,

or an adverse claims pattern, changes in terms may include a combination of a loaded

premium and a cumulative excess (not providing cover for the first x reactors within



49

that period of insurance), an assessment made after taking all factors into account6.

One firm recognised that this approach may have provided a good reason for some

clients to stay with the firm (i.e. so as to maintain a level of bTB cover they would not

be able to obtain elsewhere while they are undergoing a breakdown).

In summary, this short consultation with two insurers appears to corroborate farmers’

responses about the cost and availability of bTB insurance. It is clear that the spread

of bTB has already caused revisions to the cost of bTB insurance and the level of

cover offered by insurers, an adverse economic effect as far as farm businesses are

concerned but one which appears to have been inevitable. Moreover, this short

consultation suggests that further adjustments to premiums and cover, driven by both

claims experience and statistics on the incidence of bTB, cannot be ruled out for the

future.

The human dimension: key findings

In pursuance of the agreed objectives, two of the RAs in this research programme

were specifically concerned with the human dimension of a bTB breakdown (RA5,

the farmers’ postal survey, and RA6, the GP Case Studies) while two others partially

addressed this issue (RA2, the stakeholder consultation, and RA3, the farmers’

interview survey). The detailed findings of these studies are presented and discussed

in full in Chapter 5, but in the present context it is useful to provide here a summary

of the key findings.

It was found that these studies augmented previous research on both (a) the short-


crises on the farming community. While a bTB breakdown is less dramatic than

some other livestock disease crises (such as FMD and BSE) by its nature the

persistence and pervasiveness of the control programme represents a clear source of

on-farm stress, typically over an extended period. The stakeholder consultation

reflected perceptions of a range of human health effects as a consequence of bTB,

including physical, mental, emotional and social changes. Many highlighted

‘uncertainty’ and ‘lack of control’ as powerful drivers of human health problems.

6 The cover typically offered is based on insuring a number of animals for a certain payment per

reactor or contact (e.g. 200 animals aged over 18 months @ £500 each).



50

The GHQ-12 study provides objective evidence of the scale of the longer-term effects

of a bTB breakdown. In general terms, a much greater proportion of farmers exhibit

signs of psychiatric morbidity that the population as a whole. Overall, the highest

stress levels are seen on farms which have been under livestock movement

restrictions for a long period. However, on dairy farms, where people are generally

more stressed than on beef farms, the greatest stressor is the loss of a large number of

cattle. There is evidence that bTB is associated with raised stress levels for people

other than the farmer, particularly spouses; but the sample was too small for reliable

results for farm workers. For farmers, a bTB breakdown causes significant stress:

this is highest for dairy farmers, especially where they have lost a large number of

cattle; while for both trading beef and suckler beef farmers, a long period under

restriction is the greater stressor.

The study of rural GPs identified that farmers typically present late with symptoms

and, for mental health issues in particular, GPs probably only see a small proportion

of cases. Several GPs pointed to the range of pressures affecting farmers, particularly

livestock farmers, of which bTB is only one; and the consequent difficulty in direct

attribution of cause and effect. The lack of control experienced as a result of a bTB

breakdown was identified as an important stressor; even so, bTB was thought to be a

less serious problem than the FMD epidemic of 2001. A range of moderating factors

include age, farming experience, farm ownership, diversification, the availability of

support and the significance of other crises.

At interview, most farmers identified adverse effects on their daily lives, and often on

those of their families and others. Key effects relate to uncertainty, stress, time

pressures and financial worries. Knock-on effects on the local and farming

communities do not appear in general to be very significant.


In this chapter some findings of the research programme not reported elsewhere have

been discussed in the context of understanding the influences associated with longer-

term impacts on farm businesses arising from a bTB breakdown (or breakdowns). In

addition, key findings from two of the RAs are reviewed in order to pull together the



51

main evidence on the factors most associated with longer-term effects as a result of a

bTB breakdown.

In an analysis that focussed on the areas most severely affected by bTB, the South

West and West Midlands regions of England, and Wales, the GIS study of regional

trends in cattle farming identified some significant differences between bTB parishes

(parishes with a bTB incident or incidents) and other parishes. Although there were

some variations, there was abroad consistency in the findings across regions:

• An established and consistent trend away from dairying towards beef cattle over

the decade to 2004.

• High bTB incidence parishes have more beef female cattle than other parishes.

• There is strong evidence that it is the high bTB incidence which is itself driving

the rate of change.

• The rate of decrease in the number of cattle farms was greater in the high bTB

incidence parishes.

• In Wales high numbers of dairy cattle in a parish may be associated with the

high bTB incidence.

• A high bTB incidence appears to be a significant influence on the trends over

time (in cattle numbers, in the numbers of cattle farms, in the relative balance

between dairy and beef cattle).

The identification of farms on which strategic decisions in farm planning have been

influenced by bTB, as one factor among others (though typically the main factor),

was designed to minimise the possibility of bias through ‘leading’ questions. Farmers

on ‘bTB driver’ farms are consistently less optimistic about the future of farming, and

less likely to see their own futures, and those of their family, in farming. They are

also more negative in their attitudes to their local and farming communities.

There are notable differences between farmers on ‘bTB driver’ farms and others on a

wide range of farming values and objectives. Moreover, they are much more likely to

have made reductive adjustments to their businesses in recent years. Finally, ‘bTB

driver’ farmers are much less likely to expect a successor to take over the family farm

in due course. Distinguishing between cause and effect in these characteristics would

require further specific research.



52

On dairy farms, the evidence is that the loss of a large number of animals is most

likely to be linked with an impact on farm decision-making. This is closely followed

in importance by spending a long period under movement restrictions. Even on some

lightly affected farms, bTB may be an influence of decision-making. Lightly affected

beef farms, in contrast, are rather more likely to take bTB into account in their

decision-making. Similarly to dairy farms, ‘large number of cattle taken’ is ranked

above ‘long period of restrictions’ in terms of decision-making impacts, although

both proportions are lower than for dairy farms. Table 3.11 summarises the findings.

Table 3.11 Interviewed farms: ‘bTB driver’ and ‘non-bTB driver’ farms, by farm type and bTB category

Group A Group B Group C Totals

Dairy Beef Dairy Beef Dairy Beef Dairy Beef

'non-bTB driver' 86% 80% 56% 60% 42% 50% 63% 62%

'bTB driver' 14% 20% 44% 40% 58% 50% 37% 38%

Totals 100% 100% 100% 100% 100% 100% 100% 100%



One important way of tackling business risk is the careful use of insurance, and

insurance against livestock disease has long been one of the means available for

farmers. With regard to the availability of bTB insurance, however, it appears the

situation is not now as clear as it might once have been. It is evident that the spread

of bTB has already caused revisions both to the cost of bTB insurance and the level of

cover offered by insurers, an adverse economic effect as far as farm businesses are

concerned but one which appears to have been inevitable. While the firms consulted

remain committed to providing the best terms to their clients, and being as fair as

possible in the circumstances, the market reality is of rising claims. Further

adjustments to premiums and cover, driven by both claims experience and statistics

on the incidence of bTB, cannot be ruled out for the future.

With regard to the effects on human health and well-being, while a bTB breakdown is

less dramatic than some other livestock disease crises by its nature the persistence and

pervasiveness of the control programme represents a clear source of on-farm stress,

typically over an extended period. The GHQ-12 study provides objective evidence of

the scale of the longer-term effects of a bTB breakdown. Overall, the highest stress



53

levels are seen on farms which have been under livestock movement restrictions for a

long period. However, on dairy farms, where people are generally more stressed than

on beef farms, the greatest stressor is the loss of a large number of cattle.

There is evidence that bTB is associated with raised stress levels for people other than

the farmer, particularly spouses. For farmers, a bTB breakdown causes significant

stress: this is highest for dairy farmers, especially where they have lost a large

number of cattle; while for both trading beef and suckler beef farmers, a long period

under restriction is the greater stressor.

In summary, the factors most closely associated with a bTB breakdown having longer

term effects on farm businesses, the principal findings of this research are

summarised in Table 3.12. Two distinct areas of impact are distinguished: those

within the area of farm business economics, as identified by the effects on strategic

decision-making; and those directly affecting the people involved on the farm, in

terms of human mental health (psychiatric morbidity).

Table 3.12 Factors which result in longer term impacts from a bTB breakdown: a summary of the research findings

Farm type

Dairy farms Beef farms

Area and

nature of

impact ‘Lightly

affected’

‘Long

period

under

restriction’

‘Large

number

of cattle

taken’

‘Lightly

affected’

‘Long

period

under

restriction’

‘Large

number of

cattle

taken’

Farm business

economics

(strategic

decision-making)

+ ++ +++ + ++ +++

Human mental

health

(psychiatric

morbidity)

++ +++ ++++ + +++ ++

Note: Plus signs (+) indicate a positive effect as a factor associated with longer term impacts,

the more (+) the stronger the association.

Although completely different samples of farmers were used, there is a very good fit

between the two estimates. Overall, the factor most likely to result in longer-term

impacts is the loss of a large number of cattle, closely followed by being under



54

movement restrictions for a long period; even a light brush with bTB has measurable

effects on farm decision-making on a significant minority of farms. However, for

beef farms as a whole the most stressful factor was being under movement

restrictions for a long period. The two sets of estimate are not mutually incompatible,

of course, and it’s likely that if sample sizes had been large enough to distinguish

between beef suckler herds and beef trading units further finessing of these findings

would have resulted.



55

4 ESTIMATED LONGER-TERM ECONOMIC EFFECTS

Introduction

As discussed in Chapter 1, it was recognised from the outset of this research project

that identifying good quantitative information on the longer-term economic effects on

a farm business of a bTB breakdown would be far from straightforward. The nature

of the research problem is that the on-farm pattern and incidence of bTB breakdowns

is extremely variable, and imposed on an industry which is in any case characterised

by almost infinitely variable systems, resources and performance. The definition of

‘longer-term’ for the purposes of this research is discussed in Chapter 2, and may be

simply stated as encompassing both long and medium term impacts of bTB

breakdown on farm resource use, for practical purposes being defined as effects

extending beyond one year.

Furthermore, although the identification and valuing of short-term (direct) effects is

relatively straightforward (see, for example, Bennett,2004; and Sheppard and Turner,

2005) by their very nature longer-term effects are likely to be more diffuse,

potentially even more variable and sometimes close to impossible to measure, even

when they are identified. Perhaps the principal reason for this problem is that, given

the existence of adverse economic effects over a period of years, most if not all

farmers could be expected to make adjustments to their businesses to mitigate these

effects, at least to the extent that this was possible. This implies that in some cases

the longer-tern effects may be little more than a farm business operating in a way

which, initially at least, was second-best: whether or not this is sub-optimal may be an

open question to be determined empirically.

It is important to appreciate that the pattern of bTB breakdowns is very varied, and

the broad findings of earlier studies in this respect (Bennett, op cit; Sheppard and

Turner, op cit) appear to be still relevant. An initial analysis of the national VetNet

database, undertaken by the VLA, is shown in Table 4.1.



56

Table 4.1 The national incidence of bTB breakdowns: an initial analysis of the VetNet database, July 2007

Group Category of breakdown Farm

type

Number of

farms

affected

As % of

total farms

with a

closed

breakdown

As % of all

farms with

a bTB

breakdown

Dairy 1,412 9.9% 25.5%

Beef 2,089 14.6% 37.7%

A Lightly affected (3 or fewer

cattle taken and less than 6

months under restrictions

from 01/07/97) Other 284 2.0% 5.1%

Dairy 851 5.9% 15.3%

Beef 575 4.0% 10.4%

C (but

not in

A)

Large number of cattle

taken (> 10% of total cattle

numbers (total numbers

taken/total numbers tested) Other 92 0.6% 1.7%

Dairy 16 0.1% 0.3%

Beef 193 1.3% 3.4%

B (but

not in A

and C)

Long period of movement

restrictions (> 1 year)

Other 36 0.3% 0.6%

Total farms in Groups A, B

and C

5,548 38.7% 100.0%

Dairy 3,708 25.9%

Beef 4,505 31.5%

D Remainder (no bTB

breakdown in the last 10

years (from 1 July 1997))

Other 555 3.9%

Total farms with a closed

breakdown in the last 10

years (from 1 July 1997)

14,316 100.0%

Source: Veterinary Laboratories Agency (personal communication). Data refer to the total number of

Farms in GB (E, S & W) experiencing a bTB breakdown (either confirmed or unconfirmed) in the ten

years up to 01/07/2007, where that breakdown was finished i.e. a TB10 had been issued.

These data demonstrate several important facts about the pattern and incidence of

bTB in those areas most affected by bTB (the ‘bTB endemic’ areas) and selected for

the field study, but must be taken as indicative since they exclude farms with active

(i.e. on-going) bTB breakdowns. First, at the time of the analysis (July 2007) more

than six out of ten farms with cattle (61.3%) had either not had a bTB breakdown for

at least ten years, or had never had a bTB breakdown. Of those farms which had had

bTB breakdown but at the date of analysis were clear (i.e. the incident was officially

‘closed’) more than two thirds (68.3%) were only ‘lightly’ affected, defined here as

having lost three or fewer cattle over the ten years to July 2007 and spent fewer than

six months under livestock movement restrictions. Of the two groups which had been



57

more seriously affected (Groups B and C in Table 4.1), most had had a large number

of cattle taken (in total, over the period); only 4.3% of those farms which had had a

bTB breakdown cleared up had spent a long time under movement restrictions. It

should be noted that this latter figure in particular clearly understates the total

proportion of farms under long-term movement restrictions, because it excludes those

farms with on-going restrictions, but more comprehensive data are not available at

present.

These data (Table 4.1) identify several important characteristics of the scope and

pattern of bTB disease breakdowns, an appreciation of which is important in

developing an understanding of the current industry-level impacts of any longer-term

farm-level effects resulting from a bTB breakdown. Taking a Great Britain

perspective, the following conclusions can be drawn:

• Most cattle farms (more than 60%) either have never had a bTB breakdown or

have been clear for many years.

• Most of those farms which have had a bTB breakdown (more than 66%) have

been affected relatively lightly, losing less than three animals and being free of

movement restrictions after no more than six months.

• Small - but by no means insignificant - proportions of all bTB breakdowns

result in more serious farm-level effects: some 27% of cases lose significant

numbers of cattle (more than 10% of their herd) and a further 4% or so of cases

have to endure the imposition of livestock movement restrictions for an

extended period.

BUT

• The proportions of farms with bTB breakdowns, both light and heavier, will be

substantially greater in those counties and areas of England and Wales which

are subject to a much greater than average incidence of bTB (the ‘bTB

endemic’) areas.

By its very nature, a bTB breakdown would not be expected normally to result in

longer-term effects on the farm business concerned in the case of the Group A farms

identified above – those defined as ‘lightly affected’ in Table 4.1. Indeed, bearing in

mind the boundaries chosen for definition it would seem more than likely that even

some of the farms falling into Groups B and C (the two categories of more heavily



58

affected farms) would be unlikely to exhibit measurable longer-term effects at farm

business level, where these are defined as changes in farm resource use. It follows

that identifiable longer-term economic effects are likely to be found on a relatively

small proportion of those farms which have had a bTB breakdown, perhaps 10 – 15%.

It was for this reason that a stratified random sample was used in conducting the

farmers’ interview survey (see Chapter 1).

The expectation of finding only a relatively small number of farms with significant

longer-term economic effects from a bTB breakdown does not in any way diminish

the importance of the issue, of course. The possibility of such extended impacts from

a bTB breakdown had been recognised in several previous studies (Bennett, op cit;

Sheppard and Turner, op cit; NAO, 2003) and, indeed, the present study was

commissioned by Defra to identify and, if possible, to quantify such effects, thus

completing the evidence base as it relates to effects at the farm level. Rather, this

glimpse of the pattern of bTB breakdowns provides both a first indication of the scale

of the problem, and also a sampling framework for the empirical work. Case Studies

B and C, however unusual in themselves, illustrate something of the wide-ranging

nature of longer-term economic effects that may be experienced.

Case Study B – suckler beef and beef rearing This is a large 240 hectare (600 acre) farm business which operates from two holdings, where the main emphasis was originally towards the 100 cow beef suckler herd and followers. The business also has a number of pigs (approx 60 sows) and sheep. The majority of the income came from the suckler herd and store cattle. However, in 2001 all of the cattle were taken due to a significantly high number of positive bTB test results. This had a dramatic effect upon the business as the main income source was taken away and the business was not able to restock for at least 12 months. It then took at least a further 18 months to two years before the business could begin to receive an income from the suckler herd, as the farm was restocked with a small number maiden heifers and it takes time for the cattle to calve and to produce calves suitable for sale. As a result of this event, the business has changed its business plan and principles. The farmer and his son now trade under two separate names, one from one holding and one from the other, so that if one farm is shut down with bTB the other can continue trading. They also have tried to reduce the emphasis that was placed on the suckler herd by expanding both the sheep and breeding pig enterprises. Some haulage work is also undertaken and the business also owns a holiday cottage. They hope that this will enable the business to spread the risk.



59

Case Study C – beef rearing and deer This farmer lost his entire herd to a bTB cull as 23 of the 24 cattle in the herd reacted during a routine whole farm test. The farmer received compensation and has since re-stocked without any major long-term impacts on the cattle side of the farm business. However, this farm also has approximately 200 farmed deer, mainly producing breeding stock. As there is no pre-movement testing equivalent for deer this farmer is in a difficult situation. He has had one case of bTB in the deer but because there is no test available Defra are unable to give him a clear TB status. He has since had to move his cattle to a different holding in order to be able to trade cattle at all. This still leaves the farm with deer that cannot be sold as breeding stock, the main focus of that enterprise. Furthermore, for transporting deer, a drug has to be used (administered by dart) but afterwards these animals are then classed as being unfit for human consumption. This was no problem for breeding stock, of course; but means a lot of his stock are not able to be sold as meat either. As a result, the deer enterprise is costing money rather than contributing to profit. Taken together, the consequences of the bTB breakdown are having a large impact on the farm’s cash flow, and as a result both the farmer and spouse have had to find alternative work off-farm to pay the mortgage and have money to live off.

The specific research objective of concern here is ‘To provide broad estimates of the

longer-term economic effects of a bTB breakdown in the context of a range of ‘farm

system and bTB breakdown’ scenarios’; and this was tackled through a number of

research activities. The two principal research activities which explored the nature,

scale and extent of longer-term economic impacts from a bTB breakdown were (a) an

interview survey of farmers whose cattle herds had suffered a breakdown, and (b) an

analysis of FBS data, augmented by data from the VetNet database on the scale and

extent of the breakdown (carried out with the help of Rural Business Research, the

FBS consortium). In addition, it was expected that the stakeholders’ consultation

would provide some evidence on this topic, while the farmers’ postal survey (the

GHQ-12 survey of mental health) might also provide supplementary evidence. This

chapter is structured as follows: the findings from the stakeholders are followed by

the results of the FBS analyses, then the relevant information from the interview

survey and concluding with the key findings from the postal survey.



60

The stakeholder consultation: pointers to farm economic problems

Stakeholders with a direct interest

The identification of adverse effects from a bTB breakdown commonly pointed to

farm incomes, reductions in capital value, the inhibition of business development and

the delaying, or abandonment, of plans for future business development.

The majority of respondents felt that there are often severe longer term financial

effects following a bTB breakdown, although the scale of these effects is dependent

on the duration and extent of any outbreak. While occasional breakdowns interrupt

trade in the short term, where animals are under restriction for a number of years this

has had longer term repercussions for many farm businesses. Farms may not be able

to trade stock effectively or, at least, respond in a normal manner to market

developments; for years, and are unable to sell cattle in the open market. Prolonged

movement restrictions lead to an adverse cash flow, due to increased feed

requirements and problems with cattle welfare. Following the loss of breeding cattle,

and herd replacements, there is often disruption of calving patterns long into the

future, which has a clear financial impact.

In some cases where replacement cattle have been bought in, this has lead to the

inadvertent introduction and promotion of other diseases (such as rotavirus and

pneumonia) and this is then associated with increased veterinary costs in the

treatment of these. In the longer term there is very often an unwillingness to invest in

the business due to uncertainty about the future, and it was reported that many

farmers in this situation feel expansion of the business is going to be difficult.

Pre-movement testing was considered by some respondents to be a burden over the

longer term, and as a bTB control policy it does not appear to be working. The extra

costs such as fencing were considered by some respondents to be another long term

capital cost, while others viewed this as more of a short term item. Some of those

who commented on this felt it was extremely naïve to think that fencing alone would

stop badgers entering.

A number of other adverse effects for the farm business were identified, although

many of these are less specifically of an economic nature. It was stated by some that,



61

in the longer term, more farmers may decide not to keep cattle, or some at least would

change to other farming methods; and that additional production and disease costs

may well make the sector unviable. If this happens, it was felt, it will have adverse

effects on the environment and some suckler beef systems, for example, have been

moved from upland and hill land due to declining economic returns. Some felt that

the gradual depopulation of cattle in Wales will result in a changing landscape and

this could damage tourism. An imbalance in ecosystems in the countryside due to the

reduction in the numbers of cattle will lead to reductions in wildlife such as ground

nesting birds, hedgehogs etc.

Some respondents pointed to changes in (some) farmers’ attitudes to wildlife; in

particular, it was felt, attitudes to badgers have changed and farmers have become

very wary of the presence of these animals on their farms.

There was almost unanimous anger from this group of respondents that Defra had

taken no action to control bTB in wildlife, coupled with frustration that it seemed the

authorities were prepared to protect sick wildlife rather than cattle. One consequence

of this, it was felt by some, is that farmers are losing respect for authority and have

less confidence in wider issues of farm and rural policy development.

Some stakeholders felt that the press attention given to the levels of compensation for

cattle slaughtered may cause some degree of public backlash against farmers in the

future. It was felt by some respondents that there is little account taken of the

consequential loss to farmers associated with bTB.

Stakeholders with an indirect interest

It was agreed by most respondents that the effects of bTB depend on the duration and

frequency of outbreaks and that it was also asserted that over time many businesses

do become unviable. It was thought by one stakeholder that in the long run beef

production will be reduced due to lack of control on bTB. It was noted that dairy

farmers are being forced to shoot Friesian bull calves at birth where there have been

long term breakdowns. Some stakeholders made a comparison of bTB policy

between the UK and elsewhere in Europe, where the objective was given as

eradication (of the disease).



62

Accountants in particular highlighted the trading losses and a significantly increased

borrowing requirement by some farmers, just in order to keep afloat following a

severe bTB outbreak. Again, the extra costs of keeping animals which would

otherwise have been sold and the need to rear more replacements were identified as

common economic problems for affected farm businesses. Fencing costs were

considered by these respondents to be essentially short term.

In terms of other business effects, it was noted that there was less interest being

shown in the breeding of high quality cows, for fear that a lifetime’s work could be

destroyed almost totally. It was reported that there is also less interest in the

environment while no bTB eradication programme is in place.

Table 4.2 Adverse effects of a bTB breakdown on the financial vitality of farm businesses: summary of the stakeholders’ consultation findings

Reduced income due to extra costs of holding animals, extra

costs for testing, fencing etc.

Interrupted calving pattern

Increased borrowing and bankruptcy

Financial effects

Less interest in business development

Loss of confidence in the farming industry and therefore no

interest in succession from family members

Loss of confidence in the industry

Lack of confidence in authority on policy making

Effects on the environment and landscapes

Other business effects

Change in attitudes to wildlife

Taken together the stakeholders’ responses identified quite a wide range of possible

adverse effects on the economics and management of farm businesses arising from, or

exacerbated by, a bTB breakdown. These are summarised in Table 4.2 and these

initial findings informed the later stages of the study.

The longer- term economic impacts of bTB: evidence from the FBS

An important part of the research into the longer-term economic impacts of bTB, the

desk study using data from a sample of farms in the FBS enabled an objective study



63

of possible economic effects at the level of the whole farm to be carried using high

quality information already in existence.

The two principal problems in using the FBS database is that it has inadequate

information regarding the incidence of bTB breakdowns, particularly prior to

2005/06; and prior to 2002/03 any bTB compensation received was not separately

identified, so there is no record of which farms had suffered a disease breakdown.

The methodology used to make best use of the economic and management data

available from the FBS is discussed in Chapter 1; and it is sufficient here to state that,

working with Rural Business Research (the FBS consortium), it was possible to

identify a sample of 173 farm businesses whose business principals had given

permission to link their FBS records with those relating to their farms on the Vetnet

database. Of this total, it quickly became clear that some could not be used because

of differences in the sampling frames between the two databases7 and a final usable

sample of 149 farms was identified. These farms had completed at least one year in

the FBS between 2002/03 and 2006/07, and this data could be linked to their current

Vetnet record which thus provided valuable supplementary information on the timing

and scale of the bTB breakdown(s) experienced.

The aim of this research activity, therefore, was to look at the long term economic

impacts, if any, of bTB on farm businesses using the FBS as a source of economic

information on farm performance. This cohort of ‘bTB breakdown’ farms formed the

basis of comparisons with a ‘non-bTB breakdown’ cohort of otherwise similar farms

over the same period, 2002/03 to 2006/07. The main focus was on tracking the

development of these groups of businesses over time, but using wherever possible

sub-groups of the bTB cohort to identify whether any effects applied uniformly across

all farms, or whether there were differences according to farm type. The three farm

types examined were ‘dairy’, ‘suckler beef’ and ‘trading beef’. Lowland/upland

splits were not possible because of the resulting small sample sizes, but this was not

considered to seriously disadvantage the analysis in any case: it was judged that

differences between system were likely to be far greater than between area.

7 The FBS relates to the complete business, while Vetnet data relates to the holding; some businesses

comprise more than one holding.



64

Following the identification (by CPH number only) of the sample farms within the

FBS, a matching exercise was completed in order to obtain a summary of the

information held on the VLA database for the same farms. A total of 73 farms were

present in all five years (see Table 4.3); of these, over half (38 farms) were classified

as dairy, 23 were classified as suckler beef and 12 as trading beef farms. These

classifications were taken from the 2002-03 data.

Table 4.3 The FBS bTB sample: numbers of years in the survey over the period 2002/03 to 2006/07

Number of years in the FBS sample

Total

number of

farms

%

One year 4 2.7%

Two years 24 16.1%

Three years 24 16.1%

Four years 24 16.1%

Five years 73 49.0%

Total 149 100.0%

Only a small proportion of farms had undergone a classification change over the five

year period year period: of the 130 farms in the 2002-03 sample, by the end of the

five year period two dairy farms had been reclassified as suckler beef, two suckler

beef farms had become trading beef, and two trading beef farms had become dairy

while four trading beef had become suckler beef. This preliminary analysis shows a

small amount of fluidity in farming systems in the sample, as defined by farm type,

but also closed off one potential avenue of investigation: not only were the samples

too small to permit study of ‘change of farm system’ as such, but in any case there

was no clear overall trend which could merit further study.

Over the five year period, the numbers of farms available varied between 99 and 130

and the classifications of these in each year are shown in Table 4.4.



65

Table 4.4 The FBS bTB sample, by farm type, 2002/03 to 2006/07

Farm type 2002-03 2003-04 2004-05 2005-06 2006-07

Dairy 77 73 63 59 50

Suckler beef 38 36 37 33 36

Trading beef 15 16 20 19 13

Total 130 125 120 111 99

The length of period during which bTB livestock movement restrictions had been in

force on these farms varied widely: for three farms the restriction period had lasted

over a ten year period, for over a third of the sample (53 farms) the period of

movement restrictions had was in excess of five years, while for over two thirds (105

farms) the period of restrictions was over one year (Table 4.5).

Table 4.5 The FBS bTB sample, by period of movement restrictions

Period of restriction

More than 5

years

1 - 5

years

Less than one

year Total

Number of farms 53 52 44 149

% of total sample 35.57% 34.90% 29.53% 100.00%

The two farm types best represented in this sample of farms were dairy and suckler

beef systems. The numbers of trading beef farms were small, with a maximum of 20

farms in 2004-05, but falling to just 13 farms in 2006-07. Therefore, due to the

unreliability of any inferences that could be drawn using this data, these farms were

not used for further detailed economic data analysis as a identified farm system. The

systems analyses concentrated on the two farm types, dairy and suckler beef.

These two systems were each compared with a control set of data, in both cases being

drawn from otherwise identical farms available within the FBS survey, except that

these farms had not experienced a bTB outbreak during the five year period for which

this information is available as part of the FBS dataset. While it is possible that some

of these farms may have had an earlier bTB breakdown, it is reasonable to suppose

that such breakdowns had been cleared up by early 2002 at the latest. It is known that

no further breakdowns had occurred before March 2007.



66

A wide range of data was extracted from the FBS database for the sample farms for

each of five years under study; and these data have been categorised into four main

indicator areas: income, liabilities and assets, investment and technical performance.

All of the averages used in the data comparisons were calculated as weighted

averages using the allocated weights for England and Wales in the FBS survey. This

weighting removed some of the bias which might otherwise have resulted from small

sampling errors, making the resulting average more representative. In some of the

sub-samples, and for just some of the comparisons, it was found that a small number

of larger farms were exerting a disproportionate emphasis on the calculated means

(typically where an extreme record happened also to have a large weight), causing

skewness in the results. This effect was overcome by standardising the presentation

of the calculated results to the size of the dairy or suckler beef herd: for dairy farms

the data are presented on a ‘per 100 dairy cows’ basis whilst for suckler beef farms,

which tend to have smaller herds, the results are presented on a ‘per 50 beef cows’

basis.

The analysis of dairy farm results

Two different groupings of the dairy farms which had experienced a bTB breakdown

were used in the data analyses. The first group comprised those dairy farms which

were present in each of the five years, 2002-07, and these were defined as the

identical sample. It was felt that this selection gave a good comparator of progress

and change from one year to the next, using the same farms tracked over five years, in

order to look for patterns and trends, and there were a total of 38 farms in this sample.

The second group of dairy farms looked at in detail were those dairy farms which had

been under movement restrictions for a period of more than one year. The total

number of farms in this group varied from year to year, with a minimum of 41 farms

in 2006-07 and a maximum of 61 farms in 2002-03. Separate investigative analyses

were also carried out for those farms which had been under movement restrictions for

more than five years, in an attempt to explore even longer-term effects; however, the

sample sizes were rather small (ranging from 21 in 2006-07 to a maximum of 41 in

2002-03) and, as a consequence, the results were found to be more prone to small

sample influences, and these analyses have not been used further.



67

The control group of dairy farms were all those in the FBS database which had not

experienced a bTB breakdown. The numbers varied quite widely from year to year,

with a maximum of 540 dairy farms in 2002-03 and a minimum of 359 farms in

2005-06. Details of the sample sizes for each of the three groups are given in Table

4.6. Clearly the sample sizes for the control group are large enough to provide very

reliable population estimates as a benchmark for comparison, and even the sub-group

sample sizes should be large enough to provide broad indications. The results have

been calculated using Defra’s FBS weighting system, and it is understood that normal

Table 4.6 Sample sizes, FBS bTB dairy farms, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with

no bTB breakdown)

540 495 390 359 413

Identical sample of bTB farms 38 38 38 38 38

bTB farms, long period of

restriction (non-identical

sample)

61 57 49 49 41

practice is to publish for any sub-group with at least 15 farms in the sample.

However, given that the farm-level effects of a bTB breakdown can be very complex

(Bennett, 2004; Sheppard and Turner, 2005) and that this sample of farms will have

had widely varying scales and durations of bTB breakdowns, points to the

introduction (to the farm business accounts) of yet another layer of differentiation in

addition to the widely varying resources, systems and performance of the population

as a whole. Given this, the results should be viewed as indicative of differences

rather than in any way definitive.

The analyses undertaken look at four broad sets of indicators of economic

performance and business development, in order to allow examination of whether,

and to what degree, there has been divergence between farms which have had a bTB

breakdown and those that have not. Detailed comparative financial data for these

groups of FBS dairy farms are given in Appendix C, Tables C1 to C4. The indicators

cover the following broad categories: farm income, investment, the balance sheet and

technical performance.



68

One further point needs brief discussion. It has been recognised that, for much of the

period under review here, the actual level of compensation received by farmers for

bTB cattle taken was often in excess of the market value – the statutory basis for

compensation – for a variety of reasons associated with the operation of the valuation

procedures (see, for example, NAO Wales, 2003). With effect from 1 February 2006

a new cattle compensation system was introduced in England which uses a ‘table

valuation’ approach and subsequently the Welsh Assembly has also taken steps to

tighten up compensation procedures. The new (typically lower) levels of

compensation in England would have influenced compensation revenues on English

FBS farms from 2006/07 onwards. These changes will have been made manifest in

the final year of the five years of FBS data being examined, and thus introduce a

(valid) source of distortion to the five year trend data.

More broadly, where compensation relates to the loss of breeding animals, this

amounts to a disposal of part of the farm’s productive assets, which are not

necessarily replaced within the same accounting year: if replacements are purchased,

these may take time to locate, but many farmers prefer, for sound animal health

reasons, to rear their own replacements. In all cases where replacement stock are not

purchased in the same accounting year as compensation is received, if the level of

compensation has been over-generous this will have the effect of over-stating both

output and incomes. Even where replacements are purchased within the year, the net

effect is likely to be at least a modest boost to income for essentially short-term

reasons. Since this study is concerned with longer-term effects, most income

indicators have been calculated both including and excluding bTB compensation in

order to better understand the farm-level effects. In practice, it might be expected that

the ‘true’ level of income lies somewhere between the two (i.e. indicators with and

without bTB compensation).

Figures 4.1 and 4.2 illustrate the results for four income indicators, Occupiers’ Net

Income (ONI), Farm Family Income (FFI), Net Farm Income (NFI) and Management

and Investment Income (M&II) for the period 2002/03 to 2006/07. Cash income and

ONI are based on the actual tenure and indebtedness of the farms, while NFI was

until recently Defra’s preferred lead indicator of farm income; M&II is the most

rigorous assessment of the economic performance of the industry. It has not been



69

Figure 4.1 Dairy farms income indicators: cash income and farm family income, 2002/03 to 2006/07

Dairy farms - cash income per 100 cows

£0.00

£10,000.00

£20,000.00

£30,000.00

£40,000.00

£50,000.00

£60,000.00

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with no bTBbreakdown)

Identical sample of bTB farms (incl. bTBcompensation)

Identical sample of bTB farms (excl. bTBcompensation)

bTB farms, long period of restriction (non-identical sample, incl. bTB compensation)

bTB farms, long period of restriction (non-identical sample, excl. bTB compensation)

Dairy farms – farm family income per 100 cows

£0.00

£5,000.00

£10,000.00

£15,000.00

£20,000.00

£25,000.00

£30,000.00

£35,000.00

£40,000.00

£45,000.00

£50,000.00

2002-03 2003-04 2004-05 2005-06 2006-07








70

Figure 4.2 Dairy farms income indicators: net farm income and management and investment income, 2002/03 to 2006/07

Dairy farms - NFI per 100 cows

£0.00

£5,000.00

£10,000.00

£15,000.00

£20,000.00

£25,000.00

£30,000.00

£35,000.00

£40,000.00

2002-03 2003-04 2004-05 2005-06 2006-07






Dairy farms - M&II per 100 cows

-£20,000.00

-£15,000.00

-£10,000.00

-£5,000.00

£0.00

£5,000.00

2002-03 2003-04 2004-05 2005-06 2006-07



Identical sample of bTB farms (excl.bTB compensation)

bTB farms, long period of restriction(non-identical sample, incl. bTB



71

possible to use Defra’s current income indicators based on ‘Farm Business Income’

because the necessary information to calculate these were not collected by the FBS

until 2005/06. It should be noted that the charts provide information for three groups

of farms: two bTB groups, the ‘identical sample’ and the ‘long period under

restriction’ groups; and the control sample of all dairy farms which had not had a bTB

breakdown.

As with all of these analyses, it must be remembered that the beginning of the series

does not mark the beginning of the bTB breakdown, or the beginning of any effects,

whether positive or negative. Allowing for differences in the initial starting positions

(2002/03), these data support a number of broad conclusions:

• There were two years in which relatively high levels of bTB compensation were

received, 2003/04 and 2005/06, and this effect is clearly evident in the charts.

• The two indicators closest to cash flow, cash income and FFI, suggest a steady

divergence in income between both bTB groups on the one hand and the control

group on the other.

• For NFI, the two bTB groups out-perform the control group in all years except

2006/07, although when compensation is excluded they perform slightly worse.

If it is accepted that the long term position lies somewhere between the two

extremes, this points to a broadly similar income position.

• With M&II, which is effectively a residual indicator after all resources except

for the value of the managerial input and an allowance for a return on

investment have been charged, the trends are more volatile but suggest that, in

most years and for most indicators, bTB farms performed less well than the

control group. The only difference between NFI and M&II is the charge made

for the manual labour of the farmer and spouse, and the actual level of labour

input is looked at again later in this analysis.

• Overall, these data point to the possibility of poorer incomes being achieved on

bTB farms, although the differences from the non-bTB farms are not large in

most years.

Figure 4.3 presents the results for two further financial indicators, farm output (an

indication of business turnover) and the off-farm incomes of the farmer and spouse.



72

Figure 4.3 Dairy farms financial indicators: farm output and off-farm income, 2002/03 to 2006/07

Dairy farms - farm output per 100 cows

£0.00

£50,000.00

£100,000.00

£150,000.00

£200,000.00

£250,000.00

2002-03 2003-04 2004-05 2005-06 2006-07






Dairy farms - farmer and spouse off-farm income

0

2,000

4,000

6,000

8,000

10,000

12,000

2002-03 2003-04 2004-05 2005-06 2006-07


Identical sample of bTB farms

bTB farms, long period of restriction (non-identical sample)



73

With respect to farm output, and with a very similar starting position, the two bTB

groups have tracked the control group fairly closely, albeit with just a suggestion of a

slight gap opening up; the difference, however, is not great. Interestingly, off-farm

income appears to be rather higher on both of the bTB groups compared with the

control group, and this has been consistently so over the period except for the ‘long

period under restriction’ group in 2006/07 only. This appears to provide some

corroboration of information from other parts of the research that one response of the

farm family to a bTB breakdown is to increase, or take for the first time, off-farm

employment.

The focus turns now to the pattern of investment and Figure 4.4 presents two such

indicators of investment, for farm buildings and similar infra-structural improvements

and the broader net asset purchases. Capital investment in buildings is lower than the

control group in two of the five years, and higher in three years. There is a dramatic

peak in investment for the ’identical sample’ bTB group in 2003/04, and it could be

argued that the ‘long period under restriction’ bTB group broadly tracks the control

group.

Allowing for greater annual fluctuations in this indicator, although the data point to a

broadly comparable level of investment over the period there is some evidence of

bTB farms investing slightly more than their non-bTB counterparts. This may be

consistent with evidence from the farmers; interview survey on investment in bio-

security measures. A similar pattern is seen with net asset purchases with, again, a

broadly comparable level of investment between bTB and non-bTB farms.

Detailed analyses of aspects of the balance sheets – business assets and liabilities –

are given in Appendix C, Table C3, but these tend to be dominated by the value of

owner-occupied land and are not explored further here. However, Figure 4.5

illustrates the movement in total loans for the three groups of farms over the five year

period. The two bTB groups start with higher loans than the non-bTB control group

and, on average, maintain that position throughout, although there is some narrowing

of the gap in the final year.



74

Figure 4.4 Dairy farms investment indicators: buildings and net asset purchases, 2002/03 to 2006/07

Dairy farms - capital investment in buildings per 100 cows

£0

£1,000

£2,000

£3,000

£4,000

£5,000

£6,000

£7,000

£8,000

£9,000

£10,000

2002-03 2003-04 2004-05 2005-06 2006-07




Dairy farms - net asset purchases per 100 cows

£0

£5,000

£10,000

£15,000

£20,000

£25,000

£30,000

£35,000

£40,000

2002-03 2003-04 2004-05 2005-06 2006-07



bTB farms, long period of restriction(non-identical sample)



75

Figure 4.5 Dairy farms liability indicator: total loans, 2002/03 to 2006/07

Dairy farms total loans per 100 cows

£0.00

£10,000.00

£20,000.00

£30,000.00

£40,000.00

£50,000.00

£60,000.00

£70,000.00

£80,000.00

£90,000.00

2002-03 2003-04 2004-05 2005-06 2006-07




The final area for investigation is aspects of technical performance available from the

whole-farm FBS, and Figures 4.6 and 4.7 present the trends in milk production,

farmer and spouse labour input (as measured by AWUs), total farmed area and total

number of cattle. Each of these indicators will be taken in turn:

• Despite starting from a similar level, average milk yield (measured as milk

production per 100 cows) on both of the bTB groups tended to fall behind the

steady increase in yields seen in the control group; although the differences

were not huge (yields perhaps 6-8% lower in 2006/07, for example) clearly the

aggregate effect of this over a period of years would have an important financial

effect. Overall, of ten bTB data points, six were lower, two higher and two the

same by comparison with the control group.

• The labour input data tell a clear story: farmers(and their spouses) work

consistently longer hours on both bTB groups by comparison with the non-bTB

group. This may be seen as providing some verification to the evidence from



76

Figure 4.6 Dairy farms technical indicators: milk production and farmer and spouse AWUs, 2002/03 to 2006/07

Dairy farms - milk production per 100 cows

5400

5600

5800

6000

6200

6400

6600

6800

7000

2002-03 2003-04 2004-05 2005-06 2006-07




Dairy farms - farmer and spouse AWUs

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2002-03 2003-04 2004-05 2005-06 2006-07






77

Figure 4.7 Dairy farms technical indicators: farmed area and cattle numbers, 2002/03 to 2006/07

Dairy farms – total farmed area

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with no bTBbreakdown) Total

Control sample (all farms with no bTBbreakdown) Tenanted

Identical sample of bTB farms Total

Identical sample of bTB farms Tenanted

bTB farms, long period of restriction (non-identical sample) Total

bTB farms, long period of restriction (non-identical sample) Tenanted

Dairy farms – numbers of dairy cows and total cattle

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with no bTBbreakdown) Dairy cows

Control sample (all farms with no bTBbreakdown) Total cattle

Identical sample of bTB farms Dairy cows

Identical sample of bTB farms Total cattle

bTB farms, long period of restriction (non-identical sample) Dairy cows

bTB farms, long period of restriction (non-identical sample) Total cattle



78

other parts of the study, and other studies, that a bTB breakdown can cause

extended periods of additional work.

• In terms of total farmed area, the data point to little difference between the

‘identical sample’ bTB group and the control group, whether it is all types of

tenure or simply tenanted farms that are considered, but there is some evidence

of the ‘long period under restriction’ bTB farms tending to increase their farmed

area towards the end of the five years period. This may well be linked with the

next indicator.

• Finally, total cattle numbers provide evidence of a more pronounced increase on

‘long period of restriction’ bTB farms towards the end of the period. Of course,

it is not possible to categorically state that this is a direct consequence of bTB

movement restrictions, or whether it marks a change of system; but since this

average change is seen for a group of 41 farms (Table 4.6) it seems most likely

to be a fairly direct consequence of bTB movement restrictions. It also goes

hand-in-hand with the observed increase in farmed area, of course.

This completes the analysis of the FBS dairy farm data, the broad conclusions are set

out at the end of this chapter. Briefly stated, however, there seems to be evidence of

some differentials in terms of income (bTB farms coming slightly lower than their

non-bTB peers; slightly higher investment; higher loans; a greater labour input by

farmer and spouse; lower milk yields; and more cattle being kept on a larger farmed

area.

The analysis of suckler beef farm results

The number of available FBS farms in this group was smaller than for the dairy group

and consequently, for farms which had experienced a bTB breakdown, it was not

possible to look at an identical set of farms over the five year period. Instead, a non-

identical sample was taken for each of the five years, containing a maximum of 37

farms and a minimum of 34 farms. As for the dairy farms, a sub-group comprising

farms experiencing movement restrictions for more than one year was also examined,

although the sample numbers here were very small, so the results are possibly subject

to small sample influences and should be considered as indicative only. Sample size

constraints have prevented the separate analysis of lowland and upland suckler beef

systems. Details of the sample sizes for each group are given in Table 4.7.



79

Table 4.7 Sample sizes, FBS bTB suckler beef farms, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all suckler beef

farms with no bTB breakdown)

575 545 512 485 544

All bTB suckler beef farms 37 36 37 34 37

bTB suckler beef farms, long

period of restriction (non-

identical samples)

22 21 18 16 18

The control group of suckler beef farms were all those in the FBS database which had

not experienced a bTB breakdown. The numbers varied quite widely from year to

year, with a maximum of 575 suckler beef farms in 2002-03 and a minimum of 485

farms in 2005-06. The sample sizes for the control group clearly are large enough to

provide very reliable population estimates as a benchmark for comparison, and the

sample size for the sub-group ‘all bTB suckler beef farms’ should be large enough to

provide reliable broad indications. The sample sizes for the sub-group ‘bTB suckler

beef farms, long period of restriction’ are very small, however, and the results should

be treated with care although, using Defra’s FBS weighting system, normal practice is

to publish for any sub-group with at least 15 farms in the sample.

However, the comments given earlier in respect of the dairy sample bear repetition

here. The farm-level effects of a bTB breakdown can be very complex (Bennett,

2004; Sheppard and Turner, 2005) and this sample of farms will have had widely

varying scales and durations of bTB breakdowns. Inevitably this results in the

introduction to the farm business accounts of yet another layer of differentiation in

addition to the widely varying resources, systems and performance of the population

as a whole. Accordingly, the results should be viewed as indicative of differences

rather than in any way definitive.

As for the dairy farms, the analyses undertaken look at four broad sets of indicators of

economic performance and business development, in order to allow examination of

whether, and to what degree, there has been divergence between farms which have

had a bTB breakdown and those that have not. Detailed comparative financial data

for these groups of FBS dairy farms are given in Appendix C, Tables C5 to C8. The



80

indicators cover the following broad categories: farm income, investment, the balance

sheet and technical performance. Results have been presented on a ‘per 50 cows’

basis.

The changed statutory basis for valuing livestock taken also deserves brief

restatement because of its impact on the trends presented here (see also the earlier

discussion in relation to the dairy group analyses). The new cattle compensation

system which was introduced in England on 1 February 2006 uses a ‘table valuation’

approach and has resulted in typically lower levels of compensation being paid; this is

evident in the 2006/07 FBS data, and will have influenced the identified trends.

As for the dairy farms, most income indicators are shown with and without the value

of bTB compensation, because, where compensation relates to the loss of breeding

animals, this amounts to a disposal of part of the farm’s productive assets. These are

not necessarily replaced within the same accounting year, either because of the time

lapse until suitable replacements can be purchased or because it is preferred to rear

home-bred replacements (e.g. for animal health reasons). Even where replacements

are purchased within the year, the net effect is likely to be at least a modest boost to

income for essentially short-term reasons. Since this study is concerned with longer-

term effects, most income indicators have been calculated both including and

excluding bTB compensation in order to better understand the farm-level effects. In

practice, it might be expected that the ‘true’ level of income lies somewhere between

the two (i.e. indicators with and without bTB compensation).

Figures 4.8 and 4.9 illustrate the results for four income indicators, Occupiers’ Net

Income (ONI), Farm Family Income (FFI), Net Farm Income (NFI) and Management

and Investment Income (M&II) for the period 2002/03 to 2006/07. Cash income and

ONI are based on the actual tenure and indebtedness of the farms, while NFI was

until recently Defra’s preferred lead indicator of farm income; M&II is the most

rigorous assessment of the economic performance of the industry. It has not been

possible to use Defra’s current income indicators based on ‘Farm Business Income’

because the necessary information to calculate these were not collected by the FBS

until 2005/06. The following charts, then, provide information for three groups of

farms: two bTB groups and the control sample of all suckler beef farms which had

not had a bTB breakdown.



81

Figure 4.8 Suckler beef farms income indicators: cash income and farm family income, 2002/03 to 2006/07

Suckler beef farms - cash income per 50 cows

£0

£10,000

£20,000

£30,000

£40,000

£50,000

£60,000

2002-03 2003-04 2004-05 2005-06 2006-07


All bTB farms (incl. bTB compensation)

All bTB farms (excl. bTB compensation)

bTB farms, long period of restriction ( incl.bTB compensation)

bTB farms, long period of restriction (excl.bTB compensation)

Suckler beef farms - FFI per 50 cows

£0

£10,000

£20,000

£30,000

£40,000

£50,000

£60,000

2002-03 2003-04 2004-05 2005-06 2006-07








82

Figure 4.9 Suckler beef farms income indicators: net farm income and management and investment income, 2002/03 to 2006/07

Suckler beef farms - NFI per 50 cows

-£20,000

-£10,000

£0

£10,000

£20,000

£30,000

£40,000

2002-03 2003-04 2004-05 2005-06 2006-07






Suckler beef farms - M&II per 50 cows

-£60,000

-£50,000

-£40,000

-£30,000

-£20,000

-£10,000

£0

£10,000

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with no bTBbreakdown)All bTB farms (incl. bTB compensation)


bTB farms, long period of restriction (incl. bTB compensation)bTB farms, long period of restriction(excl. bTB compensation)



83

It is important to appreciate that the beginning of the series, 2002/03, has been

determined not by when bTB first affected the sample farms, but because the

information available does not allow the identification of farms which had a bTB

breakdown before this date. The longer-term effects, therefore, whether positive or

negative, do not start in the first year of the series: some farms will have had a bTB

breakdown prior to this date, others will have been affected by bTB for the first time

some time in the five years studied. Allowing therefore for differences in the initial

starting positions, these data provide the following broad conclusions with regard to

the profitability of the businesses:

• Incomes on the two bTB groups of farms appear to have been rather more

variable than for the control sample as a whole; and there was only one year in

which a relatively high level of bTB compensation was received, 2004/05; and

incomes on ‘long period of restriction’ farms are fairly consistently lower than

for ‘all bTB farms’.

• With the possible exception of cash income, the control group (all suckler beef

farms) has experienced a steady decline in profitability, however this is

measured, over the five years from 2002/03.

• Taking first the two indicators closest to cash flow, cash income and FFI, almost

all data points over the five years lie below those for the control group, in some

years very substantially below, suggesting a significantly lower level of

economic performance on the bTB-affected farms.

• In respect of NFI, the results are more variable: by 2006/07there has been a

degree of convergence in the NFIs of the three groups although for much of the

five years the bTB groups performed less well.

• The situation for M&II is equally mixed, with no consistent picture emerging

regarding relative levels of income between the two bTB groups and the control

group. Since M&II is a residual measure, it is subject to cyclical swings and

this is evident for both of the bTB groups.

• Overall, against a general (system) trend toward lower incomes, farms with a

bTB breakdown have performed less well in most years, those under movement

restrictions being least profitable. Both of the two bTB groups have shown

much more variability in incomes, with considerable annual fluctuations, but

these swings have not been caused by high levels of compensation payments.



84

Figure 4.10 Suckler beef farms financial indicators: farm output and off-farm income, 2002/03 to 2006/07

Suckler beef farms farm - output per 50 cows

£0

£20,000

£40,000

£60,000

£80,000

£100,000

£120,000

£140,000

£160,000

£180,000

2002-03 2003-04 2004-05 2005-06 2006-07






Suckler beef farms - farmer and spouse off-farm income

£0

£2,000

£4,000

£6,000

£8,000

£10,000

£12,000

2002-03 2003-04 2004-05 2005-06 2006-07


All bTB farms

bTB farms, long period of restriction



85

In Figure 4.10 the results for two additional financial indicators are shown, farm

output (a measure of business turnover) and the off-farm incomes of the farmer and

spouse. Farm output is seen to be markedly lower for each of the two bTB groups,

over the whole period studied. By comparison with the ‘all suckler beef farms

without bTB’ control group, off-farm income is variable and does not provide a

consistent story: for the first three years being studied, off-farm incomes were

markedly higher for both of the bTB groups, while in the least two years the situation

was reversed. This may be primarily the result of changes within the non-identical,

relatively small samples, rather than n indirect consequence of a bTB breakdown.

Turning now to investment patterns, Figure 4.11 presents the results for two

indicators: investment in buildings (including other improvements to the fixed infra-

structure of the farm), and net asset purchases. In four out of the five years

investment in buildings has been substantially lower on each of the bTB groups, the

exception being 2005/06, and is particularly marked on the small ‘long period under

restrictions’ group. With two exceptions, net asset purchases have been lower than

the control group over the period, the exceptions being 2002/03 (‘all bTB farms’

only) and 2005/06 on both bTB groups. Overall, therefore, these data suggest that

suckler beef farms which have had a bTB breakdown have in most of the five years

studied invested less than their bTB-free counterparts.

Detailed analyses of both asset and liability dimensions of these farms’ balance sheets

are given in Appendix C, Table C7; as these tend to be dominated by the value of

owner-occupied land, these are not discussed here in more detail except that it is

worth noting that all groups are well capitalised, with high owner equity ratios and

low gearing.

The picture regarding trends in total loans differs between the three groups Figure

4.12). The control group shows a trend towards a lower level of loans, albeit with a

slight increase in 2006/07. The average level of loans on the ‘all bTB farms’ group

changed very little over the five year period, except for fairly small annual

fluctuations. The level of loans on the ‘long period of restriction’ bTB group

increased dramatically over the four years to 2005/06, rising from an average of some

£20k per farm to £100k; but fell equally dramatically in 2006/07 to an average £10k



86

Figure 4.11 Suckler beef farms investment indicators: buildings and net asset purchases, 2002/03 to 2006/07 Suckler beef farms - capital investment in buildings per 50 cows

£0

£2,000

£4,000

£6,000

£8,000

£10,000

£12,000

£14,000

£16,000

£18,000

2002-03 2003-04 2004-05 2005-06 2006-07


All bTB farms


Suckler beef farms - net asset purchases per 50 cows

-£20,000

£0

£20,000

£40,000

£60,000

£80,000

£100,000

£120,000

2002-03 2003-04 2004-05 2005-06 2006-07


All bTB farms




87

Figure 4.12 Suckler beef farms liability indicator: total loans, 2002/03 to 2006/07 Suckler beef farms - total loans per 50 cows

£0

£20,000

£40,000

£60,000

£80,000

£100,000

£120,000

2002-03 2003-04 2004-05 2005-06 2006-07


All bTB farms


per farm. This result should be regarded as unreliable, and related to the small

sample size.

Finally, the analysis looked at aspects of technical performance on these farms, and

the results for the labour input of farmer and spouse are illustrated in Figure 4.13 and

for farmed area and total cattle numbers in Figure 4.14. The data show that for this

sample of FBS farms the level of labour input was lower on bTB farms (both groups)

than for the control sample, throughout the period with the small exception of the

‘long period under restriction’ group in one year, 2004/05. Further, there tended to be

less difference between the two bTB groups than between the bTB groups and the

control group.



88

Figure 4.13 Suckler beef farms technical indicator: farmer and spouse AWUs, 2002/03 to 2006/07

Suckler beef farms - farmer and spouse AWU

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

2002-03 2003-04 2004-05 2005-06 2006-07


All bTB farms


In Figure 4.14 the results for (a) total farmed area and (b) total cattle numbers are

presented. The data clearly point to the bTB farms (both groups) being smaller than

their non-bTB peers, with a proportionately smaller tenanted land area. Meanwhile,

the average size of the control group has declined over the period but the sizes of both

of the bTB groups remained broadly constant. Another significant finding that

emerges from these analyses is that total cattle numbers on both bTB groups of farms

is higher than on the control group, with some evidence of a widening gap over the

five years; and the number of beef cows is also slightly higher. Bearing in mind the

smaller average farm size, this points to a significantly higher stocking rate on bTB-

affected farms. This would be consistent with general expectations that, while a farm

is under movement restrictions following a bTB breakdown, it is very difficult under

many farming systems to prevent cattle numbers increasing.



89

Figure 4.14 Suckler beef farms technical indicators: farmed area and cattle numbers, 2002/03 to 2006/07

Suckler beef farms – farmed area

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

180.00

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with no bTBbreakdown) total area

Control sample (all farms with no bTBbreakdown) tenanted area

All bTB farms total area

All bTB farms tenanted area

bTB farms, long period of restriction totalarea

bTB farms, long period of restriction tenanted area

Suckler beef farms – numbers of beef cows and total cattle

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

2002-03 2003-04 2004-05 2005-06 2006-07

Control sample (all farms with no bTBbreakdown) beef cows

Control sample (all farms with no bTBbreakdown) total cattle

All bTB farms beef cows

All bTB farms total cattle

bTB farms, long period of restriction beefcows

bTB farms, long period of restriction totalcattle



90

This completes the analysis of the FBS data on suckler beef systems, and the broad

findings are summarised at the end of the chapter. At this stage it may be stated that,

while there are few clear differences between the two groups of farms with a bTB

breakdown and the bTB-free control group, some useful pointers do emerge. By

comparison with the control group, the two bTB groups tended to be more intensively

stocked, to achieve a lower level of output per cow, and to have a lower and much

more variable level of income; farms defined as ‘long period under restriction’

performed least well in terms of economic returns.

Evidence from the farm interview survey

Identifying farms where bTB has contributed to system change

As described in Chapter 1, the research approach adopted in the farm interview

survey took a deliberately neutral route to identifying farms on which significant

changes to the farm system had been made at least partly as a result of bTB. Given

that the sample farms were distributed between three broad categories of bTB

breakdown – ‘lightly affected’, ‘large number of animals taken’ and ‘long period

under movement restrictions’ – it should not be surprising that only 37.5% (57 farms

of the total of 152 farms) identified bTB as a driver of past business change; these

farms were labelled ‘bTB driver’ farms. Table 4.8 summarises the findings.

Table 4.8 The role of bTB as a driver of past business change

Summary Change 1 Change 2 Change 3 Change 4 Change 5

First driver 13 22 11 9 0

Second driver 8 3 0 0 0

Third driver 0 2 1 0 0

Fourth driver 0 0 0 0 0

Notes: 1. On a few farms bTB was cited as a driver for more than one change.

2. ‘Drivers’ of strategic change were ranked in order of importance by the farmer.

3. There is no particular significance in the order in which changes were listed.

4. Data relate to 69 changes on 57 farms.

By focussing early in the interview with the farmer on the nature, timing and scale of

past change to the farm business, and then on the drivers of each change, rather than

on the self-reported effects of bTB on business change as such, the aim was to

identify as objectively as possible those farm businesses where bTB really had been a

driver of change. This information was then used to support a better understanding of



91

the inter-actions between a bTB breakdown, or breakdowns, and farm business

development and adaptation.

Of course, business decisions are rarely if ever taken on the basis of one driver, and

this research points to significant business changes on the sample farms being the

product of careful deliberation involving consideration of several drivers. As Table

4.8 shows, where bTB was identified as a driver of change it was dominantly listed as

either the first (80%) or second (16%) driver, pointing to its specific significance on

at least some of the sample farms. It is important to note here that it might be

expected that business decisions which have long-term consequences are likely to be

influenced not only by the farmer’s actual experience (in this case, of bTB) but also

their expectations, their perception of the risk of a future bTB breakdown. This issue

is discussed further in Chapter 5.

Perhaps confirming this, when asked ‘What do you see as being the main threats your

business?’ farmers’ responses indicated that, for the sample as a whole, bTB was seen

very much as one threat among a number. It was the third most commonly listed

main threat (after ‘legislation/regulations’, and ‘market conditions/profitability’), the

third most cited second threat and the equal second most cited third threat.

Farmers were asked directly about what they considered to be the long-term effects of

a bTB breakdown, and their responses are recorded in Table 4.9. Virtually all

identified the loss of animals as having a long-term effect, closely followed by the

impact of movement restrictions on farm operations and cash flow effects, cited by

about four out of five of the respondents. The implications for expansion plans were

also important (16%).

Table 4.9 Farmers’ identification of the main long-term effects (more than a year) of a bTB breakdown

% of farms

Loss of animals (including multiple events) 97%

Movement restrictions 88%

Cash flow impacts 79%

Interrupted expansion 16%

Other 15%



92

Some business characteristics of ‘bTB driver’ farms

The interview questionnaire collected a range of information about the farmer and the

farm, and details are presented in Appendix B. The ‘bTB driver’ farms were on

average supporting larger businesses, with (self-declared) gross farm sales of £167k,

compared to the all farms average of £155k and the ‘non bTB driver’ farms average

of £148k, while the distribution of these businesses by size of gross farm sales was

comparable with that of the all farms group. It must also be remembered that it is

likely that there has been at least a degree of adverse effect on these farms as a result

of one or more bTB breakdowns, and this effect will have been factored in to these

data. Taken together, these data suggest the bTB driver farms might be operated by

more dynamic farmers, who have had some success in achieving business growth in

any case; and these farmers might be particularly conscious of the potential of an

ongoing bTB scenario to spoil their plans for the future.

Respondents were invited to estimate the average net profit from farming made by

their businesses and the results for all farms, ‘bTB driver’ farms and ‘non-bTb driver’

farms are shown in Table 4.10. These data suggest that, in terms of profitability,

there were some differences, albeit fairly small, between the ‘bTB driver’ farms and

both other comparators.

Table 4.10 Self-declared net profit from farming: interviewed farms by bTB category

Net profit

from farming

Made a

loss

£0 -

£5,000

£5,001 -

£10,000

£10,001 -

£20,000

£20,001 -

£50,000

£50,001

or more

Refused

to say

All farms 20 (13%) 34 (22%) 19 (12%) 26 (17%) 33 (22%) 11 (7%) 9 (6%)

‘bTB driver’

farms 10 (18%) 12 (21%) 12 (9%) 5 (9%) 9 (16%) 3 (6%) 6 (11%)

‘Non bTB

driver’ farms 10 (11%) 22 (23%) 7 (7%) 21 (22%) 24 (25%) 8 (10%) 3 (3%)

Note: Respondents were asked the average of the last three years from their tax accounts.

Their broad distribution is greater, in that ‘bTB driver’ farms are more likely to be

represented in both the loss-making and over £50k profit categories; and they are also

less likely to be found in the middle-ranking profitability groups of between £10k and

£50k net profit. There were no significant differences in the existence of off-farm

sources of income, although it does appear these farms were more likely to have



93

started of-farm employment or self-employment since 2000, by comparison with the

‘non bTB driver’ farms.

Analysis of self-declared external borrowings showed no significant differences

between the three groups. However, there were some differences between their

attitudes to the level of borrowing, as Table 4.11 sets out. Rather more of the ‘bTB

driver’ farmers were likely to regard their borrowings as ‘comfortably low’, and

rather more were at the other extreme of ‘too high for comfort’. Many of the

responses under ‘other’ referred to the farm having no or very low borrowings.

Table 4.11 Farmers’ attitudes to level of borrowing under present circumstances

Attitude which best expresses the farmer’s view All farms ‘bTB

driver’

farms

‘Non

bTB

driver’

farms

Comfortably low (i.e. no cause for concern) 40% 46% 37%

Too low for business growth (thinks should borrow

more e.g. to invest for improvement, expansion)

3% 0% 5%

About right for business growth (sustainable, with a

safety margin)

19% 18% 20%

Too high for comfort (believes the business is

potentially vulnerable to an unexpected shock)

20% 26% 17%

Other 14% 9% 17%

Not applicable 3% 2% 4%

Investment in improved bio-security

One of the longer term effects of the animal health problems caused by bTB and the

joint Government-farming industry campaign to bring the disease under control, is the

imperative for farmers to introduce, and maintain, high levels of bio-security. This

requirement is thrown into sharp focus in the wake of a bTB breakdown, as the

farmer and veterinary surgeon, together with Veterinary Laboratories Agency (VLA)

staff, deal with the issues raised. The farmer’s interview survey explored the changes

made to the farm’s bio-security over the previous ten years, the timing of those

changes (i.e. pre- or post-bTB) and the annual cost of those changes. In fact, only

57% of the sample farms had made changes during the specified period, although



94

Table 4.12 Investment in recent bio-security improvements, by type of improvement

Pre - bTB Post - bTB Resources used

Bio-security measure Number % Number % Total cost (£)

Physical measures Cost per year (£)

Fence off identified wildlife

habitats, walkways, etc.

7 8% 17 20% £4550

Proof buildings, silage clamps,

etc. against wildlife

6 7% 14 16% £110,100

Raise height of feed and water

troughs

5 6% 21 24% £80,060

Double fence farm boundaries 10 11% 19 22% £69,191

Other 1 1% 9 10% £23,550

Other 2 2% 3 3% £2500

Livestock management Cost per year (£)

Herd health plan 29 33% 28 32% £11,090

Isolation of incoming cattle 13 15% 18 21% £2300

Pre-movement testing 4 5% 25 29% £4430

Separate personnel for separate

units

0 0% 2 2% £1600

Isolation of Reactors & IRs 5 6% 34 39% £5850

Stop spreading slurry on

grazing land

3 3% 6 7% £0

Use strip grazing with backing

fence

2 2% 0 0% £100

Other 1 1% 10 11% £18,700

Strategic

Closed herd 29 33% 22 25% £500

Reduced stocking rate 4 5% 13 15% £8,400

Specific sourcing of cattle 8 9% 23 26% £5,500

Other 1 1% 2 2% £0

Note: Changes made during the ten years from 1997

some may have set up bio-security arrangements previously; but in interpreting this

figure it must be noted too that some of the surveyed farms were not fully commercial

units.



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The results are set out in Table 4.12, and the results provide a very interesting insight

into both farmers’ bio-security priorities, and changing priorities after a bTB

breakdown has occurred. Not all listed measures will be equally applicable on all

farms, of course. The most commonly introduced measures are a herd health plan,

and operating a closed herd, closely followed by pre-movement testing, now a

statutory requirement. Other very common improvements to bio-security include the

setting up of isolation facilities (for incoming cattle, and for reactors and IRs), and a

whole range of physical measures intended to prevent, or greatly reduce, cross-

contamination of cattle feeding areas by wildlife. Many of these are more likely to

have been introduced after the bTB breakdown; and the measures include fencing off

known wildlife areas, double-fencing farm boundaries, wildlife-proofing silage

storage, feed areas, feed and water troughs.

While some of these changes come either at zero cost (or at costs not readily

identified), such as operating a closed herd, others involve very deliberate and

substantial expenditures, such as double-fencing farm boundaries. Major changes to

the layouts of buildings and facilities, and raising the heights of feed and water

troughs, were amongst the most expensive items on the sample farms.

The use of compensation funds received for a bTB breakdown

The issue of the role of farmers’ own insurance against the potential losses involved

with a bTB breakdown are discussed in Chapter 3. In considering the range of

economic effects, however, both positive (beneficial) and negative (deleterious) it is

important to understand the ways in which incoming funds, whether from the farm’s

insurer or from Government, are typically used within the business. Table 4.13

provides evidence of this for the sample farms over the preceding ten years, although

clearly this pattern may not necessarily hold for the future. The main conclusion is

that the funds are dominantly reinvested in the farm business, with only 3% of

respondents admitting to using some funds for personal drawings. While over half

used funds for replacing the cattle lost, where farm policy is to operate a closed herd

this use of funds is not possible. Clearly an important use of funds received has been

to reduce borrowings, and this is corroborated by the analysis of FBS data earlier in

this chapter, which identified a reduction in loans following the receipt of



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Table 4.13 Use of funds received for compensation/insurance

Replacing livestock lost because of the bTB breakdown 53%

Reducing farm borrowings 20%

Investing in business assets (land, buildings, etc., machinery) 7%

Making other changes to the business 3%

Increased drawings from the business (e.g. holiday) 3%

Cumulative amounts not significant 8%

Other 26%

Note: more than one may apply for a given farm (‘all that apply’)

compensation. It is likely that this group of farms includes both those where

borrowings may have been seen to be too high prior to the bTB breakdown, and those

where an ex post assessment has suggested that the prudent level of borrowing has

been lowered because of perceptions of higher risks of a further breakdown.

Farmers’ perceptions of risk are considered in Chapter 6.

The farm-level impacts of livestock movement restrictions

The imposition of movement restrictions following the identification of a bTB reactor

has long been identified as having a potentially very significant effect on the

operation of a farm. These effects go far beyond the immediate implications for day-

to-day management, because they can seriously affect the farming system (see the

discussion of this issue in Chapter 2; and Temple and Tuer, 2001). The study

findings on this issue are summarised in Table 4.14, which shows both the proportion

of farms identifying the individual effect and the aggregate assessment of the

financial significance of the effect. These data need little elaboration but it is useful

to point to a number of important findings:

• First, it is clear that none of the identified impacts are felt across all, or nearly

all, farms; this in itself implies a considerable diversity of impacts at farm level.

• Most of the most widely experienced impacts are directly related to the feeding of the

cattle, and it seems fairly clear that this is a consequence of being unable to sell cattle

when planned.



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Table 4.14 The farm-level impacts and financial importance of movement

restrictions

Financial importance (scale 1 to 5)

Impacts on cattle numbers % of

farms 1 2 3 4 5

Unable to sell calves when planned 52% 0% 14% 12% 16% 58%

Unable to sell store cattle when planned 54% 3% 8% 8% 18% 64%

Unable to sell (young) breeding cattle

when planned

27% 0% 0% 16% 5% 79%

Unable to sell cull breeding cattle when

planned

26% 3% 19% 46% 14% 19%

Unable to sell finished cattle when

planned

15% 0% 0% 5% 33% 62%

Unable to buy calves when planned 16% 9% 9% 13% 17% 52%

Unable to buy store cattle when planned 10% 0% 7% 14% 36% 43%

Unable to buy replacement breeding

cattle when planned

26% 3% 5% 0% 27% 65%

Impacts on feed costs

Additional keep purchased 29% 2% 12% 20% 27% 39%

Additional fodder purchased 44% 3% 13% 22% 33% 29%

Additional concentrate feed purchased 61% 5% 13% 13% 26% 43%

Less fodder purchased 1% 0% 100% 0% 0% 0%

Less concentrate feed purchased 4% 17% 0% 33% 50% 0%

Impacts on other costs

Higher labour costs (paid or unpaid) 58% 10% 14% 20% 30% 25%

Higher machinery/vehicle costs (e.g.

transport of feed, checking animals etc.)

32% 4% 29% 36% 11% 20%

Higher ‘other livestock costs’ (e.g. vet

and med costs, livestock sundries, etc.)

40% 11% 21% 40% 9% 19%

Higher overheads (e.g. electricity, water,

buildings, etc.)

31% 18% 25% 20% 16% 20%

Higher interest payments (i.e. because of

poorer cash flow)

30% 0% 0% 14% 19% 65%

Lower labour costs (paid or unpaid) 2% 0% 0% 67% 33% 0%

Lower machinery/vehicle costs (e.g.

transport of feed, checking animals etc.)

1% 0% 100% 0% 0% 0%

Lower ‘other livestock costs’ (e.g. vet

and med costs, livestock sundries, etc.)

1% 0% 100% 0% 0% 0%

Other 5% 0% 0% 14% 0% 29%

Note: The financial impacts are scored 1 = least, 5 = greatest



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• Significant proportions of respondents reported potentially serious effects on their

farming system, including both selling and purchasing cattle (irrespective of age,

depending on system)

• Adverse financial impacts are also generated by higher costs across a wide range of

resources: labour, vehicle and machinery, other livestock costs, overheads, and interest

payments.

• In terms of the ranking of these effects by their financial importance within the

business, the most widely cited for the heaviest impact (score = 5) are additional feed

costs of all categories for the additional animals

• A very high proportion of farmers’ rated higher interest charges as a ‘5’, probably an

almost inevitable result of the poorer cash flows resulting from disrupted selling (and

purchasing) patterns.

• The financial impacts of changes in the wide range of other costs were typically rated

rather lower, ‘3’ or ‘4’ being very common, although for a substantial minority of

farms changes in these costs can be very significant.

The effects of livestock movement restrictions, though clearly a vital tool in the

disease management strategy, can in some instances have far-reaching implications;

and this is particularly likely where the farm concerned is about to enter a period of

strategic change. Case Study D illustrates one such example.

Case Study D – dairying This dairy farm had not experienced a bTB breakdown until early 2007, when one cow went down with bTB. Even though it was only one cow, the experience has caused a lot of stress, additional costs, adversely affecting the farm’s income and has changed some of the farmers’ plans. At the time of the bTB breakdown, the farm was already going through a period of change with new plans for the business at a fairly advanced stage. The farmers had arranged to sell the dairy herd completely, and were planning to set up a new enterprise buying and rearing dairy young stock for sale as down-calving dairy replacements. Due to the farm being under bTB restrictions since the breakdown, they have not been able to start selling the young stock leaving the farmer with no income since January, the dairy herd having been sold. Consequently, the farmer himself has had to start relief milking to bring in an income. This was not part of their original plan.

This part of the study identifies the nature, incidence and perceived financial

importance of the full range of effects felt at farm level under cattle movement

restrictions. The emerging picture is one of very considerable disruption to the



99

effective operation of the farm business, although the impacts and their financial

significance vary widely from farm to farm. While most of the impacts are negative

in their effect on business profitability, in a few cases there are mitigating

circumstances which, to a small extent, offset the negative. These findings need to be

seen in the light of research findings of the impact of a bTB breakdown on human

health, which are discussed in Chapter 5.

Other farm-level effects of a bTB breakdown

One of the commonest problems for farmers with breeding herds, whether dairy or

beef, is the loss of breeding stock in a bTb breakdown. Where this is a matter on two

or three cows or heifers, say, on most farms this would not have very significant

longer-term implications. But where the numbers (or proportions) taken are higher,

for example involving both the current herd and replacement breeding stock, or where

there are successive losses of breeding stock, the cumulative effects can begin to have

a real impact on the productive capacity of the cattle enterprise.

On this sample of farms, nearly half (45%) had purchased breeding stock specifically

to replace stock lost in a bTB breakdown, while half reported being short of breeding

stock nevertheless. Many farmers operate a closed herd (itself a recognised aspect of

bio-security) often for animal health reasons, although factors associated with

pedigree or breeding policy may also be involved. Where replacements are

purchased, however, there can be a number of wider implications as Table 4.15

shows.

Just over a third of the surveyed farmers reported an adverse effect on herd quality,

nearly a quarter inadvertently introduced other diseases into the herd, and up to a fifth

ended up with adverse effects on the seasonality of production; this was more marked

with beef than dairy herds, perhaps because many dairy herds now operate a de facto

all-year-round calving pattern. Other common problems associated with bringing in

purchased herd replacements are an overall reduction in milk yields or quality and,

associated with this and perhaps more evident on dairy herds, a disruption to herd

equanimity. In contrast, small numbers reported beneficial effects, e.g. improvements

in the quality of the breeding herd.



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Table 4.15 Wider implications of purchasing replacement breeding stock

Adverse changes to the seasonality of milk production 14%

Adverse changes to the seasonality of beef production 20%

Adverse impact on the quality of the breeding herd 35%

Accidental introduction of other diseases into the herd 23%

Loss of pedigree status 1%

Disruption to herd equanimity (e.g. more bullying or victimisation) 10%

Overall reduction in milk yield or quality 14%

Beneficial changes to the seasonality of milk production 3%

Beneficial changes to the seasonality of beef production 0%

Beneficial impact on the quality of the breeding herd 7%

Overall improvement in milk yield or quality 1%

Other 16%

Note: more than one effect may have been recorded for any one farm (‘all that apply’)

It might be expected that the loss of cattle and, perhaps more importantly, the

increased uncertainty following a bTb breakdown might shape business development

plans. In fact, about one in three (36%) reported this and the effects on these farms

are set out in Table 4.16.

Table 4.16 Significant effects of a bTB breakdown on farm business development plans

Previously planned expansion did not take place: % of farms

reporting this

effect

- expanded herd size 46%

- upgrade quality of the breeding herd 39%

- introduce a new cattle enterprise (please specify) 20%

- investment in new buildings and infra-structure 24%

- investment in additional land 7%

- investment in superior machinery stock 2%

- plan to take on new tenanted land (e.g. under FBT) 6%

- other 40%

Note: these responses relate to the 36% of the total sample reporting these effects



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Other effects of a bTB breakdown were explored, in particular the effects on

entitlements to the Single Payment historic element: just 14% had experienced this.

Case Study E illustrates one example of this effect.

Case Study E - dairying On this farm, one spring day some four years ago, the entire dairy herd was taken, including the bull, as every animal except one tested positive to bTB. This was understandably a huge shock, not least because the farm had never experienced problems with bTB before. The compensation paid at that time was based on valuation and initially appeared very reasonable. However, it only paid for the cows that were taken, making no allowance either for the calves they were carrying or the milk they would have continued to produce. The farm therefore experienced a total loss of income for a year since there was no monthly milk cheque and no calves to sell. The herd was taken in late March and the farm was of course not producing any milk from that date. However, three weeks later it was discovered that, in consequence, the farm’s non-production meant that the milk quota was forfeited and could not even be leased out but must be sold. Had this been understood in advance, some milk could have been bought from a shop in order to be sold and this detrimental rule therefore avoided. The farm’s milk quota sold early in the quota year, at a time when the market was flooded with milk quota from other farmers who were not producing milk (and perhaps had been leasing their quota out for years) and realised a much lower price than would have been obtained later on. By the following year, when the farm might have been in a position to start milking again, the market value of milk quota had increased dramatically. This factor, together with the age of the farmer and the condition of the milking parlour (which needed refurbishment) meant that re-starting the dairy enterprise was not a viable option. Moreover, 2004/2005 was the base year upon which the Single Payment historic element calculation was made. As a result, and despite having milked cows for 33 years, the decision was made that no dairying element should be included in the farm’s entitlement. Protracted discussions with Defra followed, and eventually the farm was awarded just a third of the historic dairying value. Overall, this farm business suffered a very significant economic loss as a result of the bTB breakdown. Defra argued that not starting milking again was a commercial decision and not a “force majeure” case. The farmer is anxious to stress that, whilst the officials dealing with the implementation of rules appeared to lack sympathy, the vets who came out to the farm were, without exception, helpful and understanding.

On the sample farms, at least, there appeared to be little if any effect on non-

agricultural enterprises. In terms of actions taken to mitigate the impacts, the

commonest response was to arrange an increase in the back overdraft or to take out a

loan, to deal with the adverse cash flow effects (28%), followed by cancelling or

postponing investment or expansion plans. One in ten initiated some form of

business diversification, while just over half (53%) had not made such changes.



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Expected future impacts of the bTb breakdown

Finally, the interview survey respondents were asked about the expected future

impacts of the bTB breakdown already experienced, that is, their responses were in

relation to on-going effects from the past bTB event rather than being related to

possible future breakdowns. Just under four out of ten (39%) expected some on-

going effects and their responses are set out in Table 4.17.

Nearly four out of ten respondents (38%) were still needing to replace breeding stock

lost as a consequence of the breakdown, a finding that suggests that, for farms with a

breeding unit, whether dairy or beef, it is the reduction in productive capacity

resulting from the loss of breeding stock (and breeding replacements) that is

responsible for the most serious long-term economic effect. Following on from this,

longer-term effects encompass the slowdown in business growth and development, as

herd expansion (28%), investment in new and improved buildings (17%) and

upgrading herd quality are all delayed, sometimes far beyond the initial breakdown.

Case Study F shows how these long-term effects can take years to work through.

Case Study F – suckler beef On this farm the beef suckler herd suffered a bTB breakdown in 1999 losing approximately 30 cows and 18 calves. The farmer estimates he lost the best 50% of his herd, in terms of genetic quality. In subsequent tests more animals were taken each time and the farm did not become clear of bTB for six years, during which the farmer was unable to sell stores or calves and everything had to be ‘finished’. The farm was not set up as a finishing unit so the cattle buildings had to be altered substantially to accommodate the older cattle. Moreover, initially the farmer was unable to buy replacement cows or calves, although he was allowed to later on. Consequently it took some time before the farm had anything at all to sell again. He admits to being rather heavily borrowed at the time of the bTB breakdown, but nevertheless until that happened the business was servicing these borrowings quite adequately. The farmer estimates his losses at about £20 thousand per year and now, eight years on, the business is only just beginning to recover; even so, the farmer has yet to see a profit in any year since the breakdown.



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Table 4.17 On-going problems: anticipated significant future impacts from the past bTB breakdown

Previously planned expansion now unlikely to happen/seriously delayed:








- other (please specify) 2%

Still needing to replace lost breeding stock (purchased or home-reared) 38%

Adverse effect on herd expansion plans (e.g. because of smaller breeding herd) 20%

Adverse effect on business growth through:

- reduced cash flow (e.g. from smaller herd) 30%

- increased uncertainty about cattle production 30%


Positive effect on business growth because of compensation payments received 0%

Expect to change farming system:

- reduced cattle enterprise 13%

- get rid of cattle enterprise 13%

- expand cattle enterprise 5%

- expand other enterprise(s) (please specify) 5%

- reduce other enterprise(s) (please specify) 3%

- get rid of other enterprise(s) (please specify) 0%

Expect to reduce the size of farm business 3%

Expect to give up tenanted land 5%

Expect to sell land 3%

Expect to get out of farming 12%

Other 20%

Note: responses include multiple effects on any one farm (‘all that apply’)



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These wider long-term effects include on-going adverse effects on cash flow (30%)

and, crucially, increased uncertainty about the reliability of cattle production (30%).

Expectations about the future were so dire for some that they were either going to

make radical changes to their farming system (13% ‘reduce scale of cattle enterprise’

and 13% ‘get rid of cattle enterprise’) or even get out of farming altogether (12%).

Looking to the future, ‘bTB driver’ farms were more likely than others in expecting

to cancel or postpone investment in livestock, premises or equipment.

Summary of responses from the postal survey

The postal survey utilising the GHQ-12 approach to assessing psychiatric morbidity

is reported in detail in Chapter 5, and the purpose here is simply to highlight the few

pointers to longer-term economic effects that emerge from that study. Three in

particular bear repetition here, because of the obvious linkage between the adverse

economic effects on the farm family’s livelihood and the levels of stress recorded for

the people concerned:

• Overall, the highest stress levels are seen on farms which have been under

livestock movement restrictions for a long period.

• However, on dairy farms, where people are generally more stressed than on

beef farms, the greatest stressor is the loss of a large number of cattle.

• For farmers, a bTB breakdown causes significant stress: this is highest for

dairy farmers, especially where they have lost a large number of cattle; while

for both trading beef and suckler beef farmers, a long period under restriction

is the greater stressor.

Taken together, the findings form these very different parts of the overall research

programme point to a clear linkage between the longer-term economic impacts of a

bTB breakdown on the operation and, ultimately, the performance of the farm

business on the one hand, and the implications of these problems for the people

whose livelihoods are affected – the farmers, their spouses and, to some degree, the

other people who work on the farm. These economic effects are not uniformly

experienced, since their impact on the individual farm depends on the farming

system, the scale and timing of the breakdown, and the stage of business development

at which that business is when the breakdown occurs. An example of the complexity



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and persistence of the consequential effects over the longer term, following a bTB

breakdown, is provided by Case Study G.

Case Study G - dairying This farmer lost all his livestock to FMD in 2001 and re-stocked with dairy cows only. During the first whole farm bTB test in 2002 he had several reactors and this trend has continued over the past five years; and the farm has remained constantly closed during the whole of this period. His experience has been an average of eight milking cows being taken with every 60 day test and between 10-15 beef animals also reacting during a calendar year. At one test, 18 milking cows were taken. This has been having a dramatic and ongoing effect on the volume of milk sold, which in turn has had a knock-on effect on milk price. Most of the dairy cows taken have also been either heavily in-calf, newly calved or fresh milkers and so the economic effect has been at its maximum: the farm kept these cows through the calving season only to lose them before they were producing any milk. This farmer is allowed to purchase cattle to replace those lost to bTB in order to maintain his herd size When the compensation levels were based upon independent valuation this farmer believes he was receiving fair price for his pedigree stock. He has invested heavily in high yielding pedigree cows since 2002, with some cattle being worth tens of thousands of pounds. The compensation paid adequately covered the cost of replacement with similar quality animals. However, since the compensation grid was introduced in 2006, this farmer has suffered serious financial difficulties. With on average eight cows going every 60 days and only receiving in the region of £1200 for pedigree cattle he has been forced to pay the surplus himself to maintain his pedigree status and good bloodlines. This, in addition to losing up to 200 litres of milk per cow per day has had a serious effect on the farm’s cash flow and borrowings. In total over the past five years, this 160 cow dairy farm has lost in excess of 210 cattle, but only one has had visible lesions at post mortem. This fact only adds to the farmer’s anguish. Another major worry is the uncertain future of the farm’s pedigree breeding stock sales: if and when the farm becomes clean, will its bTB history make a resumption of sales impossible? Further, if the farmer decided he wanted to get out of farming, there is no route to do this apart from slowly reducing cow numbers as they are taken as reactors through not replacing them, and this would also represent a significant loss of income compared with a normal sale.

Conclusions

In terms of the economic effects of a bTB breakdown within a farm business, the

study gives rise to the following main conclusions:

• At Great Britain level, most cattle farms (more than 60%) either have never had

a bTB breakdown or have been clear for many years; and two thirds of those

which have had a breakdown have been only lightly affected.



106

• The proportion of all bTB breakdowns resulting in more serious farm-level

effects is small but not insignificant: more than a quarter lose significant

numbers of cattle and a further 4% of cases are under livestock movement

restrictions for a long period.

• The proportions of farms with bTB breakdowns, both light and heavier, are

substantially greater in the ‘bTB endemic’ areas.

• Most bTB breakdowns would not be expected to result in longer-term effects on

the farm business - identifiable longer-term economic effects are likely to be

seen on perhaps 10 – 15% of all affected farms.

• The stakeholder consultation identified reduced farm profitability, higher costs,

adverse impacts on calving patterns and inhibited business development. More

widely, loss of confidence and adverse effects on the environment were cited.

• Key findings from the FBS study of dairy farms are:

o Differences in cash flow and FFI between bTB farms and the control

group increased over time, with bTB-affected farms falling behind the

others.

o In terms of NFI, the two bTB groups had a broadly similar income

position to the control group but for M&II the trends are more volatile.

o Overall, in most years and for most income indicators, bTB farms

performed less well than the control group, although the differences

are not large in most years.

o The data suggest bTB farms generally have a higher level of off-farm

income.

o Starting from a similar level, average milk yield on bTB farms fell

behind the steady increase in yields seen in the control group, with a 6-

8% gap by 2006/07.

o The evidence is that farmers (and their spouses) work consistently

longer hours on bTB farms, supporting other findings that a bTB

breakdown can cause considerable additional work over a long period.

o Total cattle numbers increased markedly on ‘long period of restriction’

bTB farms towards the end of the period; and these farms also saw an

increase in their farmed area.

• Key findings from the FBS study of suckler beef farms are:



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o The control group (all suckler beef farms) experienced a steady decline

in profitability over the five years from 2002/03.

o Farm incomes on bTB farms are more variable than for the control

sample; and incomes on ‘long period of restriction’ farms are lower

than for ‘all bTB farms’.

o Incomes data are variable: for cash incomes a significantly lower level

of economic performance on the bTB-affected farms, but there is no

consistent pattern for NFI or M&II.

o Overall, farms with a bTB breakdown performed less well in most

years, those under movement restrictions being least profitable. The

bTB farms exhibit much greater annual variability in incomes.

o In most years investment in buildings and net asset purchases was

substantially lower on bTB farms than on the control group of farms.

o The labour input of ‘farmer plus spouse’ was generally lower on bTB

farms than on others, throughout the five year period studied.

o With a smaller average farm size, bTB-affected farms have a

significantly higher stocking rate, consistent with a general increase in

cattle numbers while under movement restrictions.

• The farm interview survey established that strategic business decisions by

farmers typically take a range of factors into account, of which bTB may be

one. Just over a third of the surveyed farms listed bTB as a driver of a specific

business change, and in these cases it was dominantly listed as either the first

(80%) or second (16%) driver.

• Similarly, in terms of business threats, bTB was seen very much as one threat

among a number: it was the third most commonly listed main threat and was

ranked much the same for second and third level threats.

• The main farm interview survey findings are as follows:

o Just over half of the sample farms had made improvements to bio-

security during the previous ten years; some of these had required very

substantial investment, particularly for fencing and physical

improvements to feed storage and feeding areas.

o Funds received as compensation for bTB losses, from government or

insurer, were almost all used within the farm business; in addition to

replacing stock, a common use was to reduce external borrowings.



108

o Movement restrictions give rise to significant adverse effects, but these

vary greatly. Being unable to sell cattle when planned generates many

of the specific impacts, especially extra feeding costs; also major

disruption of the farming system is common. Adverse financial impacts

are widespread, especially higher interest charges.

o The loss of breeding stock appears a particularly serious consequence

of a bTB breakdown; and if replacements are purchased these often

result in other problems: adverse effects on calving patterns, the

introduction of ‘new’ diseases, a decrease in herd equanimity, a

general fall in yields or milk quality (dairy herds).

o Many farmers expected future consequences from the recorded bTB

breakdown(s). These included needing to replace breeding stock and a

slowdown in business growth and development, sometimes far beyond

the initial breakdown.

• Some of the postal survey findings are related to economic effects. The highest

stress levels are seen on farms which have been under livestock movement

restrictions for a long period; and people on dairy farms are generally more

stressed by a bTB breakdown than those on beef farms; the loss of a large

number of cattle, and being subject to movement restrictions for a long time, are

both notable stressors through their implications for business viability.



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5 UNDERSTANDING HEALTH AND SOCIAL EFFECTS

Introduction

The background to this part of the study has been fairly extensively set out in

Chapters 1 and 2 and the purpose here is briefly to set our study, and findings, in its

wider context. Although specific evidence of the implications for human health of

the on-going problem of bTB in many livestock producing areas of the country is

sparse, from the outset it seemed possible that this might largely be because previous

bTB-specific studies had concentrated on short-term impacts (NAO, 2003; Bennett,

2004; Sheppard and Turner, 2005). Indeed, during the last couple of decades or so,

there has been a growing recognition, fuelled by a small but nonetheless significant

number of studies both in the UK and further afield, that have identified the multiple

problems associated with the farming industry as significant causes of stress, and

even suicide, in farmers and the farming workforce. In particular, over recent years

several studies have explored the linkages between farming and adverse impacts on

the mental health and equanimity of farmers, members of farm families and farm

workers (notably Parry, et al, 2005; see also Lobley, et al, 2004).

As the detailed review in Chapter 2 makes clear, much of the UK literature is

concerned with the human consequences of the 2001 FMD epidemic and the long-

running, and continuing, BSE epidemic. Of particular relevance to the present

research, these studies have established that crises with livestock can be amongst the

most stressful events farmers and the farm workforce have to deal with; and that in

this sense livestock farmers whose cattle herds suffer a bTB breakdown are subject to

greater exposure to stressors. Previous research has also shown that livestock crises

are likely to affect not only farmers, but their spouses, any adult children living at

home and farm workers as well.

A number of factors have been identified as driving this affect, including the tight

links human beings often develop when they raise and care for animals, sometimes

over a period of many years, when the untimely loss of such animals can be



110

profoundly disturbing. Parry et al (2005) conclude that livestock crises ‘provided the

most acute and visible causes of stress’; and further noted that these effects could be

exacerbated where the animals concerned formed part of long blood-lines, where the

animal represents the culmination of an extended breeding programme. It is

important to understand here that although this clearly applies to pedigree livestock,

such connections may also be manifest in the case of the breeders of non-pedigree

livestock: the affect arises because of personal involvement with the ‘family’ of the

livestock concerned, rather than because of the animal’s precise status on a pedigree

register. A finding of particular relevance to the present study was that although the

on-going bTB epidemic had received much less attention from the media (than FMD

and BSE) it nevertheless ‘encompassed a very real fear for many interviewees’: while

less dramatic, its steady attrition of the UK’s cattle population and its lack of

symptoms which made early diagnosis difficult is the cause of much distress (Parry,

et al, 2005).

Moreover, the bTB epidemic has continued apace through a period of considerable

economic and structural change for agriculture, the causes of which have been well-

documented (see, for example, Curry Commission, 2002). As this study’s review of

previous research has highlighted, there are many causes of stress for farmers; and

those associated with the consequences of a livestock disease epidemic, both direct

and indirect, represent further significant sources of stress on top of those common to

all farmers. Although there is very limited reference to bTB as a cause of farming

stress, it was clearly appropriate for this research to explore the issue properly.

The specific research objective was agreed as follows: ‘to provide a sound evidence

base for a better understanding of the social and human health effects of a bTB

breakdown at farm-level, including both the range and the typical incidence of such

adverse impacts’. This has been addressed in a number of ways. The farmers’

interview survey included a few questions which looked at aspects of whether, and in

what ways, the bTB breakdown had affected the daily lives of the farm family and

any farm staff. The possibility of a bTB breakdown causing stress was flagged by a

number of respondents to the stakeholders’ consultation, and this issue was

investigated through a postal survey using the established General Heath

Questionnaire (GHQ-12), mailed to a sample which included farmers whose



111

businesses had recorded a bTB breakdown and a control sample of otherwise similar

farmers whose businesses had not had a bTB breakdown. Finally, a number of case

studies of GP practices situated in several of the known bTB ‘hotspot’ areas explored

the issue of the effects of a bTB breakdown on physical and mental health of farm

families and farm workers.

The stakeholder consultation: pointers to farming stress

Stakeholders with a direct interest

The majority of respondents highlighted the increased stress farmers feel when

directly affected by a bTB breakdown. Even when one outbreak clears up there is

still the worry that it will reoccur. Many identified bTB as being the ‘unknown

quantity’ with the on-going potential to cause stress: cattle farmers simply do not

know when something may happen and, in any case, feel powerless to prevent it. In a

number of cases, it was reported, this has led to high blood pressure, nervous

breakdown, strokes and even suicide. In some cases it appears to have been a

contributory factor in marriage breakdown.

Several stakeholder respondents identified the increased pressure on family members

to help out (some with family living near who may not normally work on the farm)

which is sometimes associated with a bTB breakdown. A longer term effect, it was

felt, is that younger family members may be discouraged from going into farming,

although this may also be a consequence of what is seen as political inaction. Within

the rural community, some stakeholders identified the increased workloads involved

in looking after more animals (a common consequence of the imposition of

movement restrictions following a bTB breakdown) can lead to reluctance, or

inability, to support community events.

Furthermore, it was reported that relationships between farmers and their neighbours

can become tense, particularly if it is perceived that one neighbour is not doing

enough to prevent the spread of the infection. One farmer stated that he felt very

guilty that when his farm was found to be infected with bTB as all the neighbouring

farms had to be tested, with all that implies for those farmers and their families. Also,

where a farmer has neighbours who come from a more urban background, or where

neighbours are very concerned with badger conservation, this can lead to fraught



112

relationships, where they have diametrically opposing views. Clearly, the potential

for increased levels of stress following a bTB breakdown is generated by a number of

possible causes.

Stakeholders with an indirect interest

The effects highlighted by this group of stakeholders also included stress, in this case

identifying that associated with not being able to market animals at the appropriate

time, and generally adverse effects on both the mental health and physical well being

of farmers. It was also noted that where bankruptcy had occurred, by implication as a

result of a bTB breakdown, there could be more indirect effects on social well-being:

for example, a case where the farmers’ children felt unable to go on school trips.

Some of these stakeholders also highlighted the fact that, all too often, some local

community members did not understand the pressures on farm businesses, of which a

bTB breakdown often represented a considerable escalation.

Table 5.1 Adverse effects of a bTB breakdown on human health and well-being: summary of the stakeholders’ consultation findings

Stress

Very severe physical problems such as heart trouble, stroke

and suicide

Health effects

No time off or holidays

Marital breakdown

Deterioration of relationships with neighbours

Lack of participation in community events

Social effects

Increased dependence on family members for help leads to

extra family hardship

Loss of confidence in the farming industry and therefore no

interest in succession from family members

Loss of confidence in the industry

Lack of confidence in authority on policy making

Effects on the environment and landscapes

Other human effects

Change in attitudes to wildlife

Taken together the stakeholders’ responses identified quite a wide range of possible

adverse effects on human health and well-being in the farming population arising



113

from, or exacerbated by, a bTB breakdown. These are summarised in Table 5.1 and

these findings informed the later stages of the study.

The postal survey: exploring the human impacts of a bTB breakdown

Background to the GHQ-12 study

The principal empirical part of the study concerned with the impacts of a bTB

breakdown on the social well-being and mental health of the farm family and farm

staff utilised the nferNELSON GHQ-12 questionnaire, administered through a postal

survey to a sample of farmers drawn from the VetNet database. It is widely

recognised that the livestock farming communities are likely to come under

considerable stress as a result of a bTB outbreak, and this was one of the key findings

of the stakeholder consultation; this postal study aimed to investigate the

psychological impact of a bTB outbreak on farmers and their families. Our team

included a professional psychotherapist8 who has successfully used this questionnaire

in a study of farming stress (Watts et al, 2003) and in which the method of

questionnaire delivery was postal.

Because the farming community is already under stress from a number of factors, it

was decided to compare the measurement of stress on farmers and their families who

have experienced different severities of a bTB outbreak with a control group of

livestock farmers who have not experienced a bTB outbreak. Our target population

included farmers, their spouses, their adult children (over 18 years of age) living at

home, and their regular farm employees. However, only the farmer could be

identified from the VetNet database, and so three copies of the questionnaire were

enclosed (for completion by the farmer, and two of the following: their spouse, adult

children and staff) and the covering letter invited requests for more forms as

necessary.

The target population was farms/farmers which have experienced a bTB breakdown,

together with a small control group of non-bTB livestock farmers. It should be noted

that the use of this established questionnaire on social well-being and mental health

has the important benefit in that the results can be compared with the position for the

general morbidity of the adult population (for which the questionnaire is regularly

8 Consultant Nurse in Psychological Therapies, Somerset Partnership NHS and Social Care Trust



114

used). Our study also allowed comparison with a cohort of farmers on otherwise

similar farms but who have not suffered a bTB breakdown. Details of the sample

specification are given in Table 5.2.

Table 5.2 Postal survey using the GHQ-12: sample stratification

General description

of farm group Cattle taken

Length of movement

restrictions Clear since

A - Lightly affected 3 or less within last

10 years

< 6 months within last

10 years July 2005 or earlier

B - Long period of

movement restrictions Any number > 1 year No requirement

C - Large number of

cattle taken

> 10% of total cattle

numbers (total

numbers taken/total

numbers tested)

No requirement No requirement

D – no bTB

breakdown since July

1997

n.a. n.a. n.a.

The sample contained three bTB groups – ‘lightly affected’, ‘long period of

movement restriction’ and ‘large numbers of cattle taken’ – and a control group

which had not had a bTB breakdown. A certain amount of supplementary data

relating to the farm’s bTB history as recorded on the VetNet database was provided

for use later in the analyses. For each of these four groups the target sample was 250

farms, each of which was further split into equal numbers of dairy and beef farms;

thus the total sample comprised eight groups of 125 farms, the target being a sample

of 1,000 farms. In the event, there were insufficient farms for one sample (Group C-

Dairy) and the sample mailed comprised 976 farms. The sample was drawn from the

areas of England most affected by bTB (the West Country, the English-Welsh border

counties and parts of the North West, and Wales. The first survey mailing was on 10

March 2008, with two subsequent reminders being sent after three and two weeks

respectively.



115

Characteristics of the GHQ-12

The 12-item version of the General Health Questionnaire (GHQ-12) was selected as

the most appropriate tool for identifying psychiatric morbidity (Goldberg, 1972),

although longer versions exist. It was chosen because of its design as a self-

administered screening test aimed at detecting psychiatric disorders. It is designed to

be easy to administer, acceptable to respondents, fairly short and objective. The

questionnaire has been widely used in similar circumstances to identify differentials

across a varied population.

The questionnaire score gives an assessment of an individual’s position on an axis

from normality to undoubted illness and can be thought of as giving a probability

assessment of that individual being a psychiatric case having mental health problems.

Using the GHQ-12 to assess prevalence, a GHQ score of four or above is the usual

cut-off score for identifying a ‘case’, this level being taken as indicative of a

clinically significant mental health problem. The approach to scoring is discussed in

more detail in the following section.

The questionnaire focuses on breaks in normal function, rather than upon lifelong

traits. It consists of twelve questions concerning happiness, depression, anxiety, sleep

disturbance and ability to cope. The questionnaire concerns itself with two major

classes of phenomena: six of the items on the GHQ-12 deal with an inability to carry

out one’s normal ‘healthy’ functions (e.g. ‘playing a useful part in things’; ‘able to

enjoy day-to-day activities’), and the remaining six address the appearance of new

phenomena of a distressing nature (such as ‘losing sleep over worry’ or ‘thinking of

yourself as worthless’). The respondents are not asked how long he or she has

experienced each symptom and therefore the GHQ-12 should be understood to detect

essentially short term disorders.

One advantage of using the GHQ-12 is that it is widely used in national surveys and

in many other studies, so the results can be compared with those obtained elsewhere

from different populations. Of particular relevance here is that it has been used as a

measurement tool in studies measuring stress levels amongst the farming community

e.g. the impact of foot-and-mouth disease (Peck, et al, 2002; and Watts et al, 2002).



116

Questionnaire layout and method of scoring

Each item consists of a question asking whether the respondent has recently

experienced a particular symptom or item of behaviour on a scale ranging from ‘less

than usual’ to ‘much more than usual’. This four-point scale may be scored in two

ways: it can be treated either as a multiple-response scale (termed here the GHQ

score) or as a Likert scale (with weights assigned to each position), and these two

approaches are illustrated in Table 5.3.

Table 5.3 Illustrative example of alternative GHQ-12 scoring approaches

Column 1 Column 2 Column 3 Column 4

Question: Lost much

sleep over worry?

‘Not at all’ ‘No more than

usual’

‘Rather more

than usual’

‘Much more

than usual’

Likert score 0 1 2 3

GHQ score 0 0 1 1

The usual method of scoring is in fact to use a bimodal response scale, termed ‘GHQ’

after the name of the questionnaire, as shown in the above example. It is not only a

very simple method of scoring, but has the advantage of eliminating any errors due to

‘end-users’ and ‘middle-users’ – the answers are treated as either negative or positive.

The usual way of scoring the GHQ when it is used for case identification is the ‘GHQ

method’ of 0-0-1-1. To interpret the scores a decision needs to made on the total

threshold score which differentiates between ‘cases’ and ‘non-cases’ and a

straightforward comparison made between the proportion of those with scores above

a certain threshold in each sample. In the Health Surveys for England, Scotland and

Wales a score of four or more is used as the threshold to identify respondents with

possible psychiatric disorder and this is referred to as a ‘high GHQ score’; and ths

study has adopted the same threshold.

The GHQ-12 survey results – the sample

As detailed above, a total of 976 farms were drawn for the postal survey; and from

these there were 609 respondents, representing a total of 468 farms, who returned a

completed GHQ-12 questionnaire. The response rate is detailed in Table 5.4, which

also shows that quite a high number (77, or 7.9%) of the questionnaires were returned



117

‘not delivered’ for one reason or another; taking usable responses in relation to the

effective sample, the overall response rate was 52.1%.

Table 5.4 Postal survey response rate, by category of person

Response rate Number %

Initial mailing 976 100.0

Questionnaires returned undelivered 77 7.9

Effective sample 899 92.1

As % of effective sample

Overall response (farms) 468 52.1

Composition of responses %

Farmers 360 59.1

Spouses 142 23.3

Family (adult children living at home) 56 9.2

Farm workers 17 2.8

Unknown 34 5.6

Total usable responses received 609 100.0

Slightly more than half of the respondents were from beef farms and the remainder

from dairy farms. The farms which had been affected by a bTB outbreak were

categorised into the following types - farms lightly affected (27.09%), those which

had lost a large number of animals taken for slaughter (22.17%) and those which had

been subject to a long period of cattle movement restrictions (27.42%). There was

also a control group of farms which had not been affected by a bTB outbreak

(23.32%) (Table 5.5).

Table 5.5 Total postal survey responses by bTB category and farm type

Beef

farms

Dairy

farms Total %

No bTB outbreak 67 75 142 23.32%

Lightly affected 81 84 165 27.09%

Large number of animals taken 93 42 135 22.17%

Long period of restriction 77 90 167 27.42%

Total numbers 318 291 609

% of total responses 52.22% 47.78%



118

The number of beef and dairy farms in each type were fairly consistent with the

exception of those which had had a large number of animals taken for slaughter,

where the number of beef farms out-numbered the dairy farms in a ratio of

approximately 2:1; this broadly reflected the differential in the original sample drawn,

because there was a shortfall in the number of ‘Group C – Dairy’ farms. Table 5.6

summarises the response at farm level by farm type and bTB group.

Table 5.6 Postal survey response at farm level: farm type and bTB group

Type of farm

Group A

- Lightly

affected

Group B -

Long

period of

restriction

Group C -

Large

number

of

animals

taken

Group D

- No bTB

outbreak Totals

As % of

total

farms

Trading beef 30 31 36 19 116 29.1%

Beef suckler 22 18 28 25 93 23.3%

Dairy 51 59 27 53 190 47.6%

Total farms 103 108 91 97 399 100.0%

As % of total

farms 25.8% 27.1% 22.8% 24.3% 100.0%

It was one of the research aims for this survey to reach a cross-section of all

categories of the farming population potentially affected by a bTB breakdown:

farmers, their spouses, their adult children living at home and farm employees.

However, using the VetNet database as the sampling frame provided only the name

and address of the farmer, or the business. The covering letter for the survey was

addressed to the farmer, which contained a statement of the aim to include other

people associated with the holding, and an invitation to distribute the questionnaire to

the other people. This attempt to reach at least some of the other people was partially

successful, and sufficient to provide a wider perspective on the issues being

investigated. Of course, it is not possible to calculate response rates by category

because the total numbers of people in each category on the sample farms is not

known. While the majority of the returned questionnaires were completed by the

farmer (59%), just under a quarter were completed by the spouse (23%), but less than

one in ten by an adult child living at home (9%) and just 3% by paid workers. For a



119

small proportion of the questionnaires (6%) the identity of the respondent was

unknown.

Figure 5.1 Postal survey responses by category of respondent

Farmer

59%

Spouse

23%

Adult child living at home

9%

Paid worker

3%

Unknown

6%

In the initial mailing a total of three questionnaires were sent to the selected sample of

farms with three postage paid envelopes for return (to provide confidentiality), with

reminders being sent after three weeks and a further two weeks. This provided an

opportunity for up to three people from each farm to respond to the questionnaire, and

if more were needed respondents were invited to request further copies; this offer was

taken up in a few cases, although in the event no farm returned more than three

questionnaires. Analyses of the responses are presented in Tables 5.7, 5.8and 5.9.

A single response was received from the majority of farms (60.7%), two responses

from just over a quarter of the holdings (26.1%) and three responses from the

remainder (13.2%). The greater majority of single responses were from the farmer

(79.3%), and farmer and spouse combined formed nearly 82.7% of the sample of two

responses; of the remainder nearly 12% were equally either two farmers or the farmer

and an adult child. For the farms where three people responded by far the commonest

combination was from farmer, spouse and adult child living at home (43.4%). The

next commonest was from two farmers and one spouse (13.2%), followed by farmer,



120

spouse and paid worker (9.4%) and farmer and two adult children living at home

(7.6%).

Table 5.7 Detailed composition of postal survey respondents, by responses per farm

Responses

per farm Farmer Spouse

Adult

child

living

at

home

Paid

worker Unknown

Total

number

of

responses

Total

number

of

farms

As % of

total

responses

One 192 17 10 4 19 242 242 60.7%

Two 107 87 10 2 2 208 104 26.1%

Three 61 38 36 11 13 159 53 13.3%

Totals 360 142 56 17 34 609 399 100.0%

Table 5.8 Farms with two responses: analysis of responses by category

Category of two responses

Number

of farms

As % of

total

responses

Farmer & spouse 86 82.7%

Two farmers 6 5.8%

Farmer & adult child 6 5.8%

Farmer & paid worker 2 1.9%

Two adult children 2 1.9%

Farmer & unknown 1 1.0%

Spouse & unknown 1 1.0%

Total 104 100.0%



121

Table 5.9 Farms with three responses: analysis of responses by category

Category of three responses

Number

of farms

As % of

total

responses

Farmer, spouse & adult child 23 43.4%

Two farmers & one spouse 7 13.2%

Farmer, spouse & one paid worker 5 9.4%

Farmer & two adult children 4 7.6%

Farmer, spouse & one unknown 2 3.8%

Farmer, adult child & one unknown 2 3.8%

Farmer & two unknowns 2 3.8%

Three farmers 1 1.9%

Two farmers & an adult child 1 1.9%

Two farmers & one paid worker 1 1.9%

Farmer, adult child & paid worker 1 1.9%

Farmer, paid worker & unknown 1 1.9%

Spouse, adult child & unknown 1 1.9%

Three paid workers 1 1.9%

Three unknown 1 1.9%

Total 53 100.0%

The sizes of the cattle herds associated with respondents varied widely. The dairy

herds were largest, with a mean size of 178 head and median of 143 head,

demonstrating the skewness of the distribution of herd size with a smaller number of

very large herds pulling the central measure of the mean up some 30 head from the

median measure (Table 5.10). This is illustrated in Figure 5.2. Trading beef and

suckler beef herds had a similar mean but the median of 30 head for trading beef

compared to 47 head for suckler beef demonstrates that there are a larger number of

smaller cattle herds (of less than 50 head) on trading beef farms.



122

Table 5.10 Postal survey respondents: statistical analysis of cattle herds

Herd size Trading beef Suckler beef Dairy

Mean 78.29 75.46 178.20

Lower quartile 11 13 82

Median 30 47 143

Upper quartile 115 113 247

Maximum 624 464 640

Number of herds 116 94 184

Figure 5.2 Postal survey respondents: distribution by size and type of cattle herd

0

10

20

30

40

50

60

70

80

Less than 50 head 50 - 99 head 100 - 199 head 200 head and over

Trading beef farms

Suckler beef farms

Dairy farms

There is wide variability between the mean sizes of the herds according to the

category of bTB outbreak (as defined for the purposes of this research, see Table 5.2);

those farms which had a long period of restriction were the largest of all types of

farm, with highly significant differences in the means of 110 cattle for trading beef

and 88 for suckler beef from the control group, those farms with no bTB breakdown

(Table 5.11). Where a large number of animals had been taken following a bTB

outbreak, the herd size mean is significantly lower: some 69 cattle lower for dairy

herds and 44 for trading beef herds.



123

Table 5.11 Analysis of postal response: cattle herds by type and bTB category

Category of bTB outbreak

Trading

beef

mean

herd

size

Deviation

from

control

group

Suckler

beef

mean

herd

size

Deviation

from

control

group

Dairy

mean

herd

size

Deviation

from

control

group

No bTB outbreak (control

group) 57.53 0.00 49.20 0.00 189.38 0.00

Lightly affected 77.70 20.17 93.86 44.66* 139.88 -49.50*

Long period of restriction 167.10 109.57** 136.83 87.63** 227.10 37.72

Large number of animals

taken 13.28 -44.25** 44.32 -4.88 120.33 -69.05**

** p<0.01, * p<0.05

The period in years during which the farms in the sample had been affected by a bTB

breakdown was a defined part of the sample selection and the overall sample is bi-

modal regarding this feature (Table 5.12). The overall mean period for which this

sample of farms was under movement restrictions was 2.16 years, and the median

was much lower at less than one year (0.65 years). Nearly all of those defined as

‘lightly affected’ (96.12%) had been under movement restrictions for less than a year,

while all of those defined as under ‘long term restrictions’ had longer than one year

restriction period, over a third of these (36.11%) had longer than five years. The

majority of the farms (74.73%) where a ‘large number of animals were taken’ had

been under restriction for less than one year, with less than 5% experiencing more

than five years restriction (4.40%).

Table 5.12 Postal survey: years under movement restriction, mean and median herd sizes compared

Number of

years

Lightly

affected

Long

period of

restriction

Large

number of

animals

taken

Overall

(all farms except

‘no bTB’ control

group)

Mean 0.48 4.63 1.12 2.16

Median 0.32 4.14 0.52 0.65



124

Table 5.13 Periods of cattle movement restrictions, by farm type

Dairy

Suckler

beef

Trading

beef Total

Less than 3

months 17 14 14 45

3 - 6 months 41 23 32 96

6 - 12 months 11 5 10 26

1 - 2 years 10 6 8 24

2 - 5 years 35 15 16 66

5 - 10 years 23 5 17 45

Total 137 68 97 302

Table 5.14 Years under movement restriction, by farm type

Number of years Dairy

Suckler

beef

Trading

beef Overall

Mean 2.31 1.65 2.30 2.16

Median 0.98 0.46 0.57 0.73

Number of farms 137 68 97 302

Figure 5.3 Postal survey farms: movement restrictions by herd type

0

5

10

15

20

25

30

35

40

45

Less than 3 months 3 - 6 months 6 - 12 months 1 - 2 years 2 - 5 years 5 - 10 years

Dairy

Suckler beef

Trading beef



125

GHQ score results

A GHQ score of four or above is usually considered the threshold for differentiating

between ‘cases’ and ‘non-cases’ of psychiatric morbidity. The results were analysed

first by the responses of all of the respondents in relation to the category of bTB

outbreak. There were no significant differences from the control group (no bTB

outbreak) for either the lightly affected group or the group which had had a large

number of animals taken. However there was a statistically significant difference for

the group of farms which had been under cattle movement restrictions for a long

period of time because of successive bTB testing failures (Table 5.15). This finding

points to significantly higher stress levels for those people (farmers, their spouses,

their adult children living at home and their farm staff) closely associated with a farm

which is under a long period of movement restrictions, defined here as being of more

than a year’s duration.

Table 5.15 Percentage of respondents scoring a ‘high GHQ score’ by category of bTB breakdown


Proportion of

respondents

with a high

GHQ score

Deviation

from control

group


group) 40.14% 0%

Lightly affected 46.06% 5.92%

Long term of restriction 56.29%** 16.15%

Large number of animals taken 47.41% 7.27%

** p<0.01

An examination of the results by the type of cattle enterprise showed some important

differences between ‘cases’ and ‘non-cases’ of psychiatric morbidity in comparison to

the control groups. In particular, results for the people on dairy farms showed the

greatest deviance from those of the dairy control group, although the pattern of high

GHQ scores differed slightly from the ‘all farms’ analysis in Table 5.15. As Table

5.16 shows, the incidence of psychiatric morbidity on dairy farms increased

progressively from (a) those on lightly affected farms, through (b) those on farms



126

under long a period of restriction but peaked on (c) those on farms where a large

number of their animals were taken for slaughter.

In contrast, there was a quite different pattern evident when respondents from trading

beef farms were compared to their control group (Table 5.16). All groups recorded

rates of psychiatric morbidity which were lower than the control group, but this was

partly because of the very high rate indicated for the control group (note that the size

of the control group for this farm type was relatively small –see Table 5.6). Due to

sample size effects, none of these findings are statistically significant but it would

appear that a long period of restriction is associated with the highest stress levels for

people on these farms. In particular, it will be noted that the proportion of ‘cases’

falls significantly for farms which have lost a large number of animals, a completely

different pattern to that seen on dairy farms. The pattern for suckler beef farms is

similar to that for trading beef, with the results being statistically significant for

people on both ‘lightly affected’ and ‘long period of restriction’ farms (p<0.05).

These findings are illustrated in Figure 5.4.

Table 5.16 ‘High GHQ scores’ by farm type and category of bTB breakdown


Trading

beef %

Deviation

from

control

group

Suckler

beef %

Deviation

from

control

group Dairy %

Deviation

from

control

group


group) 58.62% 0% 34.21% 0% 36.00% 0%

Lightly affected 28.89% -29.73% 58.33%* 24.12% 50.00%* 14.00%

Long term of restriction 56.25% -2.37% 58.62%* 24.41% 55.56%** 19.56%


taken 32.73% -25.89% 45.95% 11.74% 69.05%*** 33.05%

*** p<0.001, ** p<0.01, * p<0.05



127

Figure 5.4 ‘High GHQ scores’: comparison between farm types, by category of bTB breakdown

0.00% 20.00

%

40.00

%

60.00

%

80.00

%

No TB outbreak (control

group)

Lightly affected

Long period of restriction


taken

Dairy %

Suckler beef %

Trading beef %

Since the original postal sample was drawn from the VetNet database with no

restriction on the minimum size of cattle enterprise – the focus of research interest

was all people associated with cattle farming, not simply those on fully commercial

enterprises – some of the survey respondents were associated with very small cattle

enterprises. The next analysis focuses on larger units by excluding those which had

less than 20 head of cattle, the intention being to see if the inclusion of these farms

with essentially small-scale cattle enterprises was skewing the results in some way.

In fact, the analysis generally showed little difference in the results, except that the

decrease in the sample sizes for (a) trading beef ‘no bTB outbreak’ and (b) dairy

farms ‘large number of animals taken’ resulted in an increase in the proportion of

‘cases’ from what was already a very high level (Table 5.17).



128

Table 5.17 Proportions of GHQ ‘cases’ using the truncated sample of farms with 20 or more head of cattle


Trading

beef %

Deviation

from

control

group

Suckler

beef %

Deviation

from

control

group

Dairy

%

Deviation

from

control

group


group) 62.50% 0% 47.83% 0% 35.71% 0%

Lightly affected 27.78% -34.72% 57.69% 9.87% 51.39% 15.67%

Long term of restriction 52.27% -10.23% 59.26% 11.43% 54.65% 18.94%


taken 38.46% -24.04% 47.37% -0.46% 74.29% 38.57%

The three categories of family member respondents, the farmer, spouse and adult

child, were looked at individually for differences from the control group (Table 5.18).

The results point to some interesting differences between each group of family

members. For the farmer the overall pattern of proportions of ‘high GHQ score’

cases was highest on farms with a long period of restriction, a statistically

significantly difference from the control group (p<0.01). A high proportion of ‘high

GHQ scores’ was also found for farmers where a large number of the animals were

taken, representing a fairly high level of stress albeit not statistically different from

the control group.

Table 5.18 Proportions of GHQ ‘cases’ for family members, by category of bTB breakdown

Category of bTB outbreak Farmer

Deviation

from

control

group Spouse

Deviation

from

control

group

Adult

child

Deviation

from

control

group


group) 38.55% 0% 44.12% 0% 50.00% 0%

Lightly affected 44.79% 6.24% 40.48% -3.64% 71.43% 21.43%

Long term of restriction 57.58%** 19.02% 66.67%* 22.55% 45.00% -5.00%


taken 50.60% 12.05% 36.67% -7.45% 50.00% 0.00%

** p<0.01, * p<0.05



129

For the spouse, the levels of stress were significantly higher on farms under a long

period of restriction, but tended to be much lower in situations where a large number

of animals were taken (Table 5.19). The adult child appeared to be under most under

stress when the farm was lightly affected, although respondent numbers for the adult

child are small and these results may not be reliable.

A further analysis was completed looking at farmers by farm type. The results for the

farmers alone were broadly very similar to the overall sample results, with dairy

farmers showing the highest levels of ‘high GHQ score’ cases, a finding which in

statistical terms is particularly significant when a large number of animals are taken

for slaughter (Table 5.19).

Table 5.19 Proportions of GHQ ‘cases’ for farmers, by farm type and category of bTB breakdown


Trading

beef %

Deviation

from

control

group

Suckler

beef %

Deviation

from

control

group Dairy %

Deviation

from

control

group


group) 58.82% 0% 38.10% 0% 31.82% 0%

Lightly affected 31.25% -27.57% 52.63% 14.54% 50.00%* 18.18%

Long period of restriction 53.57% -5.25% 55.56% 17.46% 60.38%** 28.56%


taken 34.38% -24.45% 50.00% 11.90% 72.00%*** 40.18%

*** p<0.001, ** p<0.01, * p<0.05

Finally, an analysis using the total GHQ Likert scale was completed and the results

from this also suggested a broadly similar pattern of stress to the GHQ bi-modal

response score (Table 5.20). Respondents from dairy farms showed a similar pattern

and level of distress; the results were statistically significantly different from the

control group for all types of bTB outbreak, but were particularly high for farms

subject to a long period of cattle movement restrictions, and also farms which had lost

a large number of animals taken for slaughter. Although the results for suckler beef

farms and trading beef farms also followed a similar pattern, these were not

statistically significant because of the high levels of stress amongst even the control

groups.



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Table 5.20 Analysis using the Likert scale: proportions of GHQ ‘cases’ by farm type and category of bTB breakdown


Trading

beef

mean

Deviation

from

control

group

Suckler

beef

mean

Deviation

from

control

group

Dairy

mean

Deviation

from

control

group


group) 16.83 0.00 14.55 0.00 13.35 0.00

Lightly affected 13.49 -3.34 17.47 2.92 15.86* 2.51

Long period of restriction 17.94 1.11 17.69 3.14 18.41*** 5.06


taken 14.54 -2.29 14.78 0.23 19.14*** 5.80

*** p<0.001, ** p<0.01, * p<0.05

Discussion

Understanding the importance of the results from this study of the cattle farming

community in those areas of the country most affected by bTB requires a comparison

of the findings with those from other studies of psychiatric morbidity using the GHQ-

12 questionnaire.

The prime comparator must be the annual Health Survey for England 2004, the last

year for which whole population estimates using the GHQ-12 have been published; in

this study, the estimated prevalence of stress is put at 11% for men and 15% for

women. The results in the present study, for respondents who were either farmers,

part of the farming family or paid workers, shows first a much higher general level of

‘cases’ of predicted psychiatric morbidity, with results in the control groups (no bTB)

ranging from 34.2% for suckler beef farms, to 36.0% for dairy farms and to the very

high level of 58.6% for trading beef farms; the overall figure was 40.1% (Table 5.15).

These much higher estimates of psychiatric morbidity among livestock farmers, in

this case cattle farmers, are replicated in several other studies of livestock farmers

whose cattle herds have been subject to serious animal disease breakdowns (see, for

example, Peck et al, 2002, and Watts et al, 2003).

Moreover, the significantly higher incidence of ‘high GHQ score’ cases for the

people on dairy farms, particularly when large number of animals are taken

(proportion with a high GHQ score = 69.05%, p<0.001) are at a broadly similar level



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to the incidence measured following the foot-and-mouth outbreak in Cumbria in

2001, which was estimated at 73% of the farmers sampled (Peck et al, 2002). This

study also shows that the level of stress for beef farmers, on both suckler and trading

beef systems, is higher when their cattle herds are subject to a long period of

movement restriction (58.62% and 56.25% respectively) but, unlike dairy farmers,

tends to be relatively lower when they have lost a large number of cattle (45.9% and

32.7%). These findings are discussed in relation to farming systems and the other

study findings in Chapter 7.

Interviews with General Practitioners: some case studies

The health and social effects of bTB breakdowns are important indicators as to the

true cost of this event. In addition to exploring these issues with farmers, six general

practitioners (GPs), practising in hotspot areas, were interviewed for their experiences

of the effects of a breakdown. These case interviews serve to reinforce the data

obtained from the survey of farmers, but also provide an interesting insight into the

take-up of health and psychological support by farmers and how they compare to

their non-farming peers. The GPs were asked to consider both the short-term and

longer-term effects of a bTB breakdown on the health and well-being of their farming

patients. GPs are normally informed of cases of bTB as it indicates a possible need

for chest x-rays. No patients were identified even where case examples were given.

For the purposes of confidentiality the particular practices will not be identified,

however the interviews were conducted with GPs practicing in Devon (two),

Somerset (two) and Gloucestershire (two). The interviews were conducted by

telephone and face-to-face. Given the small nature of this survey, no attempt is made

to quantify these findings.

The general health of farmers

In general the health of farmers was said to be comparable to their non-farming peers.

There was no indication that farmers were less physically healthy than those working

in other professions in terms of measures such as blood pressure (with one exception),

strokes, and heart attacks. However, there were some differences, notably higher

rates of injury through accidents at work and lower rates of smoking. Drinking was

rated as ‘about the same’ or ‘slightly higher’ than average, however it wasn’t rated as



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a particular concern in medical terms even though in some cases drinking had

increased during stressful times.

The mental health of farmers

The primary area of impact on farmer’s health was in their psychological health.

Depression, and related symptoms such as anxiety, insomnia, loss of appetite,

withdrawal and other chronic problems, was a commonly cited area of concern. In

extreme cases this resulted in suicide, though the cases cited were attributed to the

effects of FMD and debt (delays in receiving the SFP) rather than bTB. In all cases

the suicides were unexpected, even if, particularly under FMD, some were anticipated

– the particular cases could not have been predicted. This is supported by suggestions

that farmers don’t present early, if at all, with depressive symptoms. Issues of mental

health, therefore, whilst the key area of concern for GPs, was still not adequately

captured - it is likely that what they see only represents the “tip of the iceberg”.

However where cases of depression were presented they were often recurrent

problems, and could be monitored.

The broader impacts of bTB on people

It wasn’t always easy to differentiate between the effect of a bTB breakdown and the

effect of other forms of stress facing the farmer, such as FMD, market conditions,

weather, or delays in single farm payments. Indeed, part of the effect of such a

breakdown, or fear of a future breakdown, can only be understood as part of an

accumulation of numerous stressors:

“I think they’ve had plenty of stresses other than bovine TB, over the last ten

years they’ve had an awful lot of problems to cope with just with no money,

diversification if they can, trying not to sell up. They’ve had plenty of

problems. I guess the bTB has just been one of them….”

“Diabolical state of the livestock industry...it’s hard to pin it down to bTB…I

had someone come into me yesterday who said that he’d got less for a beef

stock at [the] market yesterday than he did in 1971…I hear things like that all

the time”



133

For others the devastation of a previous incident may have served to negate the

perceived impact of a bTB outbreak. In one area where the GP was aware of at least

one outbreak every 6 months, the health effect of these outbreaks was less noticeable:

“I work in an area which was absolutely ravaged by foot and mouth and we had

huge numbers of people who lost all their herd and I think hardly any of them

had foot and mouth, they were all contiguous stuff and that had an absolutely

devastating effect on quite a lot of my patients. And I did some semi research

work at the time and it worked out something like one in every three

consultations had a knock on effect from foot and mouth at the time. Bovine

TB hasn’t really had much of an impact. Whether that’s because the farmers are

mostly immune to it – they’ve had so many things go wrong that comparatively

this isn’t that much and I also don’t think we’ve had a lot of cases..[..].. (with)

foot and mouth, I think we had three suicides.. we had a lot of farmers who lost

their stock and never got round to doing it again, a lot of depression – quite

silent depression – I had people turning up three years after foot and mouth

who’d been struggling and struggling and never got over it. Certainly the

families of the three that committed suicide had huge problems because of, it

was the way it was handled rather than just the stock being lost. And politically,

the way it was managed down here was a complete disaster. So generally

speaking people around here are phlegmatic, particularly the farmers, because

they’ve had one set-back after another. But I can’t really speak to a farmer

without them being flat and thoroughly fed-up. We’ve got very few who are

wealthy and managed to get over everything…I saw a chap last week who, their

farm had been in the family for about four generations who, and I think it was

delayed payments that got them, who just said he’d just had enough and had

sold off to go off and do something different.”

This case occurred in a region which had previously experienced problems with

organo-phosphates. Identifying the ‘bTB effect’, therefore, was not always possible

in this context as other stressors, particular large-scale crises, had a ‘masking’ or

numbing effect. Equally one GP (practicing for 30 years) felt that accidents

(including fatal accidents) were a far bigger problem than the stresses and the

suicides. However, the presentation of symptoms (particularly stress related) was



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often seen at the same time as a bTB breakdown, or around the time of a repeat test.

The lack of control, or capacity to ‘manage’ bTB was also cited as problematic.

The costs of bTB to the National Health Service

It was not possible to quantify the impact of the demands made by farmers on the

NHS, and due to their reticence to come forward any costing of health services used

would not fully measure the real health cost. Furthermore some of the measures, such

as days off sick, are not applicable in a family run farming business. Farmers were

seen as comparatively stoic when it comes to health and tend not to seek medical

attention, as it was perceived as “an admission of failure” or they “had other things to

do”:

“The vast majority would feel depressed, I should think they feel desperate, but

they’re not … the majority of farmers I would say do not present early. They’re

used to coping and they’ve got other things to do”.

One case was given of a farmer who suffered a heart attack but waited until the

morning before calling the doctor. Indications of possible concerns are often

apparent through the spouses who are more likely to seek medical care, though there

was a general feeling that farmers, and even their partners and children to a lesser

extent, didn’t tend to visit the doctor as regularly as those from the non-farming

community. There was also a gender-effect, with women (wives and daughters) more

likely to access health services and discuss their concerns for other family members,

though even they were “pretty self-sufficient has far as health goes”. In some cases

doctors were proactive in offering screening, but the take-up was not high. It was

also suggested that if there was any additional use of services this would be reflected

in the primary care sector.

Farming family and support

Stress and depression can also manifest itself in terms of domestic violence and

relationship breakdown, however there was no evidence to suggest that this was a

particular problem in these farming communities – in fact they were often described

as “more stable”. However there was evidence to suggest that the shape of the

farming family was changing as a result of pressures in farming, with farmers leaving

the profession (typically selling up to wealthy ex-city dwellers), or diversifying away



135

from farming such as the development of holiday property, bed and breakfast,

creation of riding stables, and the younger generation increasingly choosing not to get

involved:

“Now you get to the generation where kids coming through school who would

normally have gone into farming and almost without exception they’re going off

to uni and coming home and saying the last thing I’d want to do is farming.”

There was also general consensus that the family was a key mechanism of (mutual)

support (most farms were a husband and wife team). It is therefore the lack of this

support that made farmers more vulnerable to the effects of stress (in one case cited

where a farmer had become widowed there was an exponential increase in the

number of visits to the doctor through stress-related illness). It was also felt that the

experience of farm-related stress was mainly felt within the family, and primarily by

the farmer, rather than those allied to the farm. Farm workers were typically not seen

as detrimentally affected as they didn’t shoulder the responsibility, and the non-

farming local rural community was also seen as detached from the problems (and

“only interested in cheap food”).

Other farmers were also a valuable source of support, and though most farmers

benefited from this, not all enjoyed this support network - some farmers were

described as quite lonely (though this was alleviated to some extent through good

family support). In many cases farmers were connected by marriage, thereby

providing an extended family support network. Farmers who are lonely were

identified as particularly at risk.

External support and agencies

The GPs interviewed were also questioned about their assessment of the external

sources of support available to, and used by, farmers and the farming community

generally, and for other aspects of the general and mental health of farmers. The

following comments are typical of the responses from a number of doctors:

“quite a few of them (farmers) have been quietly miserable for many years

because of the difficulty of making a living….they don’t expect much in the

way of support”



136

“I think it’s (bTB) just one of many stresses that they’re currently under, and I

don’t think they feel that anyone’s really there to help them very much – that

they’re on their own. That people are looking for fault and not to support them”

“I think they feel sort of desperate really, and I think they don’t know where to

turn really. Their life is tied up in farms and they probably feel fairly hopeless

and perhaps lack confidence in changing and doing other things.”

There was a general feeling that farmers felt a considerable amount of annoyance at

the situation they are in and anger was expressed at Defra for their “lack of

decisiveness” and “procrastination” and for the “costs to the country”. Defra’s

approach to dealing with the problems of bTB, a feeling that they were “useless”,

“unsupportive”, “inaccessible” and provided “insufficient information” were also

cited as concerns/ stressing factors raised by farmers. Even the NFU, with one

exception (perhaps indicating a regional variation – though the data set is too small to

substantiate this9) was also cited as typically unsupportive:

“I think they find both Defra and the NFU pretty ineffective, or possibly even

hostile. I don’t think they find Defra supportive at all”

“I’m sure it’s crucial the way that Defra approach it, from what farmers say

about it, the way they actually approach each individual situation”

The CLA was rated more highly for their support for farmers with bTB outbreaks.

However farmers were also described as “rubbish” at approaching other support

services – perhaps unsurprising given their take-up of medical assistance. However

the younger generations were also described as better at accessing information &

support services (perhaps through being more ‘IT literate’).

The lack of control experienced with an outbreak – due to the process, information,

support and so on - was seen as acting as a stressor, though this experience was also

compared with the FMD outbreak which was seen as a more significant problem, and

had a more significant impact on depression and stress.

9 In this case, where the NFU was described as ‘thriving’ it was also suggested that farmers, whilst

angry at Defra, also understood the overall picture.



137

Moderating factors

There were a number of factors that were seen to moderate the likelihood of

presentation of symptoms (usually stress-related).

Age: One doctor was impressed with coping mechanisms of younger generations

(twenties to forties), though those of younger generations with families to support

were often perceived as at an increased risk (more likely to present with symptoms)

as there were more demands on them, a longer career ahead of them (and a pressure

to keep the farm as a going concern) and they were perceived as having higher

expectations of the industry (less battle-weary) than their older counterparts. These

groups were also sometimes associated with higher alcohol consumption. Many of

the older farmers were seen as better able to cope (in one of the areas identified the

average age of farmers was estimated at around 70).

Duration in farming: Though related to age, duration in farming (the longer the

better) was also seen as a moderating factor. A desire to maintain the farm for the

next generation was also cited as a stressor.

Farm ownership: Farmers who owned their farms were generally thought of as less

affected than tenant farmers. The farm size was also described as helping to

moderate the effects of stress. In both cases this suggests the financial security is key

to reducing stress. However Duchy tenant farmers (in more than one location)

consistently rated their landlord as sympathetic and in these cases the stress

differential between owner and tenant farmer was reduced.

.Diversification: Diversification was seen to alleviate the effects to some extent but

thus was also seen as ‘limited’ moderator (as one doctor pointed out – there can be

only so many B&Bs or riding stables in a locality). Diversification, therefore, was

closely linked with debt alleviation.

Support: The degree of isolation and loneliness was also identified as a key factor.

Those with good family (usually “supportive wives”), and farming support networks

were less likely to present with symptoms. But even with family support farmers

would present with depressive symptoms, such as:



138

“hopelessness and not knowing where to go from there.”

Community support was not cited as a factor that was known to have a significant

effect.

Other crises: FMD, in particular, acted as a moderating factor on the effect of bTB.

In one area there were three suicides, in another it was felt that about 1 in 10 of the

surgery’s farming patients had left the profession. These experiences, alongside

delays in SFPs amongst others, marginalized the direct effect of bTB.

Summary

Whilst farmers clearly discuss their concerns with GPs about bTB and the badger

population, on the basis of these case studies the cost to the health service appears not

to have been substantial. However it is clear from this study that we cannot assume

that there isn’t a significant cost (in health terms) to the farmers, and others in the

farming community, even if these are not presented at the surgery. The tendency for

farmers not to access health services, and the effects of other crises in the farming

industry which have lead to a cumulative ‘stress’ effect, means that the impact of bTB

on the farming community remains difficult to assess, but there is evidence to suggest

that there are significant mental health concerns.

In one interview, for example, the badger problem (and repetition of reaction) led two

farmers (aged 50 and 63) to leave the profession in what was an otherwise viable

business. One of these farmers was also treated for depression and received

counselling. Whilst the costs to the NHS were negligible as no medication was given,

the GP described the costs to the farmers as “considerable”. It is important, therefore,

to understand the hidden costs to both the farmers and their families and also the

future of farming families – demonstrated by the lack of interest by offspring to take

on the farm. Whilst these factors cannot always be attributed to bTB, it is evident

from this part of the research that a succession of crises in farming has had a

demonstrable effect on the health and well-being of farmers.

The farmers’ view: results from the interview survey

This chapter dealing with the effects of a bTB breakdown on the health of the farming

community concludes with the findings from the interview survey, which involved



139

152 farmers in the west and south west of England and in Wales. While this study

focussed mainly on the economic and farm management aspects of a bTB breakdown,

the final section of the questionnaire asked six questions about the wider non-

economic impacts of a bTB breakdown. The results are summarised in Table 5.21.

The findings point to the existence of longer-term effects of a non-economic nature

not only on farmers but also, albeit to a lesser extent (on the evidence presented here),

on their families and even their staff. Seven out of ten of the farmers interviewed

stated that the breakdown had affected their daily (working) life, while four out of ten

indentified effects on their families and two out of ten on their farm staff. It should

be noted that the actual incidence of this was higher as not all farms had employees.

Smaller proportions of respondents had noticed differences in the attitudes of both the

farming and local communities.

Table 5.21 The non-economic impacts of a bTB breakdown: the farmers view

Question asked Yes No No reply

Q: Has the bTB breakdown affected

your daily life in any way?

71% 29%

Q: Has bTB affected your family or

your household in any way?

43% 56% 1%

Q: Has bTB affected your employees in

any way?

19% 76% 5%

Q: Has bTB affected your community

in any way?

31% 68% 1%

Q: Have the views of the farming

community towards you changed in

any way since your bTB breakdown?

10% 90%

Q: Have the views of the local

community towards you changed in

any way since your bTB breakdown?

7% 93%

These responses should be seen in the context of GPs’ assessments of the general

reticence of many farmers to admit to, or discuss, health or social problems. For each

question, if the answer was ‘yes’ the respondent was invited to provide details and

the comments provide considerable colour to the statistics on farmers’ health, and that

of their families and workers, presented and discussed in this chapter. Space



140

constraints prevent reproducing more than a handful of these comments, but suffice to

say that most of the respondents who answered ‘yes’ did take the opportunity to share

something of the pressures of living with a bTB breakdown. Case Study H following

relates to a farm visited as part of this study, and illustrates the possible range of

effects arising as a result of a bTB breakdown.

Case H – dairying This farm is run by the farmer, his daughter and his son in law. The farm totals 195 acres and has a closed herd of around 100 dairy cows. The vast majority of the cattle on the farm are pedigree. The farm has been hit by three bTB breakdowns in 2002, 2004 and 2007. All of these disease incidents have been very severe with 65 cows being taken away with suspected bTB in 2002, 26 cows in 2004 and 9 cows (at the time of the farm visit) in 2007. The family found that the bTB breakdown hit their cash flow the hardest, as they had lost so many cows that their total milk output dropped severely. Around £80,000 of milk sales is estimated to have been lost during the first breakdown in 2002. The other major problems the family have found with the bTB breakdown are the loss of pedigree breeding cows for the herd and the loss of genetic lines, and also the loss from not being able to sell breeding stock, normally a very useful accessory enterprise. The final problem associated with the outbreak is that the farm really requires a major upgrade to its milking facilities and milking parlour, a very significant investment, but due to the ongoing bTB problems this has had to be left on hold for several years until the business regains a steady cash flow and the future looks more assured. A great amount of emotional stress has also been associated with the bTB breakdowns, due to the removal of so many cattle which had been on the farm for several years. It has also been found that the continual testing of the animals, although a necessary part of disease control strategy, also creates much stress for both the farmer and farm family, and the animals. Finally, the farmer has also been disappointed in that he doesn’t have a business reason to go to market any longer, because he isn’t allowed to sell cattle there; this meant that he doesn’t get to socialise with other farmers, and this aspect has therefore made the job more lonely.

In terms of the effects on the farmers themselves, almost all farmers who reported

some effect from the bTB breakdown on their daily lives volunteered an additional

comment. These responses are far too numerous to reproduce, but covered a very

wide range of issues, the principal themes including:

• The effects of uncertainty about the future on the business, and on plans; one

example given was the problem associated with milk production forecasts nine

months in advance for their milk buyer (which had contractual penalties if



141

supply fell short, which would be the result if too many cows were taken in the

meantime).

• Aspects of ‘stress’ were very commonly articulated: physical, mental and

emotional effects were mentioned, as well as the effects of living with the

problem for months and years – the sheer longevity of the issue was seen as

taking its toll.

• The time taken for cattle testing often constitutes a very real increase in

workload, many farms already operating with the minimum labour required

because of financial pressures. Farmers mentioned aspects of the time to fetch

animals, the actual testing operations, and their perceptions of a lack of any

direct) benefit from the time spent. Some mentioned the adverse effects of the

testing on the cattle, including the increased incidence of spontaneous abortions,

for example.

• Notwithstanding the compensation for cattle taken, several mentioned increased

financial pressures as a result of the bTB breakdown; this may result from one

or more of reduced throughput, additional costs, curtailed expansion, or a

change of system (to a sub-optimal system) in order for the business to continue

to function with the cattle movement restrictions, for example.

• Generally, following a bTB breakdown there is a noticeably increased workload

with on-going time pressures; in addition to the cattle management aspects (e.g.

the isolation of IRs, etc.) the increased administration was also frequently

mentioned, such as additional paperwork, telephone calls etc.

The best way to understand something of the flavour of the comments made is to hear

the words spoken, and the following examples of farmers’ comments are typical:

“It was a worry at the time. And all the testing is so much extra work and puts

stress on the cows.”

“Had to spend so much time testing. It puts you behind with harvesting and

other jobs. It leaves me exhausted every time.”

“My family have farmed for generations. But I've lost heart in it now. I shall

never re build my suckler herd.”

'Harder work. More stock to keep. So much worry about whether we got the

paperwork right.

“There is so much stress. And every six weeks we dread the next test. What

has to go next?”

“Financially tied. More work. Stress. So much time with no benefit from it.

Shooting down good animals for no reason and the cause is not addressed.”



142

“More stress because of worries about cost of feeding when can't sell them.

Also worrying about how long going to be closed down for. The hassle of

having to test every 60 days. Also worry of extra cost.”

“Stress and additional testing increases workload. Looking after IR's that have

to be isolated also extra work. TB testing is stressful for the animals and

between the three of them they have required hospital treatment five times due

to TB testing accidents. (Cattle) not normally wild but know when testing

time.”

“Felt a bit down at the time of the first outbreak but coped better with passing of

time.”

“The stress impacts on my working, married & family life. There is extra

tension with bTB. Especially when cows slip calves etc. The test seems

inaccurate.”

“As TB wiped out the herd, there was a large reduction in income.”

“Very stressful, husband became very ill. Way of life has now changed as a

direct result. Financial constraints were the hardest.”

“Workload increase AND the uncertainty of everything (can't make plans for

the short or medium-term future, feels as if the business is 'on hold').”

Many farming businesses operate as notably tightly focussed family enterprises, often

with little or no distinction between business and family space e.g. the farm kitchen

not infrequently serves as part of the farm office and hosts round table business

discussions in addition to its purely domestic role. In these circumstances, it is not

unexpected that other family members will be all too aware of business pressures, and

these may be exacerbated when children have a direct interest in, or even

responsibility for, some of the animals. The following comments give an indication

of the range of possible effects on family members:

“Fraught at home due to all the worry.”

“We are always talking about it. It's caused so much hassle and really upset us.”

'All the children were tested because they all drank the milk. They all used to

help with the cows and they were all devastated. My son will not drink milk

any more because he does not like shop milk and he has not eaten a bowl of

cereal since.

“Tries to not let it affect the family.”

“Children worried. Wife had to go out to work.”

“Put stress on wife and marriage.”

“We are stressed with the anxiety and upset. These cause mood swings of the

farmer which impact on the whole family.”

“Wife's temper. All the vets and workers had to be fed every 2 months for 5

years.”

“Kids very upset by seeing their favourites having to be slaughtered.”



143

“Children hate to see calves shot/taken; also, heifers groomed, walked, shown at

Agric. Shows - painful to see them destroyed (four taken and killed for bTB).”

Fewer farmers thought that a bTB breakdown affected employees (and many did not

employ anyone other than the family), but of those who did it was often recognised

that they too could be affected. The economic effects could range through extra work

pressures for no more money, through enforced extra (paid) overtime to losing their

employment. Again, a few representative comments are as follows:

“They get fed up with the job. They still have to do their other work so they get

overtime.”

“Herdswoman's favourite cow. She used to bring her an apple every day. She

was terribly upset.”

“I had a lad full time who had been here since he left school. Well I could not

afford to keep him full time and in the end he left agriculture altogether. If we

had not had TB I expect he would still be here.”

“Additional workload in testing 500-600 head of cattle every 60 days.”

“Workman injured his back during a bTB test and was out of action for four

months. On other occasions jobs don't get done as the employee may be in

contact with infected animals on another farm under restrictions which would

breach our bio-security.”

The respondent was also asked about the effects on the local community, on the

farming community and whether the views of the local community towards the

farmer had changed following the bTB breakdown. While some of the responses

reflect an overlap between these questions, there was a general feeling that the local

community was not overly affected, even if they knew of it. People from outside

farming often didn’t appreciate the consequences for the farm and the farm family,

although a number mentioned adverse effects since farmers had less money to spend

locally. For most farmers, their immediate neighbours are other farmers, and there

was a range of responses here: some reported increased friction between farmers,

others acknowledged sympathy and support, some said the farming community was

more open about bTB now while others said it was a very closed topic. Broadly,

most non-farmers were not expected to understand, although some were clearly

sympathetic and supportive; one mentioned a reluctance of ramblers to comply with

bio-security precautions (dipping feet).



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Conclusions

With respect to the implications of a bTB breakdown for human health, the main

conclusions are as follows;

• The work reported here has augmented previous research on both (a) the short-


crises on the farming community.

• While a bTB breakdown is less dramatic than some other livestock disease

crises (such as FMD and BSE) by its nature the persistence and pervasiveness of

the control programme represents a clear source of on-farm stress, typically

over an extended period.

• The stakeholder consultation reflected perceptions of a range of human health

effects as a consequence of bTB, including physical, mental, emotional and

social changes. Many highlighted ‘uncertainty’ and ‘lack of control’ as

powerful drivers of human health problems.

• The GHQ-12 study provides objective evidence of the scale of the longer-term

effects of a bTB breakdown:

o In general terms, a much greater proportion of farmers exhibit signs of

psychiatric morbidity that the population as a whole.

o Overall, the highest stress levels are seen on farms which have been

under livestock movement restrictions for a long period.

o However, on dairy farms, where people are generally more stressed

than on beef farms, the greatest stressor is the loss of a large number of

cattle.

o There is evidence that bTB is associated with raised stress levels for

people other than the farmer, particularly spouses; but the sample was

too small for reliable results for farm workers.

o For farmers, a bTB breakdown causes significant stress: this is highest

for dairy farmers, especially where they have lost a large number of

cattle; while for both trading beef and suckler beef farmers, a long

period under restriction is the greater stressor.

• The study of rural GPs identified that farmers typically present late with

symptoms and, for mental health issues in particular, GPs probably only see a

small proportion of cases. Other findings include:



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o Several GPs pointed to the range of pressures affecting farmers,

particularly livestock farmers, of which bTB is only one; and the

consequent difficulty in direct attribution of cause and effect.

o The lack of control experienced as a result of a bTB breakdown was

identified as an important stressor; even so, bTB was thought to be a

less serious problem than the FMD epidemic of 2001.

o A range of moderating factors include age, farming experience, farm

ownership, diversification, the availability of support and the

significance of other crises.

• At interview, most farmers identified adverse effects on their daily lives, and

often on those of their families and others. Key effects relate to uncertainty,

stress, time pressures and financial worries. Knock-on effects on the local and

farming communities do not appear in general to be very significant.



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6 EXPLORING FARMERS’ WTP FOR A bTB VACCINE

Background

The project also explored farmers’ willingness-to-pay (WTP) for a bTB cattle vaccine

using a methodology based on a choice experiment (CE) questionnaire, which was

designed with appropriate choice sets to elicit cattle farmers' willingness to pay. The

choice experiment was used to estimate WTP for a vaccine depending upon a number

of vaccine characteristics including, inter alia, the efficacy of the vaccine and whether

subsequent breakdown losses (i.e. even with vaccination) are covered by an insurance

programme. Using appropriate scenarios the aim was to provide estimates of cattle

farmers' WTP for a bTB cattle vaccine, given that such a vaccine is likely to be less

than 100 per cent efficacious.

The project target was to administer the choice experiment to a sample of 300 cattle

farmers, with the sample split according to those that have suffered a bTB breakdown

and those that have not but are situated within bTB 'hotspot' areas. The sample was

split according to survey method. All of the target 152 cattle farmers interviewed as

part of the farm survey were also invited subsequently to complete the choice

experiment questionnaire, with a target of a further 150 cattle farmers also being

interviewed; both samples were interviewed by telephone. Both sets of farmers were

sent explanatory material relating to the choice experiment by post in advance of the

interview. The combined sample of 300 was thought to be adequate to ensure

statistical significance for the main analyses.

Study objectives and methodology

Formally, this part of the research project had the following specific objectives:

1. Estimate cattle farmers’ WTP for a bTB cattle vaccine.

2. Assess how farmers’ WTP varies with selected vaccine attributes.

The methods used in this part of the research project have a strong theoretical basis

and, in particular, the WTP estimation methods combined two established



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approaches. Farmers’ WTP for the vaccine attributes were estimated from the choice

experiment responses using a Bayesian logit model in accordance with Koop and

Poirier (1993). Farmers’ WTP for the cattle vaccine specified in the contingent

valuation (CV) questions were estimated using a Bayesian interval data estimation

method, which is a special case of the simultaneous equation probit estimator

described by Cameron and James (1987). This Bayesian estimator should be very

similar to the maximum likelihood estimator.

Main stages of the research

The research into farmers’ WTP involved a number of stages, designed to ensure that

the scenarios and assumptions used were robust, scientifically realistic and well

grounded in terms of cattle farmers’ understanding and expectations. The principal

stages in designing the questionnaire and refining the approach included:

1. Scoping and consideration of vaccine attributes.

2. Initial questionnaire design.

3. Farmer focus group.

4. Questionnaire development and pre-testing.

5. Questionnaire pilot survey.

6. Main telephone survey.

7. Data entry and analysis.

8. Reporting.

The first two stages involved discussions with Defra and others concerning vaccine

attributes and design of a questionnaire to elicit farmers’ WTP for a bTB cattle

vaccine. The next stage involved undertaking a focus group of cattle farmers. Focus

group participants were recruited from a veterinary practice list of cattle farmer

clients. The practice was based in a bTB ‘hotspot’ near Tiverton in Devon. Nine

cattle farmers were recruited for the focus group made up of dairy, suckler beef and

finishing beef cattle farms. Of these, six had had a bTB breakdown within the last

five years and three had never had a breakdown. The focus group was undertaken in

a public house near Tiverton over lunch time for two hours led by the researcher as

facilitator and an assistant. The focus group was structured around two main

objectives which were (a) to discuss farmer’s perceptions of and attitudes to bTB, a



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cattle vaccine and other methods of bTB control and (b) testing the draft

questionnaire for ease of understanding and ability to complete the questions.

The fourth stage of research involved using the findings of the focus group to further

develop the questionnaire followed by a pre-testing of the questionnaire by means of

personal and telephone interviews to known farmers. Following these the

questionnaire was further refined prior to undertaking a pilot survey.

A pilot survey of 30 farmers was then undertaken using those sampled from the

VetNet data base. Following this pilot any minor problems in the questionnaire were

amended and the main telephone survey was undertaken. Responses to the

questionnaire were entered onto a database (MS Access) for analysis. Analyses

involved production of descriptive statistics for each question and estimation of

willingness to pay. Additional analyses involved cross tabulation and exploration of

correlations between question responses/variables.

Questionnaire design

The questionnaire used in the telephone survey was structured into eight main parts

(see Appendix F for the actual questionnaire), as follows:

1. Basic farm information (such as whether dairy or beef, size of herd etc.).

2. Information relating to bTB on the farm.

3. Attitudinal statements and questions concerning bTB risk, bio-security etc.

4. Information statement about bTB and a possible cattle vaccine.

5. A CE exercise (containing a description of the choice experiment and

explanation of what the respondent should do and presentation of eight choice

sets).

6. A CV question.

7. Open-ended debriefing question (asking respondents to explain the reasoning

behind the choices they made) and follow-up attitudinal questions relating to

their WTP.

8. Personal details of the farmer and the farm family (e.g. age, family members

farming).



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Sampling frame and survey method

A stratified sample of cattle farmers in annually bTB-tested areas in England and

Wales was drawn from the VetNet database. The sample was stratified according to

whether farms were dairy or beef, whether they had had a bTB breakdown in the last

five or ten years or never had a breakdown and by area. The sample also included the

152 farms which took part in the farm survey.

Farmers were contacted by telephone and asked if they would participate in the

survey. For those that agreed, a convenient time for the telephone interview was

agreed and a confirmatory letter and additional information was then sent to the

participant prior to the telephone interview. The additional material sent comprised

the information statement, including a description of the choice experiment exercise,

and a copy of the choice sets from which they would be asked to select their preferred

choice.

The interviews lasted approximately 15 minutes. In most cases participants had read

the information statement and marked their choices on the choice sets previously sent

to them prior to the interview. This considerably aided the interview process and

limited the time taken to conduct the interview.

CE and CV design

The information statement described a single-dose bTB cattle vaccine that would be

administered once only to targeted groups of cattle. Four attributes were chosen in

relation to the way in which the cattle vaccine as characterised in the choice sets,

having been selected following discussions with Defra and others and following the

guidance gained at the farmer focus group. The attributes were:

(i) vaccine effectiveness in terms of the ability of a vaccine to reduce the risk of

a farm having a bTB breakdown (i.e. its ability to prevent a breakdown);

(ii) vaccine effectiveness in terms of the ability of a vaccine to reduce the

severity of a breakdown (i.e. reduction in the number of reactors);

(iii) the level of insurance or ‘loss recovery’ associated with a vaccine where a

vaccine fails to prevent a bTB breakdown; and

(iv) the cost per dose per animal of the vaccine.



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The first attribute was given four possible levels providing reductions in the

probability of a farm getting a bTB breakdown of 20%, 40%, 60% or 80%. The

second attribute was also given four possible levels for reducing the severity of a

breakdown of 20%, 40%, 60% or 80% (i.e. that the number of reactors would be

reduced by these percentages). The third attribute – the extent to which the farmer

was covered by insurance for total losses due to bTB – was given five levels of 0%,

40%, 60%, 80% and 100%. The fourth attribute – price of the vaccine – was given

five levels of £5, £10, £15, £20 and £30. These levels were chosen following

discussions with Defra and others and were subsequently tested in the focus group

and in the pilot survey.

An illustrative example of a choice set is shown in Table 6.1 below, together with the

levels that were used for each attribute. Columns A, B, and C represent the alternative

choices from which respondents were asked to choose their preferred one (i.e. they

must choose either A, B or C).

Table 6.1 Example CE choice set showing attributes and their levels

Twenty-four choice sets were generated from the attribute levels shown in Table 6.1

by means of a purpose-built computer program for generating choice sets. Any

choice sets that had a clearly dominant choice (i.e. contained attribute levels that were

better in all regards compared to other choices) were amended accordingly. Eight

choice sets from these 24 were then randomly allocated to each participant in the

survey (because eight choice sets were considered to be the maximum that could

£0 £20 £10 4. Cost of vaccine dose (£5, £10, £15, £20, £30)

70% 100% 60% 3. Insurance/loss recovery as % of total financial

loss from bTB (0, 40, 60, 80, 100)

0% 80% 80% 2. Vaccine effectiveness - reduction in the

breakdown severity (20, 40, 60, 80)

0% 80% 60% 1. Vaccine effectiveness - reduction in the risk

of a breakdown (20, 40, 60, 80)

C B A Vaccine attributes



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reasonably be presented to each participant). An illustrative example of just one of

these choice sets is shown in Table 6.1.

The CV question immediately followed the CE exercise. In this, participants were

provided with a brief scenario of a bTB cattle vaccine that was 90% efficacious and

backed by a 100% insurance/loss recovery of total losses due to a TB breakdown. It

was assumed that the vaccine had to be administered annually.

The CV question was double bounded and asked participants ‘Would you be willing

to pay £x per animal per year for such a vaccine?’ Participants were first asked this

question in relation to either £10 or £15 (chosen randomly). If they answered ‘no’ to

this first question they were then asked whether they would be willing to pay a lower

amount, either £5 or £7 respectively. If they answered ‘yes’ to the first question they

were then asked whether they would be willing to pay a higher amount, either £20 or

£30 respectively.

Study results

Findings from the focus group

The main findings of the focus group were that:

• All farmers felt at high risk of their cattle getting bTB.

• There was a general consensus that there may need to be control of badgers

(and possibly other wildlife) as well as other bTB control strategies – but they

felt that information regarding the pros and cons of badger culling was

confusing.

• There was recognition that bTB was a ‘political issue’ and a belief that any

policy decision (e.g. regarding badgers) needed to have the backing of the

general public.

• There was a desire to ensure that a vaccine should not hinder international

trade (e.g. a differential test must be able to differentiate vaccinated cattle

from those exposed to bTB).

• It was questioned whether farmers should have to pay all of the costs of

administering a cattle vaccine or whether there should be some cost sharing

with government.



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• It was strongly felt that a vaccine should be insurance-backed to provide

against adverse outcomes from using an ineffective vaccine.

• The most important vaccine attribute for farmers was the ability to prevent a

breakdown (not ability to reduce breakdown severity).

• It was recognised that a ‘bTB-free’ label could be good for marketing

purposes, of both cattle and cattle products.

• The draft questionnaire largely worked well and participants could understand

and answer all of the questions.

The telephone survey results

Completed questionnaires were obtained from 287 cattle farmers in England and

Wales. Sixty-five per cent of those farmers who agreed initially to participate in the

survey actually completed the telephone interview, the relatively high ‘failure to

complete’ rate being explained by on-farm pressures of work when the time to take

part actually arrived. Eighty-seven of these had also participated in the University of

Exeter/ADAS interviews.

The sample comprised 46% dairy farmers and 54% beef farmers. Of these, 5.6%

were registered organic. The mean size of farm was 147 dairy cows and 50 beef

cattle. Twenty-four per cent of herds were pedigree. Thirty per cent of dairy herds

and 52% of beef farms had a beef fattening/finishing enterprise. Fifty per cent of

farms also had a sheep enterprise (30% of dairy and 67% of beef farms), whilst 5%

had pigs and 5% poultry. Average milk yield per cow was 7,246 litres per year for

dairy herds and suckler beef herds had an average calving rate of 95%. Ninety per

cent of respondents were aged 40 yrs or over and the majority (88%) were owners of

their farms (12% were tenant farmers and just 1% were farm managers). Around

63% of farmers had successors to the farm business whilst 93% intended to continue

cattle farming for the foreseeable future (this percentage was almost identical for

farms which had had a bTB breakdown compared to those that had not). Of those

who intended not to continue cattle farming, 21% (only five respondents, representing

about a fifth of the ‘give up cattle farming’ group) gave bTB as the reason.

In terms of their bTB history, 68% of farms had had a bTB breakdown (74% of dairy

farms and 63% of beef farms), of which 78% were confirmed breakdowns. On



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average, farms had had 2.4 bTB breakdowns in the previous five years, with an

average of seven reactors in the most recent breakdown (7.5 for dairy farms and 6.5

for beef farms). The number of reactors quoted ranged from zero to 200 (this latter is

likely to have been the number of animals slaughtered rather than all of them being

reactors). The average length of breakdown experienced by these farms was 34

weeks (36 weeks for dairy farms and 32 weeks for beef farms). More than two thirds

(68%) of farms were clear of bTB at the time of the survey.

Responses to the attitudinal questions are shown in Table 6.2 below. Respondents

were asked to score on a scale of 1 to 5 the extent to which they agreed or disagreed

with each statement, where 1 = disagree strongly and 5 = agree strongly and 3 =

neither agree nor disagree.

Table 6.2 Percentage of farmers scoring at each level of agreement to the attitude statements presented to them and their mean score

Statement 1 2 3 4 5 Mean

score

bTB is a major risk for the cattle

industry

0.4 1.0 2.1 19.1 77.4 4.7

My farm has a high risk of a bTB

breakdown

4.9 21.9 12.5 31.9 28.8 3.6

Bio-security measures on farms can

greatly reduce the risk of bTB

13.9 25.7 21.2 30.2 9.0 2.9

There is not much that I can do to

prevent my cattle getting bTB

3.5 15.6 9.4 45.8 25.7 3.7

Overall, these scores suggest that these farmers, at least, strongly consider bTB to be

a major risk for the cattle industry and that their farms are at a high risk of getting

bTB. Moreover, many of these farmers are not convinced that on-farm bio-security

measures can greatly reduce the risk of bTB, while many feel relatively powerless in

preventing their cattle getting bTB.

Their perceptions of business risk from bTB were further explored by three additional

questions. The first asked them to tick which level of risk they perceived to relate to

the probability of their herd testing positive to bTB in any one year. The results are



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shown in Table 6.3. The table shows that a relatively high percentage of this group of

farmers (54%) felt that they face a 50% or greater chance of their herd getting bTB in

any one year. It is also clear that those farmers who have already experienced a bTB

breakdown feel at even greater risk (63% felt they had a 50% or greater chance of

their herd getting bTB) than those that have not (35% felt they had a 50% or greater

chance of their herd getting bTB).

Table 6.3 Farmers’ perceptions of the risk of their herd testing positive to bTB in any one year

Likelihood (%) Whole sample bTB breakdown No bTB breakdown

>50 30 38.5 12.1

50 24 24.6 23.1

33 12.2 12.3 12.1

20 9.4 10.8 6.6

10 8.7 5.1 16.5

5 5.6 3.5 11

<5 10.1 5.6 18.7

Table 6.4 further supports this finding of a high perception of risk amongst the

sample farmers. Farmers were asked how likely they felt it was that their herd would

test positive to bTB within the next three years. Fifty-six per cent of the whole

sample felt that this was quite or very likely but 75% of those whose herds had had a

bTB breakdown felt this. Only 7% of these farmers thought this outcome was very

unlikely.

Table 6.4 Farmers’ perceptions of the likelihood of their herds testing bTB positive within the next three years

Whole sample bTB breakdown No bTB breakdown

Very likely 32.8 44.6 7.3

Quite likely 33.4 30.8 39.6

Neither 11.5 10.8 13.2

Quite unlikely 15.0 8.7 27.5

Very unlikely 7.3 5.1 12.1



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These perceptions can be compared to national bTB statistics which show that even in

the highest incidence counties, such as Devon and Gloucestershire, around 22% of

herds had been under restrictions due to a bTB incident during 2007 (Defra website).

Of course, the local incidence of bTB could be considerably higher than the county-

level incidence, and it is this which undoubtedly will colour these farmers’

perceptions of the risk faced by their own cattle.

This group of farmers were also asked how likely they thought it was that they would

experience a severe bTB breakdown within the next five years (see Table 6.5). The

sample was evenly split on this: nearly 40% thought this outcome either ‘quite likely’

or ‘very likely’ (44% of those who had already had a bTB breakdown), while 43%

thought it ‘quite unlikely’ or ‘very unlikely’. It is possible that their expectations will

have been influenced by their local experience with regard to changes in the

incidence of bTB breakdowns over the past five years.

Table 6.5 Farmers’ perceptions as to the likelihood of a severe breakdown within the next 5 years

Whole sample bTB breakdown No bTB breakdown

Very likely 16.4 22.1 7.7

Quite likely 23.0 22.0 39.6

Neither 17.4 18.5 13.2

Quite unlikely 26.1 23.6 27.5

Very unlikely 17.1 13.8 12.1

Farmers were asked about their bio-security practices. Table 6.6 shows the

percentage of these farmers who currently undertake the recommended bio-security

measures to protect their herds against bTB10. It can be seen that around 28% of

cattle farmers do not always ensure that bought-in cattle or hired bulls are bTB free

(despite mandatory pre-movement bTB testing) with 21% never doing this.

However, a large proportion of the latter are those who operate closed herds and so do

not buy in cattle. Nearly 23% of farmers do not always isolate reactors or

10 Editor: Since the questionnaire does not have a ‘not applicable’ option for use e.g. where the farm

operates as a closed herd (cf option1 - no bought in/hired cattle) or where the system does not involve

breeding (cf options 3 and 4 on home reared replacements, use of AI) it is not clear how responses

were handled in these cases. This will be clarified for the final report.



156

inconclusive reactors from the main herd (despite this being a mandatory

requirement). Most farms do not isolate and post-movement test purchased stock

(although around a third of those that never do this do not buy in cattle) but nearly

three quarters only purchase or hire bTB-free livestock. There was little difference in

responses concerning bio-security practices when comparing farms that had had a

bTB breakdown and those that had not.

Table 6.6 Adoption of standard bTB bio-security practices by sample farmers

Bio-security option Always Mostly Sometimes Never

1. Ensure bought-in cattle/hired

bulls are bTB free 72.2 3.9 2.8 21.1

2. Keep cattle away from cattle

on other farms 66.0 19.1 4.5 10.4

3. Breed own replacements 53.1 20.1 6.6 20.2

4.Use AI instead of hiring a bull 21.5 20.1 11.2 47.2

5.Isolate reactors/inconclusives 77.4 6.6 5.6 10.4

6. Cleansing and disinfection 66.8 16.6 8.5 8.1

7. Isolate and post-movement

test purchased stock 27.2 6.0 6.0 60.8

8. Protect farm buildings from

access by wildlife 10.4 25.7 13.9 50

However, for the most practical of the identified bio-security measures it would

appear that generally about 85% of these farmers at least are always/mostly applying

the guidance on bio-security (options 2, 4, 5 – given that options 1, 3 and 4 are not

applicable to all). It is also possible that there is some interaction between the

responses given to option 7 above and those citing option 1 – if bTB free stock are

purchased, it might be seen that there is less need to isolate/retest them. The

interview survey established that the last option remains contentious, to the extent

that there seems to be a common view that it can’t necessarily be achieved in

practice; and this was given some support by Defra (add 2007 reference). Moreover,

contamination of grassland by badgers is recognised as a possible source of infection,

thus potentially rendering expensive defences of farm buildings (if this is what option

8 might involve) worthless. At the very least, these results point to the need for a



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renewed knowledge transfer effort regarding the need for the highest standards of bio-

security.

Estimates of farmers’ WTP for a bTB vaccine

Choice experiment estimate

First, the results from the CE are presented in Table 6.7, which shows the Bayesian

logit estimation of the mean WTP for each of the vaccine attributes.

Table 6.7 CE estimates of WTP for a bTB cattle vaccine

Mean WTP Median WTP Stdv

£ per percentage point

Vaccine reduction in breakdown risk 0.278102 0.278200 0.0124642

Vaccine reduction in breakdown

severity

0.004201 0.002999 0.0038650

Insurance/loss recovery (vaccine

failure)

0.192020 0.192400 0.0151300

The proportion of correctly predicted responses for the logit estimator was around 54%,

which is a reasonable percentage for such models. In Table 6.7, WTP is expressed in

terms of pounds per percentage point. Thus, on average, farmers would be willing to

pay £0.28 for a 1% increase in the ability of the vaccine to reduce the risk of a herd

having a bTB breakdown but only £0.004 for a 1% increase in the ability of the

vaccine to reduce the severity of a breakdown. It is clear from this that farmers’ main

concern is to prevent a breakdown in the first instance rather than to minimise its

severity. Respondents are also willing to pay £0.19 for every 1% increase of total

loss that is covered by insurance. Thus, for example, assuming a linear relationship

between the level of these attributes and WTP, farmers would be willing to pay £25

per animal per dose for a vaccine that is 90% effective at reducing the risk of a bTB

breakdown and an estimated £44.20 (£25 + £19.20) for such a vaccine that is backed

by 100% insurance of loss if a breakdown should occur despite vaccination.

Further analysis found that farmers who had not had a bTB breakdown had a

statistically-significantly higher WTP for the vaccine’s ability to reduce the risk of a

breakdown than those that had had a breakdown. It may be that these farmers have a



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greater dread of having a bTB breakdown than those that have learnt to live with the

disease and thus have a higher WTP to avoid it. However, WTP was also correlated

positively with farmers’ perceptions of the likelihood of their herd having a bTB

breakdown (i.e. the more likely farmers thought it was that their herd would suffer a

breakdown the higher their WTP for a cattle vaccine). The strength of association

was highest in terms of WTP to reduce the severity of a breakdown (i.e. if they were

to have a bTB breakdown then farmers valued the vaccine’s ability to limit its

severity).

Contingent valuation estimate

Secondly, WTP was also estimated from the responses to the CV question using a

Bayesian interval data estimation method. Table 6.8 shows the resulting estimates.

Table 6.8 CV estimation of WTP for a bTB cattle vaccine

£ Stdv

Mean WTP 16.94 0.732

Error variance 132.98 16.46

The valuation is for a vaccine that is 90% effective and insurance backed by 100%

recovery of total loss and is administered annually. As can be seen from the table, the

mean WTP was estimated at £16.94 per dose/per animal/per year.

Follow-up questions

Respondents were asked a number of follow-up questions after the WTP questions, to

provide some context to their responses. The first was an open-ended question asking

respondents to explain the reasoning behind their WTP responses. Farmers’

responses to this question were coded into a number of different categories (with

individual farmers often giving reasons relating to more than one, sometimes several,

categories and so the following percentages will add to more than 100%). Around

48% of respondents’ reasoning mentioned the importance of the effectiveness of the

vaccine to their WTP responses, whilst 34% mentioned the importance of

‘affordability’ and 29% mentioned weighing up the benefits of a vaccine in relation to

its costs. Twenty-one percent of respondents mentioned consideration of the risk of

bTB as important to their WTP responses, whilst 19% mentioned the need to control



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bTB in wildlife. Eighteen percent mentioned the importance of insurance back-up to

a vaccine, 12% mentioned the role of government (usually in the context that

government should do more or do things differently) and nearly 8% referred to the

poor profitability of farming as important to their WTP responses.

The second follow-up question contained a number of attitudinal statements and

respondents were asked the extent to which these represented their views on a scale of

0 to 10, where 0 = ‘does not represent my views at all’ to 10 = ‘represents my view

very well’. Table 6.9 shows a summary of their responses to these statements.

Table 6.9 Farmer responses to attitudinal questions

Statement Mean score

Development of a bTB vaccine is the best solution to the problem 6.4

A vaccine with high effectiveness is important 8.8

A vaccine with low cost is important 7.5

A high level of insurance/cost recovery for a bTB vaccine is important 7.8

Another strategy (other than vaccine development) is needed 8.3

I would be willing to pay something for a badger vaccine 3.4

A combined strategy is needed (including wildlife control) 8.4


The results of the survey show that cattle farmers have a substantial WTP for a bTB

cattle vaccine. They also show that farmers primarily value the ability of a vaccine to

prevent a breakdown (or rather to reduce the probability of a breakdown). However,

they also recognise that a vaccine is unlikely to be 100% efficacious and so they also

value insurance backing that would pay compensation to farmers should vaccinated

cattle test bTB positive. Farmers appear to value the ability of a vaccine to reduce the

severity of a breakdown somewhat less, and their prime concern is to prevent a bTB

breakdown in the first place. This is not surprising, given that some of the most

intrusive consequences of bTB, such as movement restrictions, re-testing etc. are

incurred regardless of the severity (i.e. number of reactors) of a breakdown.

Both the CE and CV instruments used were carefully developed and tested prior to

their application. The WTP estimated by the CE method seemed reasonable and was



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backed up by appropriate reasoning given by respondents. The CV estimate was of

broadly the same order of magnitude as the CE estimate, despite the two valuation

methods being fundamentally different and the payment vehicle used also being

different, and with a different vaccine scenario presented to respondents. This

provides some reassurance that the WTP estimates are robust and will not vary

greatly depending on the valuation method or the nuances of the scenario presented to

respondents.

Many cattle farmers in the survey felt at high risk of their cattle having a bTB

breakdown. Indeed, their (subjective) perception of risk is likely to be somewhat

higher than the actual (objective) risk that they face11. This perception of risk was

found to be positively correlated with their WTP. Despite this, farmers often did not

adopt all possible bio-security measures against bTB, although it is likely that not all

are equally relevant on every farm, or for every farming system. Many farmers felt

that there was little they could do to prevent their herds getting bTB.

Overall, cattle farmers did not think that development of a cattle vaccine was, by

itself, the answer to the bTB problem but that a combined strategy was necessary with

a number of elements, including wildlife control.

11 It would be useful to contrast farmers’ perceptions of risk (at a local level) with appropriate

statistically derived estimates of risk, for possible use in a knowledge transfer programme.



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7 DISCUSSION AND CONCLUSIONS

Review and reflection

The research programme reported here involved a very wide range of activities and,

as discussed in Chapter 1, it is recognised that from some perspectives this approach

to research is less academically satisfying than what might be termed ‘pure’ research,

in that it lacks a clear conceptual coherence and can give the impression of a ‘scatter-

gun’ approach. However, for the reasons set out earlier, for this particular topic it is

difficult to identify alternative ways in which it could have been pursued.

Hence, our research approach is best regarded as investigative research in that it relied

on a number of distinct RAs to build as comprehensive a set of information as

possible, from as wide a range of sources as possible. Further, it was structured to

provide a foundation for possible future research exploring the longer term impacts of

bTB (e.g. through the use of a standard questionnaire). Comprehensive reviews and

analyses of all existing data sources that could be identified as having potentially

useful information on the topic were carried out, while at the heart of the programme

was a series of focussed empirical studies of both economic and human health aspects

of a bTB breakdown.

The findings are set out and discussed at length in the earlier chapters, and in the

appendices, and it remains only to draw together the main findings. Bovine TB has

over the past decade or so become endemic in an increasing number of areas of

England and Wales which are characterised by high cattle populations associated with

primarily pastoral livestock farming systems. Not only have the short-term impacts

of a bTB breakdown of farm businesses been well established, but also the likely

existence of longer-term effects (Bennett, op cit; Sheppard and Turner, op cit; and

NAO Wales, op cit). This study has been carried out to provide more substantive

evidence on the nature, scale and incidence of the longer-term implications for farm

businesses. An important part of the research programme as a whole, though not an



162

integral part of the broad investigative research, has been the study by Reading

University of farmers’ WTP for a bTB vaccine.

The research has aimed to address five main objectives, in pursuance of the overall

aim of providing an evidence base on the longer-term effects of a bTB breakdown on

farm businesses, and so to inform policy development in the future control of bTB.

The two principal areas for concern were identified in earlier studies to be (a) the

economic effects within businesses that extend over a period of many months and

years, or even result in permanent changes (e.g. to the farm system); and (b) the

adverse effects on the people involved on the farm, including both health and social

effects.

The first objective was to review the existing evidence from a wide range of studies

of relevance to the study area, and including economic, socio-economic and medical

studies. Using economic principles as a framework the review defined longer-term in

the context of the present study to encompass both medium term and long term

impacts on farm resource use. Hence, a modification to the farming system (e.g. to

accommodate the constraints arising from livestock movement restrictions, one of the

bTB control measures), investment in bio-security measures to reduce the risk of

cattle infection, and giving up dairy or even cattle production are examples of the

longer-term adjustments which are the focus of the study. The empirical and desk

studies provide extensive evidence of the nature and scale of these adjustments.

The literature review also identified the themes which came to dominate the research:

aspects of farmers’ decision-making which cannot be explained adequately by

reference to conventional economic variables; the lack of evidence on the human

stress effects of a bTB breakdown, although other animal disease epidemics have

been well researched; the shaping of farmers’ attitudes to authorities by their

perceptions or risk and the consequences of animal disease; and a range of issues

around the responsibility of farmers to improve the bio-security on their farms, and

thereby contribute directly to the control of bTB. This brief overview will pick up

these themes.

Farmers’ decision-making was found to be influenced by bTB considerations on more

than a third of the farms visited as part of the interview survey, and in four out of five



163

cases where bTB had been a driver of past change it was listed as the most important.

However, it was almost always one factor among others, a finding that provides some

reassurance about the quality of farmers’ decision-making. It seems likely that the

identified role of bTB in driving strategic change on farm businesses goes at least part

way to explaining the inability of conventional studies of the economics of resource

use in dairying to fully capture the dynamics of structural change. This conclusion is

strengthened by the GIS-based analysis of parish-level change in cattle farming,

which found clear linkages between extraordinary structural changes and bTB

incidents.

Although the stress effects of other animal diseases has been reasonably well

researched, little specific attention has been given to the implications of a bTB

breakdown, and the GHQ-12 study provides objective evidence of the scale of these

effects for the first time. In general terms, a much greater proportion of farmers

exhibit signs of psychiatric morbidity that the population as a whole. Overall, the

highest stress levels are seen on farms which have been under livestock movement

restrictions for a long period. However, on dairy farms, where people are generally

more stressed than on beef farms, the greatest stressor is the loss of a large number of

cattle. There is evidence that bTB is associated with raised stress levels for people

other than the farmer, particularly spouses; but the sample was too small for reliable

results for farm workers. For farmers, a bTB breakdown causes significant stress:

this is highest for dairy farmers, especially where they have lost a large number of

cattle; while for both trading beef and suckler beef farmers, a long period under

restriction is the greater stressor.

One of the very interesting findings of this research study has been that farmers’

perceptions of the risk of a bTB breakdown may be well out of line with the objective

statistical probability, based on the very detailed parish-level bTB statistics now

available. This was highlighted as part of the WTP study conducted by Reading

University, which identified a cognitive dissonance between subjective and objective

assessments of risk. While this is an area that deserves further research, inasmuch as

it is the farmers’ subjective assessment of risk which drives decision-making at farm

level and also contributes to the stress experienced by farmers and others, both in the



164

wake of a specific bTB breakdown but probably also in the more generic context on

farms which are in, or close to, bTB ‘hotspot’ parishes.

Insurance is one of the classic tools for managing business risk, and the research

identified what appears to be an emerging problem in relation to the cost and the

level, even availability, of cover against a bTB breakdown. It is evident that the

spread of bTB has already caused revisions both to bTB insurance premiums and the

level of cover offered by insurers. This is clearly an adverse economic effect as far as

farm businesses are concerned, even if it appears inevitable in the context of the

spread of bTB. While the firms consulted remain committed to providing the best

terms to their clients, and being as fair as possible in the circumstances, their

experience is of rising claims. Further adjustments to premiums and cover, driven by

both claims experience and statistics on the incidence of bTB, cannot be ruled out for

the future.

Finally, the research sheds some further light on the sometimes contentious issue of

bio-security. The WTP study identifies the existence of a continuing gap in the

adoption by farmers of the full range of bio-security recommendations, while the

farmers’ interview survey identified the level of investment some of the sample

farmers had made in pursuit of improved bio-security. However, anecdotal evidence,

from this research and elsewhere, points to continuing uncertainty among farmers

about some bio-security measures, on the grounds or one or more of the following:

their practicality, their efficacy or their value-for-money. At the very least this

suggest that a concerted knowledge transfer programme on this issue is required but,

as Enticott (2008) points out, the effective introduction of new forms of disease

control (represented by extensions to bio-security) in the context of an erosion of trust

between the farming community and government raises important issues which need

to be addressed in a comprehensive way if progress is to be made in disease control.

Concluding summary

This research has focussed on the longer-term effects of a bTB breakdown and has

considerably extended the evidence base. There are two aspects of the research as a

whole that deserve final mention here: the identification of the key factors which are

associated with longer-term effects, and the exploration of farmers; WTP for a bTB



165

vaccine. The key factors were identified through studies of impact (a) within the

farm business and (b) on the people associated with the farm, while the WTP study

was essentially an independent RA.

First, the key factors analysis relied on distinguishing two distinct areas of impact:

those within the ambit of farm business economics, as identified by the effects on

strategic decision-making; and those directly affecting the people involved on the

farm, in terms of human mental health (psychiatric morbidity). Although the two

estimates were derived independently of each other, there is a very close fit between

them. Overall, the factor most likely to result in longer-term impacts is the loss of a

large number of cattle, closely followed by being under movement restrictions for a

long period; even a light brush with bTB has measurable effects on farm decision-

making on a significant minority of farms. However, for beef farms as a whole the

most stressful factor was being under movement restrictions for a long period. The

two sets of estimate are not mutually incompatible, of course, and it’s likely that if

sample sizes had been large enough to distinguish between beef suckler herds and

beef trading units further finessing of these findings would have resulted.

Second, the work undertaken by Reading University shows that cattle farmers have a

substantial WTP for a bTB cattle vaccine. Moreover, it finds that farmers primarily

value the ability of a vaccine to prevent a breakdown (or rather to reduce the

probability of a breakdown). However, farmers also recognise that a vaccine is

unlikely to be 100% efficacious and so they also value insurance backing that would

pay compensation to farmers should vaccinated cattle test bTB positive. Farmers

appear to value the ability of a vaccine to reduce the severity of a breakdown

somewhat less, and their prime concern is to prevent a bTB breakdown in the first

place. This is not surprising, given that some of the most intrusive consequences of

bTB, such as movement restrictions, re-testing etc. are incurred regardless of the

severity (i.e. number of reactors) of a breakdown. The broad comparability of the

two WTP estimates, from the CE and CV approaches respectively, provides some

reassurance of their robustness. The issue of risk perceptions deserves further study,

since farmers’ (subjective) perceptions of risk appear to be higher than the actual

(objective) risk that they face. This perception of risk was found to be positively

correlated with their WTP.



166

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171

APPENDICES

A Stakeholders’ consultation: overall summary

B Farmers’ interview survey: summary results

C Desk study using FBS data: summary results

D Postal survey: the GHQ-12

E The GIS study of changes in cattle populations

F The choice experiment bTB cattle vaccine questionnaire



172

Appendix A Stakeholder consultation: overall summary

Introduction

In order to identify the full range of claimed longer-term impacts on farm businesses

arising from bTB breakdowns, a range of organisations and farmer representatives

were consulted. ADAS consultants devised a short self-completion questionnaire

which respondents posted back. In total 56 questionnaires were sent out and 40 were

returned, a response rate of 71%. The views of those respondents who were

considered to have a direct interest (33) and of those who did not have an evident

direct interest (7) are considered separately.

Respondents with a direct interest

Economic Effects

The identification of adverse effects from a bTB breakdown commonly pointed to

farm incomes, reductions in capital value, the inhibition of business development and

the delaying, or abandonment, of plans for future business development.

The majority of respondents felt that there are often severe longer term financial

effects following a bTB breakdown, although the scale of these effects are dependant

on the duration and extent of any outbreak. While occasional breakdowns interrupt

trade in the short term, where animals are under restriction for a number of years this

has had longer term repercussions for many farm businesses. Farms may not be able

to trade stock effectively for years, and are unable to sell cattle in the open market.

Prolonged movement restrictions lead to an adverse cash flow, due to increased feed

requirements and problems with cattle welfare. Following the loss of breeding cattle,

and herd replacements, there is often disruption of calving patterns long into the

future, which has a clear financial impact.

In some cases where replacement cattle have been bought in, this has lead to the

inadvertent introduction and promotion of other diseases (such as rotavirus and

pneumonia) and this is then associated with increased veterinary costs in the



173

treatment of these. In the longer term there is very often an unwillingness to invest in

the business due to uncertainty about the future, and it was reported that many

farmers in this situation feel expansion of the business is going to be difficult.

Pre-movement testing was considered by some respondents to be a burden over the

longer term, and as a bTB control policy it does not appear to be working. The extra

costs such as fencing were considered by some respondents to be another long term

capital cost, while others viewed this as more of a short term item. Some of those

who commented on this felt it was extremely naïve to think that fencing alone would

stop badgers entering. Another major long term cost identified by some is the cost to

the taxpayer for compensating affected farms, although clearly this issue lies outside

the focus of the present study.

Social Effects

The majority of respondents highlighted the increased stress farmers feel when

directly affected by a bTB breakdown. Even when one outbreak clears up there is

still the worry that it will reoccur. Many identified bTB as being the ‘unknown

quantity’ with the on-going potential to cause stress: cattle farmers simply do not

know when something may happen and, in any case, feel powerless to prevent it. In a

number of cases, it was reported, this has led to high blood pressure, nervous

breakdown, strokes and even suicide. In some cases it appears to have been a

contributory factor in marriage breakdown.

Several stakeholder respondents identified the increased pressure on family members

to help out (some with family living near who may not normally work on the farm)

which is sometimes associated with a bTB breakdown. A longer term effect, it was

felt, is that younger family members may be discouraged from going into farming,

although this may also be a consequence of what is seen as political inaction. Within

the rural community, some stakeholders identified the increased workloads involved

in looking after more animals (a common consequence of the imposition of

movement restrictions following a bTB breakdown) can lead to reluctance, or

inability, to support community events.

Furthermore, it was reported that relationships between farmers and their neighbours

can become tense, particularly if it is perceived that one neighbour is not doing



174

enough to prevent the spread of the infection. One farmer stated that he felt very

guilty that when his farm was found to be infected with bTB as all the neighbouring

farms had to be tested, with all that implies for those farmers and their families. Also,

where a farmer has neighbours who come from a more urban background, or where

neighbours are very concerned with badger conservation, this can lead to fraught

relationships, where they have diametrically opposing views. Clearly, the potential

for increased levels of stress following a bTB breakdown is generated by a number of

possible causes.

Other effects

It was stated by some that, in the longer term, more farmers may decide not to keep

cattle, or some at least would change to other farming methods; and that additional

production and disease costs may well make the sector unviable. If this happens, it

was felt, it will have adverse effects on the environment and some suckler beef

systems, for example, have been moved from upland and hill land due to declining

economic returns. Some felt that the gradual depopulation of cattle in Wales will

result in a changing landscape and this could damage tourism. An imbalance in

ecosystems in the countryside due to the reduction in the numbers of cattle will lead

to reductions in wildlife such as ground nesting birds, hedgehogs etc.

Some respondents pointed to changes in (some) farmers’ attitudes to wildlife; in

particular, it was felt, attitudes to badgers have changed and farmers have become

very wary of the presence of these animals on their farms.

There was almost unanimous anger from this group of respondents that Defra had

taken no action to control bTB in wildlife, coupled with frustration that it seemed the

authorities were prepared to protect sick wildlife rather than cattle. One consequence

of this, it was felt by some, is that farmers are losing respect for authority and have

less confidence in wider issues of farm and rural policy development.

Some stakeholders felt that the press attention given to the levels of compensation for

cattle slaughtered may cause some degree of public backlash against farmers in the

future. It was felt by some respondents that there is little account taken of the

consequential loss to farmers associated with bTB.



175

Respondents with no direct interest

In broad terms, respondents without a direct interest held similar views to those with

a clear direct interest, and identified a similar range of factors.

Economic Effects

It was agreed by most respondents that the effects of bTB depend on the duration and

frequency of outbreaks and that it was also asserted that over time many businesses

do become unviable. It was thought by one stakeholder that in the long run beef

production will be reduced due to lack of control on bTB. It was noted that dairy

farmers are being forced to shoot Friesian bull calves at birth where there have been

long term breakdowns. Some stakeholders made a comparison of bTB policy

between the UK and elsewhere in Europe, where the objective was given as

eradication (of the disease).

Accountants in particular highlighted the trading losses and a significantly increased

borrowing requirement by some farmers, just in order to keep afloat following a

severe bTB outbreak. Again, the extra costs of keeping animals which would

otherwise have been sold and the need to rear more replacements were identified as

common economic problems for affected farm businesses. Fencing costs were

considered by these respondents to be essentially short term.





Social Effects

The effects highlighted by this group of stakeholders also included stress, in this case

identifying that associated with not being able to market animals at the appropriate

time, and generally adverse effects on both the mental health and physical well being

of farmers. It was also noted that where bankruptcy had occurred, by implication as a

result of a bTB breakdown, there could be more indirect effects on social well-being:

for example, a case where the farmers’ children felt unable to go on school trips.

Some of these stakeholders also highlighted the fact that, all too often, some local



176

community members did not understand the pressures on farm businesses, of which a

bTB breakdown often represented a considerable escalation.

Other effects





Summary

The stakeholder consultation reported here found a broad consistency between

respondents with a direct interest and those without. For clarity, it should be

understood that respondents are here identifying the range of effects of which they are

aware, rather than reporting on the typical incidence or likelihood of a particular

effect being evident. This study found that, in the views of these stakeholders, the

principal longer term effects of a bTB breakdown on farm businesses include:

• Financial effects: o reduced incomes, due to extra costs of holding unsold animals, extra

costs for testing, fencing

o interrupted calving patterns

o increased borrowing and bankruptcy

o less interest in business development

• Health effects: o Stress

o very severe problems such as heart trouble, stroke and suicide

o no time off/holiday

• Social effects: o marital breakdown

o deterioration of relationships with neighbours

o lack of participation in community events

o dependence on family members for help leads to extra family hardship

• Other effects: o loss of confidence in the industry and therefore no interest in

succession from family

o loss of confidence in the industry

o lack of confidence in authority on policy making

o effects on the environment and landscapes

o change in attitudes to wildlife



177

APPENDIX B

Farm interview survey: summary results

This contains the analysed data from the farmer’s interview survey, presented in a

form which mainly follows the format of the questionnaire itself. The survey

involved 152 farms, all of which had had at least one bTB breakdown during the ten

years prior to 2007, and was carried out between October and December 2007. Many

of the results are presented for a sub-group of the sample in addition to ‘all farms’.

The group labelled ‘bTB drivers’ comprise those farmers who cited bTB as a driver

of at least one strategic change made to their farm business over the previous ten

years; the group labelled ‘non bTB drivers’ are all other sample farmers. In total 57

farmers (37.5%) reported bTB as a driver of a major business change. For more

information on this see Section B following and also Chapter 1.

Section

A Background information about you and your farm

business

B A review of your business strategy

C Animal health control on your farm

D History of cattle disease on your farm

E Movement restrictions

F Other effects associated with the bTB breakdown

G Expected future impacts of bTB on the farm business

H Wider non-economic impacts of a bTB breakdown



178

SECTION A

BACKGROUND INFORMATION ABOUT YOU AND YOUR FARM BUSINESS

QUESTION 1

YOUR POSITION IN THE FARM BUSINESS

What is the legal structure of your business, and what is your position in the business?

Tick O

NE box only on each row

Sole trader

(Includes

F&S

partnerships)

Fam

ily

partnership

Other

partnership

Company

(Ltd or llc)

NA

Business

structure

39 (26%)

107 (70%)

2 (1%)

4 (3%)

Business

principal

Partner

(fam

ily

partnership)

Partner

(other)

Director

Manager

NA

Interviewee’s

position

47 (31%)

96 (63%)

3 (2%)

3 (2%)

2 (1%)

1 (1%)

QUESTION 2

YOUR AGE AND EDUCATIONAL BACKGROUND

(a)

In which age group are you?

Tick O

NE box only

Age group

Under 35

35 – 45

46 -55

56-65

66-75

75+

Respondents

4 (3%)

29 (19%)

56 (37%)

44 (29%)

14 (9%)

5 (3%)



179

(b)

What education and training have you had?

Tick all relevant boxes

Education category

Respondents*

Secondary school (to norm

al school-leaving age)

102 (67%)

GCSE. O levels or equivalent

51 (34%)

A-Levels or equivalent

12 (8%)

Further education (College study including day release, etc.)

46 (30%)

College/National Certificate/Diploma, etc.

42 (28%)

Degree

12 (8%)

Postgraduate qualification

1 (0.7%)

Apprenticeship

1 (0.7%)

Specialist post-experience training (e.g. sprayer use, etc.)

28 (18%)

Other (give details)

8 (5%)

Not completed secondary and no other education or training

4 (3%)

*Note, percentages add to more than 100% since more than one box could be ticked.



180

QUESTION 3

YOUR ATTITUDE TO FARMING

Which of the following statements best reflects your current attitude towards British farming?

Current attitude towards British farm

ing

All farm

s ‘bTB driver’

farm

s

‘Non bTB

driver’ farms

Farming has no future – I intend to give up

10 (7%)

8 (14%)

2 (2%)

Farming has a limited future – I need to diversify

9 (6%)

5 (9%)

4 (4%)

I see my future in farming and I want to increase the size of my farm

business

25 (16%)

8 (14%)

17 (18%)

I am

happy to stay farming as I am

now and for the foreseeable future

57 (38%)

15 (26%)

42 (44%)

I am

worried about my future in farming but I don’t know what else I can

do

22 (14%)

10 (18%)

12 (13%)

I see my future in farming but I expect that I will have to change my

farming practice

25 (16%)

8 (14%)

17 (18%)

Declined to answ

er

4 (3%)

3 (5%)

1 (1%)



181

QUESTION 4

YOUR FARMING VALUES AND OBJECTIVES

(a)

What is your level of agreem

ent with each of these statem

ents?

Strongly

agree

Agree

Disagree

Strongly

disagree

N/A

As a farmer, I am

a respected mem

ber of the local

community

All farms

19 (13%)

96 (63%)

34 (22%)

3 (2%)

‘bTB driver’ farms

4 (7%)

31 (54%)

20 (35%)

2 (4%)

Bad press has undermined farmers’ standing in the local

community

All farms

25 (16%)

78 (51%)

48 (32%)

1 (1%)


11 (19%)

32 (56%)

13 (23%)

1 (2%)

Local residents are not sympathetic to farmers and their

needs

All farms

13 (9%)

53 (35%)

81 (53%)

5 (3%)


5 (9%)

19 (33%)

33 (58%)

0 (0%)

Local Authorities do not understand farmers and their

farming needs

All farms

27 (17%)

76 (50%)

47 (31%)

1 (1%)

1 (1%)


11 (19%)

23 (40%)

23 (40%)

0 (0%)

Farmers should more actively promote farming interests

All farms

26 (17%)

100 (66%)

26 (17%)

0


6 (11%)

37 (65%)

14 (25%)

0 (0%)



182

(b)

What is your level of agreem

ent with each of these statem

ents?

Strongly

agree

Agree

Disagree

Strongly

disagree

NA

In running a farm as a business, maximising profit is

most important

All farms

28 (18%)

86 (57%)

36 (24%)

0

2 (1%)


1 (2%)

6 (11%)

33 (58%)

17 (30%)

Farmers should conserve/improve farm

landscape/habitats, regardless of profits

All farms

9 (6%)

63 (41%)

70 (46%)

9 (6%)

1 (1%)


2 (4%)

28 (49%)

24 (42%)

3 (5%)

Beyond earning a reasonable income, the main joy of

farming is the lifestyle

All farms

36 (23%)

98 (64%)

16 (11%)

1 (1%)

1 (1%)


11 (19%)

41 (72%)

5 (9%)

0 (0%)

The most important thing to me is to maintain an

attractive lifestyle for my fam

ily

All farms

22 (15%)

101 (66%)

26 (17%)

1 (1%)

2 (1%)


7 (12%)

41 (72%)

8 (14%)

1 (2%)

My objective is to ensure there is a viable business for

my successors when I retire

All farms

29 (19%)

73 (48%)

41 (27%)

7 (5%)

2 (1%)


5 (9%)

26 (46%)

22 (39%)

4 (7%)

Farming today depends on forces beyond farmers’

control, all they can do is to adjust to the situation

All farms

59 (39%)

82 (54%)

10 (6%)

1 (1%)


21 (37%)

33 (58%)

3 (5%)

0 (0%)



183

(c)

In relation to financial support to the agricultural industry under the CAP, to what extent do you agree or disagree with the following

statem

ent?

Strongly

agree

Agree

Disagree

Strongly

disagree

‘I believe that farmers should only be eligible to claim

the Single Payment if their farms meet the required

standards for cross-compliance’

All farms

10 (7%)

107 (70%)

30 (20%)

5 (3%)


3 (5%)

37 (65%)

16 (28%)

1 (2%)

QUESTION 5

PEOPLE W

HO W

ORK ON THE FARM

Who works on the farm, in what capacity, and for how much of their time (use per week or per annum as appropriate)?

Tim

e <1 FTE

1 FTE

2 FTEs

3 FTEs

4FTEs

>5 FTEs

Number of people

11 (7%)

47 (31%)

47 (31%)

29 (19%)

10 (7%)

8 (5%)

QUESTION 6

YOUR SUCCESSION PLANS

Definitely/

very likely

Possibly

Unlikely/

definitely

not

N/A

Do you expect a mem

ber of your family to take on the farm

business after you?

All farms

54 (35%)

38 (25%)

57 (38%)

3 (2%)


16 (28%)

13 (23%)

27 (47%)

1 (2%)



184

QUESTION 7

YOUR FARM BUSINESS

(a)

What is the tenure of the land you farm?

Total farm

ed area (hectares)

Less than 40

40 – 100

100 – 200

200 - 300

300 and above

Number of farms

17 (11%)

57 (38%)

50 (33%)

18 (11%)

10 (7%)

(b)

Have you made any significant changes to your business or income sources within the last 10 years?

n/a

Started

Increase

d

Decrease

d

Stopped

Started

&

stopped

No

change

Total farmed area

All farms

0%

38%

13%

0%

0%

49%


0%

0%

37%

21%

0%

0%

42%

Proportion of owner-occupied

land

All farms

1%

1%

23%

12%

0%

0%

63%


2%

0%

0%

32%

12%

0%

54%%

Number of farm enterprises

All farms

2%

6%

25%

0%

0%

67%


0%

2%

5%

37%

0%

56%

Size of cattle enterprise - dairy

All farms

45%

1%

28%

8%

7%

1%

11%


47%

0%

23%

14%

5%

0%

14%

Size of cattle enterprise - beef

All farms

13%

2%

31%

22%

7%

0%

24%


12%

2%

28%

32%

12%

0%

14%



185

Size of sheep enterprise

All farms

42%

0%

12%

14%

11%

1%

18%


44%

0%

12%

12%

16%

2%

14%

Size of other livestock

enterprise(s)

All farms

82%

3%

5%

2%

1%

0%

7%


82%

5%

2%

4%

2%

0%

5%

Size of arable enterprise

All farms

57%

2%

16%

3%

3%

1%

18%


54%

2%

25%

0%

7%

2%

11%

Size of other crop enterprise(s)

All farms

86%

1%

7%

1%

1%

1%

5%


89%

0%

7%

2%

0%

0%

2%

Numbers of people employed

full-time

All farms

18%

1%

7%

19%

3%

1%

51%


19%

2%

7%

30%

5%

0%

37%

Use of agricultural contractors

All farms

8%

1%

37%

7%

0%

0%

48%


4%

0%

39%

7%

0%

0%

51%

Own agricultural contracting

All farms

64%

0%

5%

7%

0%

1%

24%


54%

0%

5%

14%

0%

0%

26%

Diversified enterprises

All farms

74%

10%

6%

1%

1%

1%

8%


68%

12%

7%

2%

0%

0%

5%

Off-farm sources of income

All farms

62%

6%

11%

1%

1%

2%

18%


67%

12%

9%

0%

2%

0%

11%



186

(c)

Are there any important characteristics of your cattle enterprise(s) which either result in significant seasonal variations in cattle

number on the farm, and/or involve regular cattle movem

ents onto or off the farm?

Variations in cattle numbers

All farm

s ‘bTB driver’ farm

s ‘N

on bTB driver’

farm

s

Buying in store cattle - autumn

7 (5%)

2 (4%)

5 (5%)

Buying in store cattle - spring

6 (4%)

1 (2%)

5 (5%)

Heifer-rearing (e.g. dairy replacements)

14 (9%)

2 (4%)

12 (13%)

Calf-rearing (producing weaned calves)

19 (12%)

8 (14%)

11 (12%)


15 (10%)

5 (9%)

10 (11%)

None

84 (55%)

33 (58%)

51 (54%)

No reply

7 (5%)

(e)

Does, or did, your farming system

involve frequent movem

ent of cattle on and/or off the farm?

All farm

s Yes

51 (34%)

No

101 (66%)

(f)

Is the farm

ed area fragmented?

All farm

s Yes

61 (40%)

No

91(60%)



187

QUESTION 8

YOUR FARM ENTERPRISES

(a)

What are your approximate gross farm sales (average of the last three years)?

Average gross farm

sales

Mean

Median

Minim

um

Maxim

um

Number of

responses

Wouldn’t

say

All farm

s £155,366

£110,000

£30

£2,000,000

122

30

‘bTB driver’ farm

s £167,263

£138,000

£150

£850,000

45

12

‘Non bTB driver’ farm

s £148,412

£100,000

£30

£2,000,000

77

18

Average gross farm

sales

Less than

£10,000

£10,000 to

£50,000

£50,000 to

£100,000

£100,000 to

£200,000

£200,000 to

£500,000

£500,000 or

above

Wouldn’t

say

All farms

15 (12%)

22 (18%)

17 (14%)

37 (30%)

27 (22%)

4 (3%)

30 (20%)


3 (7%)

10 (22%)

4 (9%)

16 (36%)

10 (22%)

2 (4%)

12 (21%)

‘Non bTB driver’ farms

12 (16%)

12 (16%)

13 (17%)

21 (27%)

17 (22%)

2 (3%)

18 (19%)



188

(b)

Which enterprises do you have, and approximately what does each contribute to the farm’s gross sales? Do any of the enterprises

have organic or pedigree animals?

Group name

Dairy and Beef threshold %

of total enterprises

% of cases

Wholly or predominantly Dairy and Beef enterprises

85% or above

47%

Mainly Dairy and Beef enterprises

60% to 85%

24%

Mixed enterprises

40% to 60%

10%

Minority Dairy and Beef enterprises

15% to 40%

12%

Wholly or predominantly non-Dairy and Beef enterprises

Less than 15%

7%

All

farm

s

‘bTB

driver’

farm

s

‘Non

bTB

driver’

farm

s

% of

total

farm

s

Dairy organic

3

2

1

2%

Beef organic

7

5

2

5%

Other organic

5

2

3

3%

Dairy pedigree

28

14

14

18%

Beef pedigree

18

8

10

12%

c)

Do you make any sales of pedigree livestock in a typical year?

All farm

s Yes

19 (13%

)



189

QUESTION 9

BUSINESS PERFORMANCE AND INCOME

(a)

What is your net profit from farming (average of the last three years from your accounts)?

Net profit from

farm

ing

Made a

loss

£0 - £5,000

£5,001 -

£10,000

£10,001 -

£20,000

£20,001 -

£50,000

£50,001 -

£100,000

£100,001

or more

Wouldn’t

say

All farms

20 (13%)

34 (22%)

19 (12%)

26 (17%)

33 (22%)

8 (5%)

3 (2%)

9 (6%)


10 (18%)

12 (21%)

12 (9%)

5 (9%)

9 (16%)

1 (2%)

2 (4%)

6 (11%)


10 (11%)

22 (23%)

7 (7%)

21 (22%)

24 (25%)

7 (7%)

1 (3%)

3 (3%)

(b)

Within the farm fam

ily, are there any sources of income from outside farming?

Yes

No

All farms

78 (51%)

74 (49%)


28 (49%)

29 (51%)


50 (53%)

45 (47%)



190

(c)

If ‘yes’, what are the sources of this income, and when did they start?

Year started

Before 1980

1980-1989

1990-1999

Since 2000

‘bTB

driver’

farms

‘Non bTB

driver’

farms

‘bTB

driver’

farms

‘Non bTB

driver’

farms

‘bTB

driver’

farms

‘Non bTB

driver’

farms

‘bTB

driver’

farms

‘Non bTB

driver’

farms

Off-farm employment

2

3

1

3

5

8

11

8

Off-farm self em

ployment (including

other business interests)

8

3

0

2

2

2

8

3

Investment income

1

3

1

1

4

1

2

5

Pension income

0

0

0

1

0

8

2

1


0

2

2

1

0

3

3

7

(d)

Approximately, what proportion of your total family income comes from the various sources?

Aggregate %

of household income

(all farm

s)

Farm business activities (including diversification)

68%

Off-farm employment

16%

Off-farm self em

ployment (including other business

interests)

6%

Investment income

4%

Pension income

3%


3%

Other

0.1%



191

QUESTION 10

YOUR EXTERNAL BORROWINGS

(a)

Approximately, what is the current level of the total borrowing for your farm

business? W

hat proportion of your borrowings is

subject to variable, as distinct from fixed, interest rates?

External borrowings

£0

£1 -

£10,000

£10,001 -

£50,000

£50,001 -

£100,000

£100,001

or more

Wouldn’t

say

All farms

68 (45%)

15 (10%)

14 (9%)

15 (10%)

19 (13%)

21 (14%)


24 (42%)

6 (11%)

4 (7%)

5 (9%)

7 (12%)

11 (19%)


44 (46%)

9 (9%)

10 (11%)

10 (11%)

12 (13%)

10 (11%)

Proportion of borrowings

subject to variable interest

rates

0%

1%

-25%

26%

-50%

51%

-75%

Over 75%

Wouldn't

say

All farms

65 (43%)

2 (1%)

9 (6%)

12 (8%)

39 (26%)

25 (16%)


24 (42%)

1 (2%)

3 (5%)

4 (7%)

18 (32%)

7 (12%)


41 (43%)

1 (1%)

6 (6%)

8 (8%)

21 (22%)

18 (19%)



192

(b)

How do you regard this level of borrowing, given the present circumstances of your business?

View on level of borrowing

All farm

s ‘bTB

driver’

farm

s

‘Non bTB

driver’

farm

s

Comfortably low (i.e. no cause for concern)

40%

46%

37%

Too low for business growth (thinks should borrow more e.g. to invest for

improvem

ent, expansion)

3%

0%

5%

About right for business growth (sustainable, with a safety margin)

19%

18%

20%

Too high for comfort (believes the business is potentially vulnerable to an

unexpected shock)

20%

26%

17%


14%

9%

17%

NA

3%

2%

4%



193

(c)

In respect of the interviewee’s attitude to the current level of external borrowing, are there any comments regarding the reasons for

the current position (e.g. factors which have influenced borrowings now seen as too low, or too high)?

All farm

s ‘bTB driver’ farm

s ‘N

on bTB driver’

farm

s

Basic profitability of your business

48 cases (32%)

20 cases (35%)

28 cases (29%)

Future prospects for the industry

18 cases (12%)

4 cases (7%)

14 cases (15%)

Level and trend of interest rates

11 cases (7%)

4 cases (7%)

7 cases (7%)

The need for reinvestment (e.g. upgrading,

expanding)

25 cases (16%)

6 cases (11%)

19 cases (20%)

Perceptions of risk (e.g. problems with

disease, etc.)

13 cases (9%)

2 cases (4%)

11 cases (12%)


57 cases (38%)

28 cases (49%)

29 cases (31%)




194

SECTION B

A REVIEW OF YOUR BUSINESS STRATEGY

QUESTION 11

BUSINESS STRATEGY AND CHANGES

(a)

Thinking about your business planning decisions, please explain what changes you have made in the past TEN years (e.g. buying,

selling or letting land; extended existing buildings; built new

buildings or other infra-structure; introduced one or more new

enterprises;

abandoned one or more enterprises; expansion/reduction of enterprises including diversification change in key farm practices; major changes

to the machinery/equipment inventory; major changes to the use of technical/business advice; major changes to the financial structure of the

business)?

(b)

What are the reasons/drivers behind the changes you have already made (e.g. changing relative profitability, succession issues,

ageing

(c)

Thinking about your business planning decisions, please explain what changes you intend to make to your farm business over the

next TEN years (e.g. buying, selling or letting land; extended existing buildings; built new

buildings or other infra-structure; introduced one

or more new

enterprises; abandoned one or more enterprises; expansion/reduction of enterprises including diversification change in key farm

practices; major changes to the machinery/equipment inventory; major changes to the use of technical/business advice; major changes to the

financial structure of the business)?

(d)

What are the reasons/drivers behind the changes you intend to make (e.g. changing relative profitability, succession issues, ageing

infra-structure, rising input prices, poor prices in future for farm products, animal health issues, legislation, retirement, etc.)?



195

(a) bTB noted as a driver of past change (57 farm

s = ‘YES’, out of the total sample of 152 farm

s)

Summary

Change 1

Change 2

Change 3

Change 4

Change 5

First driver

13

22

11

9

0

Second driver

8

3

0

0

0

Third driver

0

2

1

0

0

Fourth driver

0

0

0

0

Notes:

1. On a few

farms bTB was cited as a driver for more than one change.

2. ‘Drivers’ of strategic change were ranked in order of importance by the farm

er

3. There is no particular significance in the order in which changes were listed.

Notes the m

ethodology used

Section B represents an attem

pt to avoid over-reporting the influence of bTB on farm business development and change. The briefing notes

to interviewers stated:

‘Ultimately, we are looking to generate information on whether bTB breakdowns cause cattle farmers either to make changes in their

farm businesses that otherwise would not have occurred, or fail to make changes that were planned. This is an important

consideration.

With this in mind, it is crucial that we avoid reference to bTB when asking farmers about the reasons why they have made changes,

or intend to do so to their business (at Question 7). There are many reasons why farmers will make changes to their business, and

we must avoid leading the farm

er to potentially exaggerating the impacts of bTB upon their business.

A problem unavoidably faced is not that farmers intend to deceive, but that they will rationalise their longer-term decisions as being

bTB related when, in fact, they were not. They m

ay do so simply because their recollection of events is misted by the passage of

time, or because (even subconsciously) they are m

aking a political point, viz. "I've had a hard time with bTB, and the government

simply doesn't understand how much farmers have suffered."’

See also the further discussion on this issue in Chapter 1.



196

QUESTION 12

PERCEIVED THREATS TO YOUR BUSINESS

Q12

What do you see as being the main threats your business (e.g. legislation, cash flow, animal health, market conditions, availability of

workers, etc.)?

Threat 1

Threat 2

Threat 3

Threat 4

Threat 5

None

10 (7%)

28 (18%)

84 (55%)

123 (81%)

140 (92%)

Regulations/legislation

38 (25%)

31 (20%)

11 (7%)

5 (3%)

3 (2%)

Market conditions (profitability)

31 (20%)

17 (11%)

5 (3%)

2 (1%)

1 (1%)

Animal health (bTB)

29 (19%)

23 (15%)

11 (7%)

3 (2%)

2 (1%)

Animal health (general)

13 (9%)

24 (16%)

13 (9%)

4 (3%)

4 (3%)

Cash flow

10 (7%)

12 (11%)

7 (5%)

5 (3%)

1 (1%)

Market structure (e.g. supermarkets)

9 (6%)

7 (5%)

4 (3%)

2 (1%)

0 (0%)

Labour availability

5 (3%)

4 (3%)

7 (5%)

7 (5%)

0 (0%)

Retirem

ent/succession/ill health

4 (3%)

3 (2%)

4 (3%)

0 (0%)

1 (1%)

Debt servicing

2 (1%)

3 (2%)

4 (3%)

0 (0%)

0 (0%)

Access to finance

1 (1%)

0 (0%)

0 (0%)

0 (0%)

0 (0%)

Other fam

ily issue

0 (0%)

0 (0%)

2 (1%)

1 (1%)

0 (0%)

Totals

152 (100%)

152 (100%)

152 (100%)

152 (100%)

152 (100%)



197

QUESTION 13 IMPACT OF HERD bTB BREAKDOWN

(a) From your responses, overall, your bTB breakdown does not appear to have been

a primary reason in your past changes/intended changes to your farm business. Is this

correct?

No reply Yes No

All farms 62 (41%) 84 (55%) 6 (4%)

‘bTB driver’ farms 52 (91%) 2 (4%) 3 (5%)

‘Non bTB driver’ farms 10 (11%) 82 (86%) 3 (3%)

Where ‘no’

(b) Please could we confirm your responses at Questions 11(b) and 11(d)?

Yes, confirmed No, need to revise

All farms 139 (91%) 13 (9%)

‘bTB driver’ farms 53 (93%) 4 (7%)

‘Non bTB driver’

farms

86 (91%) 9 (9%)

Where ‘yes’

(c) Please explain the reasons why bTB has not so far been considered a primary

factor in your business decisions?

All farms ‘bTB driver’

farms

‘Non bTB

driver’ farms

Breakdown too small to have a significant effect

on business strategy

45 (30%) 1 (2%) 44 (46%)

Farming system not too vulnerable to

consequences of a breakdown

32 (21%) 1 (2%) 31 (33%)

Identified factors more important drivers of

business strategy

23 (15%) 1 (9%) 22 (23%)



198

SECTION C ANIMAL HEALTH CONTROL ON YOUR FARM

QUESTION 14 RECENT BIOSECURITY IMPROVEMENTS

(a) In the past TEN years, have you introduced any new measures to help disease

prevention on your farm?

Yes 87 57% No 65 43%

(b) What changes have you made?

Pre - bTB Post - bTB Resources used

Bio-security measure Number % Number % Total cost (£)

Physical measures Cost per year (£)

Fence off identified wildlife

habitats, walkways, etc.

7 8% 17 20% £4550

Proof buildings, silage clamps,

etc. against wildlife

6 7% 14 16% £110,100

Raise height of feed and water

troughs

5 6% 21 24% £80,060

Double fence farm boundaries 10 11% 19 22% £69,191

Other 1 1% 9 10% £23,550

Other 2 2% 3 3% £2500

Livestock management Cost per year (£)

Herd health plan 29 33% 28 32% £11,090

Isolation of incoming cattle 13 15% 18 21% £2300

Pre-movement testing 4 5% 25 29% £4430

Separate personnel for separate

units

0 0% 2 2% £1600

Isolation of Reactors & Irs 5 6% 34 39% £5850

Stop spreading slurry on

grazing land

3 3% 6 7% £0

Use strip grazing with backing

fence

2 2% 0 0% £100

Other 1 1% 10 11% £18,700

Strategic

Closed herd 29 33% 22 25% £500

Reduced stocking rate 4 5% 13 15% £8,400

Specific sourcing of cattle 8 9% 23 26% £5,500

Other 1 1% 2 2% £0



199

SECTION D HISTORY OF CATTLE DISEASE ON YOUR FARM

QUESTION 15 CATTLE DISEASE EVENTS ON YOUR FARM

(a) Please describe all the major cattle disease events that you have experienced

%age of farms that experienced at least one occurerence

FMD BSE Leptospirosis IBR Johnes’

Disease

bTB BVD Blackleg Other

15% 15% 12% 10% 10% 99% 18% 2% 5%

bTB counts in each year

Year 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Counts 8 10 14 24 25 32 41 37 50 29 38

(b) Were there any long-term effects (i.e. lasting for beyond the first year after the

initial problem) from any of the above disease events?

bTB

Number of farms with a

tick

Loss of animals (includ. multiple events) 97%

Movement restrictions 88%

Cash flow impacts (record estimated change) 79%

Interrupted expansion (record estimated change from

plan)

16%

Other (give details) 11%

Other 4%



200

QUESTION 16 DISEASE COMPENSATION AND INSURANCE

(a) Did you originally insure against a bTB breakdown and any subsequent

breakdown?

bTB Event

1

Event

2

Event

3

Event

4

Event

5

Event

6

Not insured for

any bTB

breakdowns

Insured 32% 14% 7% 3% 2% 2% 68%

(c) Do you currently insure against a bTB breakdown?

Yes No

18% 82%

If not, why not?

bTB cover refuse by insurance company 10%

bTB insurance premium too high to justify 9%

Other 0%

No reply 81%

(h) How many of the reactors, if any, were pedigree stock?

bTB Event 1 Event 2 Event 3 Event 4 Event 5 Event 6

Aggregate number of pedigree

animals taken

232 122 97 29 10 9



201

(i) Do you have comments to make about the role of bTB insurance in reducing

business risk at farm level?

It’s a useful way to reduce business risk 28%

It’s never worth insuring against animal disease 12%

It’s always been too expensive to justify 42%

I’ve been unable to get insurance because of bTB history 33%

Since the bTB breakdown, insurance is too expensive 28%

Other 15%

Other 3%


QUESTION 17 ACTUAL USE OF FUNDS OBTAINED FROM bTB

COMPENSATION AND INSURANCE

(a) What did you use the funds from your compensation/insurance for?

Replacing livestock lost because of the bTB breakdown 53%



Investing in new farm machinery 2%

Investing in more land 0%

Investing in new/improved buildings/infra-structure 5%

Making other changes to the business (please specify) 3%

Increased drawings from the business (e.g. holiday) 3%

Cumulative amounts not significant 8%

Other 22%

Other 4%



202

(b) How important were the compensation payments for your bTB outbreak in

financing changes to your business?

Crucial (e.g. change(s) wouldn’t have happened without these funds) 5%

Very important (e.g. change(s) would have been much harder otherwise) 5%

Of marginal importance (e.g. not critical to the decision to fund change(s)) 7%

Not all at important (e.g. useful extra funds, didn’t affect decision at all) 2%

Not applicable 68%

(d) Were there any other financial considerations that you considered important in

deciding to make these changes?

Yes 4% No 73% No answer 23%



203

SECTION E MOVEMENT RESTRICTIONS

I’d like to ask you some questions regarding the impacts associated with the bTB

breakdown(s) of your herd. Please consider only the impacts directly associated with the

breakdown(s) and try to isolate them from other potential factors.

QUESTION 18 ANIMALS AFFECTED BY MOVEMENT RESTRICTION

(a) Were/are you affected by movement restrictions?

Yes 93% No 7%

Where ‘yes’, continue with Q18(b)

(b) What were/are the consequences of this?

Impacts on cattle numbers Tick all

that

apply

Financial importance (Scale 1 to 5)

1 2 3 4 5

Unable to sell calves when planned 52% 0% 14% 12% 16% 58%

Unable to sell store cattle when

planned

54% 3% 8% 8% 18% 64%

Unable to sell (young) breeding cattle

when planned

27% 0% 0% 16% 5% 79%

Unable to sell cull breeding cattle

when planned

26% 3% 19% 46% 14% 19%

Unable to sell finished cattle when

planned

15% 0% 0% 5% 33% 62%

Unable to buy calves when planned 16% 9% 9% 13% 17% 52%

Unable to buy store cattle when

planned

10% 0% 7% 14% 36% 43%

Unable to buy replacement breeding

cattle when planned

26% 3% 5% 0% 27% 65%

Impacts on feed costs

Additional keep purchased 29% 2% 12% 20% 27% 39%



204

Additional fodder purchased 44% 3% 13% 22% 33% 29%

Additional concentrate feed

purchased

61% 5% 13% 13% 26% 43%

Less keep purchased 0%

Less fodder purchased 1% 0% 100% 0% 0% 0%

Less concentrate feed purchased 4% 17% 0% 33% 50% 0%

Impacts on other costs

Higher labour costs (paid or unpaid) 58% 10% 14% 20% 30% 25%

Higher machinery/vehicle costs (e.g.

transport of feed, checking animals

etc.)

32% 4% 29% 36% 11% 20%

Higher ‘other livestock costs’ (e.g.

vet and med costs, livestock sundries,

etc.)

40% 11% 21% 40% 9% 19%

Higher overheads (e.g. electricity,

water, buildings, etc.)

31% 18% 25% 20% 16% 20%

Higher interest payments (i.e.

because of poorer cash flow)

30% 0% 0% 14% 19% 65%

Lower labour costs (paid or unpaid) 2% 0% 0% 67% 33% 0%

Lower machinery/vehicle costs (e.g.

transport of feed, checking animals

etc.)

1% 0% 100% 0% 0% 0%

Lower ‘other livestock costs’ (e.g. vet

and med costs, livestock sundries,

etc.)

1% 0% 100% 0% 0% 0%

Lower overheads (e.g. electricity,

water, buildings, etc.)

0%

Lower interest payments (i.e. because

of poorer cash flow)

0%

Other (please specify) 5% 0% 0% 14% 0% 29%



205

(c) Have you sent any cattle directly to the abattoir that you would have

otherwise sold?

Yes 40% No 60%

(d) Have you sent any cattle to market under special licence?

Yes 11% No 88% No reply 1%

(e) Have you lost specific sales contracts (such as, for example, M&S or

Waitrose) as a consequence of having a bTB breakdwon?

Yes 3% No 97%



206

SECTION F OTHER EFFECTS ASSOCIATED WITH THE bTB

BREAKDOWN

QUESTION 19 RESTOCKING POLICY

(a) Have you bought in breeding livestock to replace those lost because of the bTB

breakdown(s)?

Yes 45% No 55%

Where ‘yes’

(b) Have there been any wider or unexpected implications of introducing the

purchased replacements into the dairy or beef herd?

Adverse changes to the seasonality of milk production 14%

Adverse changes to the seasonality of beef production 20%

Adverse impact on the quality of the breeding herd 35%

Accidental introduction of other diseases into the herd 23%

Loss of pedigree status 1%

Disruption to herd equanimity (e.g. more bullying or victimisation) 10%

Overall reduction in milk yield or quality 14%

Beneficial changes to the seasonality of milk production 3%

Beneficial changes to the seasonality of beef production 0%

Beneficial impact on the quality of the breeding herd 7%

Overall improvement in milk yield or quality 1%

Other (please specify) 16%

(c) Have you ever been short of breeding/trading stock as a result of bTB losses and

consequent re-stocking restrictions, or because of difficulties in sourcing new stock?




207

QUESTION 20 EFFECTS ON MILK QUOTA

(a) What have been the effects of the bTB breakdown(s) on your milk production in

relation to your milk quota position?

No significant effect 30%

Production significantly under quota 13%

Production significantly over quota 3%

Effect has varied in different outbreaks 6%

Not applicable 45%

No reply 4%

QUESTION 21 EFFECTS ON SUBSIDIES

(a) Did the bTB breakdown(s) affect your Single Payment historic element?

Yes 14% No 86%

(a) Were there any significant effects on your business development plans?

Yes 36% No 64%

(b) What were these significant effects?

Previously planned expansion did not take place:











208

QUESTION 24 EFFECTS OF bTB ON NON AGRICULTURAL

ENTERPRISES

What effect, if any, has the bTB breakdown had on your non agricultural enterprises?

34% said “none” and 59% replied “not applicable”

QUESTION 25 BUSINESS STRATEGY TO MITIGATE IMPACTS OF bTB

(b) Is there any other business tactic or strategy you have used to reduce losses caused

by bTB?

Yes 36% No 64%

QUESTION 26 IMPACT OF bTB ON BUSINESS DECISIONS

Have you already taken any of the following decisions in response to difficulties caused

by your bTB breakdown?

Take out a loan or increase the overdraft to overcome losses/cash flow

difficulties

28%

Cancel or postpone investment in stock, premises or equipment 14%

Cancel or postpone expansion plans for the business 14%

Diversify into other or new lines of business 9%

Other (give details) 10%

None 53%



209

SECTION G EXPECTED FUTURE IMPACTS OF bTB ON THE

FARM BUSINESS

QUESTION 27 FUTURE IMPACTS OF THE bTB BREAKDOWN(S)

Looking to the future, what impacts do you envisage your past bTB breakdown(s) will

have upon the development of your FARMING SYSTEM?

(a) Do you envisage any significant effects in the future?

Yes 39% No 61%

(b) What significant effects do you expect?

Previously planned expansion now unlikely to happen/seriously delayed:









Still needing to replace lost breeding stock (purchased or home-reared) 38%

Adverse effect on herd expansion plans (e.g. because of smaller breeding herd) 20%

Adverse effect on business growth through:

- reduced cash flow (e.g. from smaller herd) 30%

- increased uncertainty about cattle production 30%


Positive effect on business growth because of compensation payments received 0%


- reduced cattle enterprise 13%

- get rid of cattle enterprise 13%

- expand cattle enterprise 5%

- expand other enterprise(s) (please specify) 5%



210

- reduce other enterprise(s) (please specify) 3%

- get rid of other enterprise(s) (please specify) 0%

Expect to reduce the size of farm business 3%

Expect to give up tenanted land 5%

Expect to sell land 3%

Expect to get out of farming 12%

Other (please specify) 20%

QUESTION 28 IMPACT OF bTB ON FUTURE BUSINESS DECISIONS

Are you considering having to take any of the following decisions in response to

difficulties caused by your bTB breakdown?

Tick all that apply

All

farms

‘bTB

driver’

farms

‘Non bTB

driver’

farms

Take out a loan or increase the overdraft to overcome

losses/cash flow difficulties

14% 16% 14%

Cancel or postpone investment in stock, premises or equipment 8% 11% 6%

Cancel or postpone expansion plans for the business 7% 7% 7%

Diversify into other or new lines of business 6% 7% 5%

Other (give details) 2% 0% 2%

None of the above 70% 59% 66%



211

QUESTION 29 EFFECTS OF bTB ON NON AGRICULTURAL

ENTERPRISES

Looking to the future, what effect, if any, do you envisage your past bTB breakdown(s)

will have on your NON-AGRICULTURAL ENTERPRISES?

Expect to change business system:

- Introduce new diversified enterprise(s) 1%

- Expand existing diversified enterprise(s) 2%

- Reduce existing diversified enterprise(s) 1%

- Get rid of existing diversified enterprise(s) 0%


None of the above 89%

QUESTION 30 BUSINESS STRATEGY TO MITIGATE IMPACTS OF bTB

Is there any other business tactic or strategy you are considering using to reduce losses

caused by bTB?

Yes No 100%

If ‘yes’, what are they?


- combination of agricultural enterprise(s)

- more enterprises 2%

- fewer enterprises 0%

- relative sizes of agricultural enterprise(s)

- cattle relatively less important 0%

- cattle relatively more important 0%




212

SECTION H WIDER NON-ECONOMIC IMPACTS OF A bTB

BREAKDOWN

QUESTION 31 IMPACTS OF A bTB BREAKDOWN ON PERSONAL HEALTH

AND SOCIAL WELL-BEING

Includes personal injury as well as social aspects, family & community relations, etc.

(a) Has the bTB breakdown affected your daily life in any way?

Yes 71% No 29%

(b) Has TB affected your family or household in any way?


(c) Has TB affected your employees in any way?


(d) Has bTB affected your community in any way?


(e) Have the views of the farming community towards you changed in any way

since your bTB breakdown?

Yes 10% No 90%

(f) Have the views of the local community towards you changed in any way since

your bTB breakdown?

Yes 7% No 93%



213

Appendix C Desk study using FBS data: summary results Table C1 FBS study: Dairy farms income indicators, 2002/03 to 2006/07

Table C2 FBS study: Dairy farms investment indicators, 2002/03 to 2006/07

Table C3 FBS study: Dairy farms liabilities and assets indicators, 2002/03 to 2006/07

Table C4 FBS study: Dairy farms technical indicators, 2002/03 to 2006/07

Table C5 FBS study: Suckler beef farms income indicators, 2002/03 to 2006/07

Table C6 FBS study: Suckler beef farms investment indicators, 2002/03 to 2006/07

Table C7 FBS study: Suckler beef farms liabilities and assets indicators, 2002/03 to 2006/07

Table C8 FBS study: Suckler beef farms technical indicators, 2002/03 to 2006/07



214

Table C1 FBS study: Dairy farms income indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Cash income per 100 cows

Control sample (all farms with no bTB breakdown) £35,617 £45,517 £50,102 £54,317 £49,583

Identical sample of bTB farms (incl. bTB

compensation) £35,355 £42,734 £41,927 £47,589 £36,916

Identical sample of bTB farms (excl. bTB

compensation) £30,554 £36,118 £38,369 £39,312 £36,023

bTB farms, long period of restriction (non-identical

sample, incl. bTB compensation) £35,175 £48,215 £48,316 £49,982 £36,674


sample, excl. bTB compensation) £30,643 £33,968 £44,275 £38,602 £34,987

FFI per 100 cows

No bTB cases £29,456 £38,506 £38,365 £39,734 £39,761

All bTB cases (with bTB compensation) £27,080 £43,508 £34,357 £33,132 £27,716

All bTB cases (without bTB compensation) £22,279 £36,893 £30,799 £24,856 £26,823

Long period of bTB restriction (with bTB

compensation) £27,944 £46,520 £36,303 £36,847 £29,847

Long period of bTB restriction (without bTB

compensation) £23,411 £32,273 £32,262 £25,466 £28,160

ONI per 100 cows

No bTB cases £17,171 £23,112 £24,861 £26,058 £21,511

All bTB cases (with bTB compensation) £17,727 £26,359 £23,846 £28,353 £15,858

All bTB cases (without bTB compensation) £12,926 £19,744 £20,289 £20,076 £14,964

Long period of bTB restriction (with bTB

compensation) £19,680 £33,120 £26,383 £29,720 £19,237

Long period of bTB restriction (without bTB

compensation) £15,148 £18,873 £22,342 £18,340 £17,551

NFI per 100 cows



compensation) £22,030 £27,519 £25,510 £30,146 £18,324


compensation) £17,229 £20,903 £23,652 £21,869 £17,431


sample, incl. bTB compensation) £22,048 £34,029 £25,555 £28,489 £18,643


sample, excl. bTB compensation) £17,515 £19,782 £21,514 £17,109 £16,956

M&II per 100 cows

Control sample (all farms with no bTB breakdown) -£10,475 -£6,501 -£5,343 -£8,994 -£11,607


compensation) -£3,808 -£1,435 -£5,459 -£7,767 -£15,457


compensation) -£8,609 -£8,050 -£9,016 -£16,044 -£16,350


sample, incl. bTB compensation) -£7,426 £915 -£8,267 -£7,285 -£9,610


sample, excl. bTB compensation) -£11,958 -£13,332 -£12,307 -£18,665 -£11,297

Farmer and spouse off farm income (per farm)


Identical sample of bTB farms £7,185 £6,886 £10,078 £7,924 £8,959


sample) £5,202 £5,016 £7,430 £8,064 £6,845



215

Table C2 FBS study: Dairy farms investment indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Capital expenditure on land and buildings per

100 cows




sample) £4,416 £13,443 £7,045 £7,069 £8,769

Capital expenditure on buildings per 100 cows




sample) £4,101 £6,105 £5,246 £4,965 £8,952

Asset purchases per 100 cows




sample) £20,780 £39,475 £19,503 £28,056 £23,078

Asset sales per 100 cows




sample) £2,414 £5,324 £3,433 £4,064 £2,593

Net asset purchases per 100 cows




sample) £18,366 £34,151 £16,071 £23,992 £20,484

Table C3 FBS study: Dairy farms liabilities and assets indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Total loans per 100 cows




sample) £73,418 £80,327 £58,121 £67,424 £78,729

Total liabilities per 100 cows




sample) £124,743 £127,726 £96,758 £124,353 £139,780

Total assets per 100 cows




sample) £614,605 £695,174 £637,377 £648,895 £675,642

Net worth per 100 cows




sample) £504,239 £567,448 £540,619 £533,651 £516,360



216

Table C4a FBS study: Dairy farms technical indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Milk production per 100 cows

Control sample (all farms with no bTB breakdown) 6211 6379 6316 6461 6818

Identical sample of bTB farms 6298 6048 6465 6448 6302


sample) 6206 5896 6218 6387 6549

Farmer and spouse AWU

Control sample (all farms with no bTB breakdown) 2.21 2.11 2.04 2.12 2.00

Identical sample of bTB farms 2.08 2.27 2.15 2.42 2.11


sample) 2.31 2.56 2.34 2.31 1.79

UAA

Control sample (all farms with no bTB breakdown) 81.76 81.43 83.08 84.97 91.31

Identical sample of bTB farms 86.21 77.58 84.53 84.04 92.81


sample) 90.02 74.44 86.43 92.89 116.49

Farmed area

Control sample (all farms with no bTB breakdown) Total 81.76 81.43 83.08 84.97 91.31

Tenant. 24.49 25.63 25.74 24.57 31.13

Identical sample of bTB farms Total 86.21 77.58 84.53 84.04 92.81

Tenant. 41.54 36.40 38.04 38.39 42.86


sample) Total 90.02 74.44 86.43 92.89 116.49

Tenant. 28.20 26.10 31.51 34.88 44.62

Table C4b FBS study: Dairy farms technical indicators, 2002/03 to 2006/07

Number of cattle

Control sample (all farms with no bTB

breakdown) Dairy cows 93.24 93.46 95.10 90.87 101.60

Total cattle 175.20 172.32 176.93 169.80 193.15

Identical sample of bTB farms Dairy cows 100.91 92.83 128.59 104.15 114.18

Total cattle 200.35 178.51 240.47 204.05 226.63


(non-identical sample) Dairy cows 105.89 89.68 104.02 108.21 144.67

Total cattle 218.46 176.01 205.14 225.45 297.58

Farm output per 100 cows


breakdown) £153,757 £167,191 £171,649 £183,013 £190,585


incl. bTB

compens. £156,268 £163,830 £167,100 £184,017 £179,599


excl. bTB

compens. £151,467 £157,214 £163,542 £175,741 £178,706


(non-identical sample)

incl. bTB

compens. £156,290 £163,580 £162,791 £178,033 £181,207


(non-identical sample)

excl. bTB

compens. £151,758 £149,333 £158,750 £166,653 £179,520



217

Table C5 FBS study: Suckler beef farms income indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Cash income per 50 cows


All bTB farms (incl. bTB compensation) £28,842 £41,150 £32,173 £37,115 £30,812

All bTB farms (excl. bTB compensation) £27,544 £38,905 £28,938 £26,539 £30,281

bTB farms, long period of restriction ( incl. bTB

compensation) £25,783 £32,718 £21,442 £53,123 £31,799

bTB farms, long period of restriction (excl. bTB

compensation) £24,550 £29,880 £19,569 £34,305 £30,727

FFI per 50 cows





compensation) £24,484 £33,710 £16,758 £40,863 £24,586


compensation) £23,251 £30,871 £14,885 £22,045 £23,514

ONI per 50 cows





compensation) £5,789 £13,275 -£7,720 £30,921 £12,506


compensation) £4,556 £10,436 -£9,592 £12,103 £11,434

NFI per 50 cows





compensation) £188 £8,808 -£10,195 £26,577 £9,594


compensation) -£1,045 £5,969 -£12,068 £7,759 £8,523

M&II per 50 cows

Control sample (all farms with no bTB breakdown) -£7,707 -£8,957 -£12,195 -£21,418 -£31,661

All bTB farms (incl. bTB compensation) -£14,133 -£3,821 -£29,417 -£8,396 -£23,351

All bTB farms (excl. bTB compensation) -£15,432 -£6,066 -£32,651 -£18,972 -£23,882


compensation) -£26,075 -£16,582 -£47,564 £2,384 -£16,917


compensation) -£27,308 -£19,421 -£49,436 -£16,434 -£17,988

Farmer and spouse off farm income per farm


All bTB farms £7,130 £10,555 £9,519 £6,663 £7,690

bTB farms, long period of restriction £5,913 £10,057 £11,196 £6,365 £7,390



218

Table C6 FBS study: Suckler beef farms investment indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Capital expenditure on land and buildings per 50

cows

Control sample (all farms with no bTB breakdown) £18,192 £9,928 £2,072 -£5,193 £6,298

All bTB farms £84,114 £3,105 £6,191 £9,815 £3,486

bTB farms, long period of restriction £1,478 £3,220 £4,101 £19,172 -£12,586

Capital expenditure on buildings per 50 cows




sample) £4,101 £6,105 £5,246 £4,965 £8,952

Asset purchases per 50 cows


All bTB farms £100,982 £25,567 £19,096 £27,279 £21,013


Asset sales per 50 cows


All bTB farms £4,765 £9,082 £2,666 £3,698 £9,368

bTB farms, long period of restriction £2,115 £10,788 £582 £2,042 £15,289

Net asset purchases per 50 cows


All bTB farms £96,217 £16,485 £16,430 £23,581 £11,645

bTB farms, long period of restriction £12,569 £13,208 £9,867 £40,071 -£3,958



219

Table C7 FBS study: Suckler beef farms liabilities and assets indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Total loans per 50 cows


breakdown) £91,773 £98,772 £42,866 £35,634 £48,806

All bTB farms £41,697 £57,072 £43,406 £60,569 £49,531


Total liabilities per 50 cows


breakdown) £135,585 £124,765 £69,593 £60,973 £95,531

All bTB farms £71,746 £90,604 £62,562 £79,673 £81,881


Total assets per 50 cows


breakdown) £1,148,126 £989,685 £881,149 £974,986 £1,095,210

All bTB farms £788,697 £796,042 £653,448 £836,182 £776,382


Net worth per 50 cows


breakdown) £1,012,541 £864,921 £811,556 £914,013 £1,014,165

All bTB farms £716,951 £705,437 £590,886 £756,509 £694,501




220

Table C8 FBS study: Suckler beef farms technical indicators, 2002/03 to 2006/07

2002-03 2003-04 2004-05 2005-06 2006-07

Farmer and spouse AWU

Control sample (all farms with no

bTB breakdown) 3.60 2.71 2.70 2.72 2.79

All bTB farms 2.75 2.24 2.46 1.74 2.07

bTB farms, long period of restriction 2.29 1.98 2.79 1.67 1.82

UAA


bTB breakdown) 151.18 132.50 126.85 125.26 119.19

All bTB farms 113.20 110.90 99.69 109.02 104.11

bTB farms, long period of restriction 106.02 96.07 92.26 115.84 116.03

Farmed area


bTB breakdown) Total area 158.11 138.81 134.09 129.02 122.48

Tenanted area 63.31 47.13 41.72 43.71 45.19

All bTB farms Total area 116.65 112.90 107.33 119.20 110.13

Tenanted area 29.62 28.83 20.78 13.98 32.01

bTB farms, long period of restriction Total area 107.26 92.10 97.19 121.18 120.92

Tenanted area 19.43 15.64 13.18 11.49 37.00

Number of cattle


bTB breakdown) Beef cows 41.11 42.03 38.42 38.06 37.12

Total cattle 107.37 107.75 101.35 100.34 100.08

All bTB farms Beef cows 40.47 42.28 41.15 49.70 42.68

Total cattle 109.75 113.95 108.48 134.37 120.85

bTB farms, long period of restriction Beef cows 44.75 43.19 39.17 50.72 51.56

Total cattle 123.49 107.67 101.09 136.64 134.89

Farm output per 50 cows


bTB breakdown) £168,871 £141,722 £140,507 £135,729 £143,894

All bTB farms

incl. bTB

compensation £121,799 £133,126 £100,786 £99,345 £97,412

All bTB farms

excl. bTB

compensation £120,501 £130,881 £97,552 £88,769 £96,881


incl. bTB

compensation £86,512 £90,530 £97,611 £124,071 £91,473


excl. bTB

compensation £85,279 £87,692 £95,739 £105,253 £90,402



221

Appendix D Postal survey: the GHQ-12 questionnaire



222

[University of Exeter logo]

Which of the following best describes you? (please tick one only)

A. I am the farmer

B. I am the farmer’s spouse/partner

C. I am a member of the farmer’s family (over 18)

D. I am an employee working on the farm

The General Health Questionnaire

(Copyright David Goldberg, 1978, NFER – Nelson)

Please read this carefully

We would like to know if you have had any medical complaints and how your health has

been in general, over the last few weeks. Please answer ALL the questions by ticking the

box beneath the response which you think most nearly applies to you. Remember that we

want to know about present and recent complaints, not those that you had in the past.

It is important that you try to answer ALL the questions

HAVE YOU RECENTLY:

1. been able to concentrate on whatever you are doing?

Better than usual

Same as usual

Less than usual

Much less than usual

2. lost much sleep over worry?

Not at all

No more than usual

Rather more than usual

Much more than usual

3. felt you are playing a useful part in things?

More so than usual

Same as usual

Less useful than usual

Much less useful

4. felt capable of making decisions about things?

More so than usual

Same as usual

Less so than usual

Much less capable



223

General Health Questionnaire

Continued

5. felt constantly under strain?

Not at all

No more than usual



6. felt you couldn’t overcome your difficulties?

Not at all

No more than usual



7. been able to enjoy your normal day-to-day activities?

More so than usual

Same as usual

Less so than usual


8. been able to face up to your problems?

More so than usual

Same as usual

Less able than usual

Much less able

9. been feeling unhappy and depressed?

Not at all

No more than usual



10. been losing confidence in yourself?

Not at all

No more than usual



11. been thinking of yourself as a worthless person?

Not at all

No more than usual



12. been feeling reasonably happy all things considered?

More so than usual

About the same as usual

Less so than usual


THANK YOU FOR YOUR CO-OPERATION



224

Appendix E The GIS study of changes in cattle populations

Background to the study

One possible longer term cost of bTB and its control is that cattle farmers give up cattle

production or switch to less profitable cattle systems (for example, moving from dairying

to beef). The objective of the work done for this part of the research study was to

examine the evidence relating to temporal changes in cattle populations (as recorded by

Defra’s June Census/survey) to see whether there is a connection between bTB endemic

areas and relative divergence in trend changes in cattle populations over the longer term.

This work aimed to examine the available data to establish the existence, or otherwise of

dissonance in cattle population trends for ‘bTB endemic’ areas and ‘bTB-free’ areas.

ADAS holds 1km² spatial data sets for the June census for England for 1970, 1980, 1995,

2000 and 2004. For this work the parish testing interval was used as an indicator of bTB

risk and this information was integrated with ADAS’ June census database to determine

the existence and strength of any correlations, within both regions and sub-regions,

between changes in cattle populations and the testing interval. Where data allows, an

investigation into the number of cattle holdings was also carried out, determining trends

in farm types within affected areas. This short report presents the output of this work,

with tabulated summaries presenting the statistical results. This research activity was led

and undertaken by ADAS UK Ltd.

Study methodology

This study relied on a number of distinct data sources which included information

available from the Agricultural Census, the VetNet database and spatial data. The data

actually used in each case is summarised below.



225

Agricultural Census data

Agricultural census data for England mapped to 1km x 1km squares were used to

calculate the numbers of dairy females, beef females and total cattle per parish for each

census year included in the scope of the study (1970, 1980, 1995, 2000 and 2004).

VetNet data

VetNet data (complete from 1988 onwards) was provided by Defra for every farm in the

UK. The data that was extracted from this database for use by this project were the

number of bTB tests performed on a herd and the dates and types of these tests; the

number of bTB breakdowns that a herd experiences and the dates of these breakdowns,

and the XY coordinates of the holding. These data were used to provide a parish

incidence of bTB for the four years prior to and including each census year (from the

1995 census onwards). The four yearly incidence was used as this was the maximum

period that could be used without having overlapping years in the incidence calculations.

Also, the minimum testing interval is four years, therefore every farm should have had at

least one bTB test within this period. VetNet data was also used to calculate the number

of cattle farms in each parish during each four-year period as this variable was not

available from the census data.

Spatial data

The locations of UK farms from the VetNet database were plotted and the numbers of

routine herd tests and breakdowns per parish (using 2004 boundaries) was calculated.

Four-year incidences were then calculated for each parish by dividing the number of

breakdowns by the total number of routine herd tests. Regional boundaries were used to

select parishes within three areas with extensive bTB hotspots (the bTB endemic

regions), these being the South-West, West-Midlands and Wales. Parish IDs, along with

the numbers of cattle, tests and breakdowns, and the four-year bTB incidence were

exported for each region for input into the statistical software.



226

Analytical approaches

Complete testing records were not available from the VetNet database before 1988 (and

the parish testing interval was only recorded from 1998), therefore the effects of bTB on

cattle populations were assessed over the period 1992-2004, using census data from 1995,

2000 and 2004. The analyses controlled for starting cattle numbers taken from the 1980

census. Parishes were allocated to bTB incidence categories of either 0 or 1, defined as

follows:

Category 0 – the ‘no bTB group’, comprised parishes which (a) had had no incidences

of bTB during the four years prior to each census year from 1995 until 2004, and (b)

where at least five routine herd tests had been performed within the parish during each

four-year period.

Category 1 – the ‘bTB group’, comprised parishes in which bTB had occurred, and two

sets of analyses were completed for this group:

1. For the first set of analyses, parishes were included in Category 1 if their

bTB incidence in each of the four-year periods was above the median value of all

parishes with a bTB incidence in the four-year period prior to 1995. This group

was chosen to maximise the differences between the two categories (‘bTB’and

‘non-bTB’), albeit at the expense of reducing sample size in Category 1.

2. A second set of analyses were performed using the less rigorous

requirement for inclusion in Category 1, which for these analyses included parishes

where there had been at least one bTB incidence in all four-year periods.

The results presented here use the first analytical method (bTB incidence above the

median value) for the South-West, which has a higher overall incidence of bTB; and the

second analytical method (of at least one bTB incidence) for both the West Midlands and

Wales. Results were qualitatively similar between the two sets of analyses.

Outcome variables were (i) the total number of cattle per parish; (ii) the number of dairy

females per parish; (iii) the number of beef females per parish, (iv) the ratio of dairy



227

females: beef females and (v) the total number of cattle farms per parish. Each of these

were analysed separately. For analytical purposes, all of the outcome variables were log-

transformed so that they were approximately normally distributed.

Since this is a longitudinal study, the data were analysed using General Estimating

Equations (GEE) for panel data, which are similar to General Linear Models (GLM) but

allow specification of a covariance structure that assumes observations are related to their

own past values through an autoregressive process. An autoregressive correlation

structure indicates that two observations taken close in time within an individual (parish)

tend to be more correlated than two observations taken far apart in time from the same

individual. The explanatory variables used in the models were the incidence category of

either 0 or 1 and the census year, whilst controlling for the number of bTB tests and the

value of the outcome variable in the base year (1980). If both the incidence category and

the census year had a significant effect on the outcome, then the interaction between the

two was tested. Statistical analyses were performed in Stata (v.10).

These analyses were carried out for each of the three identified bTB endemic areas – the

West Midlands, the South West and Wales – and for all three areas combined.

Analytical results: the West Midlands

The Category 1 cut-off parish incidence used for the West Midlands was at least one bTB

incidence (N=41). The median parish incidence in Category 1 was 0.10 (95% C.I. 0.06-

0.13) for the four years up to 1995; 0.20 (95% C.I. 0.10-0.27) for the four years up to

2000 and 0.37 (95% C.I. 0.27-0.42) for the four years up to 2004.

Total numbers of cattle

The mean numbers of all cattle in each incidence category for the West Midlands from

the census data are shown in Table E1.



228

Table E1 Mean numbers of cattle in each incidence category in the West Midlands for each census year from 1980

Incidence Category 0 Incidence Category 1

Year Mean number Std. Dev. N Mean number Std. Dev. N

1980 1298 669 81 2002 1591 41

1995 1100 640 81 1789 1509 41

2000 1040 634 81 1707 1521 41

2004 973 649 81 1600 1373 41

Longitudinal data analysis using GEE showed that overall the log total number of cattle

decreased from 1995 to 2004 (b=-0.150, P<0.001) when controlling for incidence

category and 1980 cattle numbers, but there was no effect of incidence category on log

total cattle numbers when controlling for year and 1980 counts (b=0.047, P=0.518).

Numbers of dairy females

The mean numbers of dairy females in each incidence category for the West Midlands are

shown in Table E2.

Table E2 Mean numbers of dairy females in each incidence category in the West Midlands for each census year from 1980



1980 480 333 81 722 880 41

1995 396 325 81 643 828 41

2000 428 370 81 698 951 41

2004 384 378 81 623 830 41

Overall, the log number of dairy females decreased from 1995 to 2004 (b=-0.112,

P=0.001) when controlling for incidence category and 1980 cattle numbers, but there was

no effect of incidence category on log number of dairy females when controlling for year

and 1980 counts (b=-0.008, P=0.946).



229

Numbers of beef females

The mean numbers of beef females in each incidence category for the West Midlands are

shown in Table E3.

Table E3 Mean numbers of beef females in each incidence category in the West Midlands for each census year from 1980



1980 84 65 81 156 109 41

1995 123 95 81 220 138 41

2000 139 99 81 245 162 41

2004 137 92 81 241 140 41

Overall, the log number of beef females increased from 1995 to 2004 (b=0.125, P<0.001)

when controlling for incidence category and 1980 cattle numbers. The log number of

beef females was significantly greater in parishes that had been affected by bTB since

1995 when controlling for year and 1980 counts (b=0.158, P=0.038). The incidence

category group by census year interaction was not significant (b=-0.005, P=0.534).

Ratio of dairy to beef females

The mean ratios of dairy to beef females in each incidence category for the West

Midlands are shown in Table E4.

Table E4 Mean ratios of dairy to beef females in each incidence category in the West Midlands for each census year from 1980


Year Mean ratio Std. Dev. N Mean ratio Std. Dev. N

1980 7.76 5.43 81 5.79 6.54 41

1995 4.08 3.01 81 3.08 2.95 41

2000 3.61 2.60 81 3.27 4.76 41

2004 3.22 2.66 81 2.78 4.00 41



230

Overall, the log ratio of dairy to beef females decreased from 1995 to 2004 (b=-0.239,

P<0.001) when controlling for incidence category and 1980 ratios, but there was no effect

of incidence category on the log ratio when controlling for year and 1980 ratios (b=-

0.018, P=0.875).

Total numbers of cattle farms

The mean numbers of cattle farms in each incidence category for the West Midlands are

shown in Table E5. As the number of cattle farms for each census year was derived from

VetNet data, there are no figures for 1980.

Table E5 Mean numbers of cattle farms in each incidence category in the West Midlands for each census year from 1995



1995 10.06 5.95 81 16.61 12.87 41

2000 9.63 5.52 81 17.02 11.97 41

2004 7.86 4.71 81 13.80 10.63 41

Overall, the log number of cattle farms decreased from 1995 to 2004 (b=-0.223, P<0.001)

when controlling for incidence category. There was no effect of incidence category on

the log number of cattle farms per parish when controlling for census year (b=0.099,

P=0.372).

Analytical results: the South West

The Category 1 cut-off parish incidence that was used for the South West was the more

rigorous approach of only including those parishes with a bTB incidence rate greater than

the median value of 0.07. The median parish incidence in the restricted Category 1 group

was 0.12 (95% C.I. 0.11-0.14) for the four years up to 1995; 0.15 (95% C.I. 0.13-0.18)

for the four years up to 2000 and 0.38 (95% C.I. 0.31-0.43) for the four years up to 2004.



231


The mean numbers of all cattle in each incidence category for the South West from the

census data are shown in Table E6.

Table E6 Mean numbers of cattle in each incidence category in the South West for each census year from 1980



1980 1062 601 91 1482 1047 121

1995 919 530 91 1411 1174 121

2000 839 481 91 1286 1090 121

2004 763 446 91 1194 1045 121



category and 1980 cattle numbers. There were a greater log number of cattle in parishes

in incidence Category 1 when controlling for year and 1980 counts, which was

marginally significant (b=0.038, P=0.064). There were no significant interaction effects

between incidence category and year (b=-0.001, P=0.775).


The mean numbers of dairy females in each incidence category for the South West are

shown in Table E7.



232

Table E7 Mean numbers of dairy females in each incidence category in the South West for each census year from 1980



1980 492 359 91 546 422 121

1995 400 306 91 475 430 121

2000 396 305 91 479 471 121

2004 342 268 91 423 446 121


P<0.001) when controlling for incidence category and 1980 cattle numbers. There was

no effect of incidence category on log number of dairy females when controlling for year

and 1980 counts (b=0.058, P=0.374).


The mean numbers of beef females in each incidence category for the South West are

shown in Table E8.

Table E8 Numbers of beef females in each incidence category in the South West for each census year from 1980



1980 48 37 91 114 197 121

1995 78 49 91 179 318 121

2000 98 67 91 206 316 121

2004 100 68 91 202 307 121



beef females was significantly greater in parishes that were in incidence Category 1 when



233

controlling for year and 1980 counts (b=0.146, P=0.047). The incidence category group

by census year interaction was not significant (b=-0.006, P=0.161).


The mean ratios of dairy to beef females in each incidence category for the South West

are shown in Table E9.

Table E9 Ratios of dairy to beef females in each incidence category in the South West for each census year from 1980



1980 13.76 9.88 91 11.09 9.04 121

1995 6.19 4.38 91 5.10 3.73 121

2000 5.15 4.55 91 4.32 3.30 121

2004 4.25 3.85 91 3.72 2.83 121


P<0.001) when controlling for incidence category and 1980 ratios. There was no effect

of incidence category on the log ratio when controlling for year and 1980 ratio (b=-0.069,

P=0.563).


The mean numbers of cattle farms in each incidence category for the South West are

shown in Table E10. As the number of cattle farms for each census year was derived

from VetNet data, there are no figures for 1980.



234

Table E10 Numbers of cattle farms in each incidence category in the South West for each census year from 1995



1995 8.14 4.61 91 14.25 11.01 121

2000 7.51 4.06 91 13.46 10.07 121

2004 6.12 3.37 91 9.06 6.56 121

Overall, the log number of cattle farms decreased from 1995 to 2004 (b=-0.373,

P<0.001) when controlling for incidence category. ,The log number of cattle farms was

significantly greater in parishes that were in incidence Category 1 when controlling for

census year (b=0.402, P<0.001). ,The incidence category by year interaction was also

significant (b=-0.017, P=0.005), indicating that the log number of cattle farms decreased

at a faster rate in incidence Category 1 compared to incidence Category 0.

Analytical results: Wales

The Category 1 cut-off parish incidence used for Wales was at least one bTB incidence.

The median parish incidence in Category 1 was 0.06 (95% C.I. 0.04-0.14) for the four

years up to 1995; 0.11 (95% C.I. 0.09-0.14) for the four years up to 2000 and 0.23 (95%

C.I. 0.17-0.32) for the four years up to 2004.


The mean numbers of all cattle in each incidence category for Wales from the census data

are shown in Table E11.



235

Table E11 Mean numbers of cattle in each incidence category in Wales for each census year from 1980



1980 1284 810 185 3032 1896 75

1995 1202 774 185 3032 1992 75

2000 1165 783 185 2987 1992 75

2004 1175 764 185 2943 1995 75


decreased from 1995 to 2004 (b=-0.040, P=0.003) when controlling for incidence

category and 1980 cattle numbers. There was a marginal positive effect of incidence

category on the log number of cattle when controlling for year and 1980 counts (b=0.107,

P=0.065). The interaction term between census year and incidence category was not

significant.


The mean numbers of dairy females in each incidence category for Wales are shown in

Table E12.

Table E12 Mean numbers of dairy females in each incidence category in Wales for each census year from 1980



1980 305 292 185 1250 233 75

1995 252 262 185 1121 870 75

2000 266 297 185 1308 1025 75

2004 254 277 185 1234 1001 75

Overall, the log number of dairy females remained the same from 1995 to 2004 (b=-

0.045, P=0.258) when controlling for incidence category and 1980 cattle numbers. There



236

was a greater mean log number of dairy females in parishes in incidence Category 1

when controlling for year and 1980 counts (b=0.383, P=0.017).


The mean numbers of beef females in each incidence category for Wales are shown in

Table E13.

Table E13 Numbers of beef females in each incidence category in Wales for each census year from 1980



1980 227 222 185 249 233 75

1995 253 224 185 365 315 75

2000 291 242 185 475 387 75

2004 302 236 185 477 395 75


when controlling for incidence category and 1980 cattle numbers. There was a greater

mean log number of beef females in parishes in incidence Category 1 controlling for year

and 1980 counts (b=0.187, P=0.002). There was no significant interaction between

census year and incidence category.


The mean ratios of dairy to beef females in each incidence category for Wales are shown

in Table E14.



237

Table E14 Ratios of dairy to beef females in each incidence category in Wales for each census year from 1980



1980 3.22 5.00 185 8.78 7.85 75

1995 1.90 2.68 185 4.42 3.15 75

2000 1.61 4.55 185 3.67 2.74 75

2004 1.47 2.40 185 3.37 2.52 75


P<0.001) when controlling for incidence category and 1980 ratios. The mean log ratio

was greater in parishes in incidence Category 1 when controlling for year and 1980

counts (b=0.447, P=0.007). There was no significant interaction between census year

and incidence category.


The mean numbers of cattle farms in each incidence category for Wales are shown in

Table E15. As the number of cattle farms for each census year was derived from VetNet

data, there are no figures for 1980.

Table E15 Numbers of cattle farms in each incidence category in Wales for each census year from 1995



1995 13.95 8.88 185 30.15 24.45 75

2000 12.98 8.17 185 30.89 22.48 75

2004 12.02 7.88 185 26.07 18.95 75


P<0.001) when controlling for incidence category. The mean log number of cattle farms

was greater in parishes in incidence Category 1 when controlling for census year



238

(b=0.649, P<0.001). There was no significant interaction between census year and

incidence category.

Analytical results: All three areas combined

The Category 1 cut-off parish incidence used for the analyses of all areas combined was

the more rigorous approach of only including those parishes with a bTB incidence rate

greater than the median value of 0.06 (N=214). The median parish incidence in the

restricted Category 1 was 0.12 (95% C.I. 0.11-0.14) for the four years up to 1995; 0.16

(95% C.I. 0.13-0.18) for the four years up to 2000 and 0.33 (95% C.I. 0.33-0.40) for the

four years up to 2004.


The mean numbers of all cattle in each incidence category for all three regions from the

census data are shown in Table E16.

Table E16 Mean numbers of cattle in each incidence category in all three regions for each census year from 1980 to 2004



1980 1231 736 357 1654 1156 214

1995 1107 698 357 1562 1196 214

2000 1054 696 357 1456 1137 214

2004 1024 690 357 1378 1125 214



category and 1980 cattle numbers. There was no effect of incidence category on the log

total number of cattle per parish when controlling for the effects of census year.


The mean numbers of dairy females in each incidence category for all three regions

combined are shown in Table E17.



239

Table E17 Mean numbers of dairy females in each incidence category in all three regions for each census year from 1980 to 2004



1980 393 331 357 610 472 214

1995 322 297 357 529 446 214

2000 336 324 357 561 511 214

2004 306 305 357 506 507 214


P<0.001) when controlling for incidence category and 1980 cattle numbers. The log

number of dairy females was significantly greater in parishes that were in incidence

Category 1 when controlling for year and 1980 counts (b=0.190, P=0.001). The incidence

category group by census year interaction was not significant (b=0.001, P=0.854).


The mean numbers of beef females in each incidence category for all three regions

combined are shown in Table 18E.

Table E18 Mean numbers of beef females in each incidence category in all three regions for each census year from 1980 to 2004



1980 149 183 357 122 169 214

1995 179 186 357 187 263 214

2000 208 203 357 222 274 214

2004 213 201 357 218 263 214



beef females did not differ significantly between incidence categories (b=0.027,

P=0.542).



240


The mean ratios of dairy to beef females in each incidence category for all three regions

combined are shown in Table E19.

Table E19 Ratios of dairy to beef females in each incidence category in all three regions for each census year form 1980 to 2004



1980 6.93 7.97 357 10.11 8.64 214

1995 3.49 3.72 357 4.73 3.70 214

2000 2.96 3.55 357 4.03 3.18 214

2004 2.57 3.13 357 3.51 2.75 214


P<0.001) when controlling for incidence category and 1980 ratios. The ratio of dairy to

beef females was significantly greater in parishes in incidence Category 1 when

controlling for year and 1980 ratios (b=0.340, P<0.001). The incidence category group

by census year interaction was not significant (b=0.002, P=0.698).


The mean numbers of cattle farms in each incidence category for all three regions

combined are shown in Table E20. As the number of cattle farms for each census year

was derived from VetNet data, there are no figures for 1980.

Table E20 Numbers of cattle farms in each incidence category in all three regions for each census year from 1995 to 2004



1995 11.59 7.78 357 14.60 11.69 214

2000 10.83 7.15 357 14.82 11.64 214

2004 9.57 6.84 357 10.52 8.10 214



241


P<0.001) when controlling for incidence category. The log number of cattle farms was

significantly greater in parishes that were in incidence Category 1 when controlling for

census year (b=0.118, P=0.019). The incidence category by year interaction was also

significant (b=-0.011, P=0.002), indicating that the log number of cattle farms decreased

at a faster rate in incidence Category 1 compared to incidence Category 0.

Summary and conclusions

A summary of the effects of (a) time (1995-2004) and (b) being in a high bTB incidence

parish compared to being in a parish with no bTB incidents during the study period is

shown in table E21. A plus or minus sign shows that there was a significant effect (at

P≤0.05) and the direction of the effect. An equals sign shows that there was no

statistically significant effect of time or bTB incidence category.

Table E21 Summary of the effects of time and bTB on outcome variables for each area studied, and all areas

Area

West Midlands South West Wales All three areas

Outcome

variable

Time bTB Time bTB Time bTB Time bTB

Total cattle - = - - - = - =

Dairy females - = = = = + - +

Beef females + + + + + + + =

Ratio dairy: beef - = - - - + - +

Cattle farms - = -* -* - + -* +*

Minus signs (-) indicate a significant negative effect; plus signs (+) indicate a significant positive

effect and equals signs (=) indicate no significant effect. * indicates that the interaction between

census year and bTB category was significant.

An example of the data for the mean number of cattle farms per parish by census year

and stratified by region and bTB incidence category is shown in Figure E1. The

difference in the rate of decrease between Categories 0 and 1 over time (interaction term)

in the South-West can be seen.



242

Overall, all of the outcome measures decreased over time apart from beef females, whose

numbers increased in each region and over all areas. This indicates a move from dairy to

beef in all three areas studied over the period 1995-2004. Parishes with a high bTB

incidence compared to those without had higher mean numbers of beef females in all

three areas when analysed separately. This was the case even when taking into

consideration 1980 census counts, suggesting that these parishes either have a higher

number of beef cattle due to the effect of bTB, or have a higher incidence of bTB due to

having a greater number of beef cattle. However, in the ‘all areas’ combined analysis, the

incidence category had no effect on the mean number of beef females in the parish. This

suggests that the parish sample in the ‘no bTB group’ category across all three regions

had higher mean numbers of beef females than the ‘no bTB group’ category parishes in

the separate analyses.

Figure E1 Rate of change in the mean numbers of cattle farms per parish stratified by region and bTB incidence category

0

5

10

15

20

25

30

35

1995 2000 2004

Census year

Mean number of cattle farm

s per parish

WM 0

SW 0

Wales 0

WM 1

SW 1

Wales 1

Regions are represented by a single colour and incidence categories by a single symbol.

Overall, all of the outcome measures decreased over time apart from beef females, whose

numbers increased in each region and over all areas. This indicates a move from dairy to



243

beef in all three areas studied over the period 1995-2004. Parishes with a high bTB

incidence compared to those without had higher mean numbers of beef females in all

three areas when analysed separately. This was the case even when taking into

consideration 1980 census counts, suggesting that these parishes either have a higher

number of beef cattle due to the effect of bTB, or have a higher incidence of bTB due to

having a greater number of beef cattle. However, in the ‘all areas’ combined analysis, the

incidence category had no effect on the mean number of beef females in the parish. This

suggests that the parish sample in the ‘no bTB group’ category across all three regions

had higher mean numbers of beef females than the ‘no bTB group’ category parishes in

the separate analyses.

The numbers of dairy females and the ratio of dairy to beef females were greater in

parishes with a history of bTB in Wales and in the ‘all areas’ combined analysis. Since

the mean number of beef females was increasing over time in Wales while total cattle

numbers were diminishing, this suggests a definite move from dairy to beef. However,

the higher numbers of dairy cattle in bTB endemic areas in Wales for all census years

indicates that high number of dairy cattle may be associated with the high bTB incidence

in Wales, an effect that is also significant in the ‘all areas’ combined analysis.

The mean number of cattle farms was significantly greater in parishes in the high

incidence category in the South-West, however the significant interaction between census

year and incidence category was negative, showing that the rate of decrease in the mean

number of cattle farms from 1995-2004 was faster for parishes with higher bTB

incidence. This effect appears to be stronger between 2000 and 2004 (Figure 1E). This

interaction is similarly significant when all three areas are analysed together, suggesting

that this increased rate of decline of cattle farms in high bTB incidence parishes (i.e.

using a cut-off of 6-7%) is an effect common to all hotspot regions. This appears to

provide objective evidence that bTB is having a long term negative effect on the numbers

of cattle farms in the UK.

In conclusion, where time has a significant effect when controlling for incidence

category, there is an overall increase or decrease in cattle or farm numbers over time,



244

which could be due to a variety of reasons. Where incidence category affiliation has a

significant effect when controlling for time, but the interaction is not significant, the

mean numbers of cattle and cattle farms are greater in one group of parishes compared to

the other. This is more likely to be a cause rather than an effect of high bTB incidence

or, conceivably, a correlate of a farming practice that is more conducive to bTB, since

there is no differential rate of change over time (i.e. with increasing severity of bTB).

Where the interaction term is significant, this represents a differential time effect between

parish groups, which suggests that it is the high bTB incidence itself that is driving the

rate of change.



245

Appendix F The choice experiment bTB cattle vaccine questionnaire

Hello, I am …………. from the University of Reading. You will remember I telephoned

a few days ago to make an appointment for an interview with you to find out your

attitude to the development of a cattle vaccine against TB. Is now still a convenient time

to talk?

Then, first, I would like to obtain some basic details about your farm.

1. Do you own the farm or are you a tenant or manager?

Owner [ ] Tenant [ ] Manager [ ] (Can tick more than one)

2. Is your farm mainly dairy or beef? Dairy [ ] Beef [ ]

3. Are you a registered organic producer? YES [ ] NO [ ]

4. What is the size of your herd(s) (not including followers)?

Dairy cow numbers ………….. Suckler cow numbers …………..

5. Do you have a pedigree herd? YES [ ] NO [ ]

6. Do you rear calves? YES [ ] NO [ ]

7. Do you have a beef fattening/finishing enterprise? YES [ ] NO [ ]

If YES, how many animals per year does this involve on average? …………

8. What other livestock do you have? Sheep YES [ ] NO [ ]

Pigs YES [ ] NO [ ]

Poultry YES [ ] NO [ ]

Other (please state) ………………………………………..

9. If you have dairy cows, what is your average milk yield per cow? ……….. litres/yr

10. If you have a beef suckler enterprise, how many calves do you rear per 100

cows? ……………….



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Now I have some questions about your farm’s TB history

11. What is the frequency of TB testing in your area? 1 x Year [ ] 2 x Year [ ]

12. Have you ever had a TB breakdown? YES [ ] NO [ ]

If YES, go to question 13. If NO, go to question 17.

13. Was it a confirmed or unconfirmed breakdown?

Confirmed [ ] Unconfirmed [ ]

14. How many breakdowns have you had in the last 5 years and when was your last

breakdown (date of first positive test)?

Number of breakdowns in last 5 years ……………..

Date of most recent breakdown ……………………

15. How many reactors did you have in your last breakdown and how long was the

breakdown?

Reactors ……… Length of time ………. (weeks)

16. Are you clear now? YES [ ] NO [ ]

17. To what extent do you agree with the following statements?

Agree strongly (5)

Agree (4)

Neither agree nor disagree (3)

Disagree (2)

Disagree strongly (1)

(i) TB is a major disease risk for the cattle industry.

Score …….

(ii) My farm has a high risk of a TB breakdown.

Score ……

(iii) Appropriate biosecurity measures on farms (such as ensuring only disease-free

replacement stock are bought in) can greatly reduce the risk of TB.

Score ……



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(iv) There is not much that I can do to prevent my cattle getting TB.

Score …….

18. In your opinion, what is your rough estimate of the risk of your herd testing

positive to TB in any one year? Please choose the category nearest to your own estimate

(tick ONE only).

Greater than 50% chance [ ]

50% chance [ ]

33% chance [ ]

20% chance [ ]

10% chance [ ]

5% chance [ ]

Less than 5% chance [ ]

19. How likely do you think it is that your herd will test positive to TB sometime in

the next 3 years?

Very likely Quite likely Neither likely Quite unlikely Very unlikely

nor unlikely

20. How likely do you think it is that your herd will suffer a severe breakdown (a

large number of reactors/ long period of breakdown) sometime in the next 5 years?

Very likely Quite likely Neither likely Quite unlikely Very unlikely

nor unlikely

21. Which of the following farm biosecurity measures do you undertake? Please

tick.

Always Mostly Sometimes Never

Ensure that bought-in cattle and hired bulls

are TB free [ ] [ ] [ ] [ ]

Keep your cattle away from other cattle [ ] [ ] [ ] [ ]

Breed your own replacements [ ] [ ] [ ] [ ]

Use Artificial Insemination [ ] [ ] [ ] [ ]

Keep isolation procedures for any [ ] [ ] [ ] [ ]

reactor or inconclusive animals and

cleansing and disinfection procedures



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Isolate and post-movement test purchased [ ] [ ] [ ] [ ]

stock

Protect farm buildings from access by [ ] [ ] [ ] [ ]

wildlife

A brief statement about the development of a TB cattle vaccine

The Government is funding research to develop a vaccine to protect cattle from TB. This

could be available within 10 years. The vaccine would need to be given as a single dose

to targeted groups of cattle. A test is being developed that will be used to differentiate

cattle that have been vaccinated and are immune from TB from those that are potentially

infected.

Initially, after commercial release of the TB cattle vaccine, current measures to control

TB would remain in place. Research is concentrating on how effective the vaccine will be

(i.e. how good it is at preventing cattle getting TB and/or infecting other animals with

TB). No vaccine can ever be 100% effective and it is likely that arrangements for

insurance or loss recovery payments for farmers will be needed in cases where the

vaccine has failed to prevent a TB breakdown.

You will be asked to make choices concerning the availability of a TB cattle vaccine

Here is an example of a set of choices. You will be asked to state your preferred choice.

You must choose ONE only of A, B or C.

A B C

1. Vaccine effectiveness - reduction in the risk of

a breakdown

60% 80% 0%

2. Vaccine effectiveness - reduction in the

breakdown severity

80% 80% 0%

3. Insurance/loss recovery (as a % of total

financial loss from TB) 60% 100% 70%

4. Cost of vaccine dose £10 £20 £0

Each set of choices includes the current situation, where no TB vaccine is available (that

is Column C). It is assumed that all choices include the current ‘test and slaughter’ and

pre-movement testing policies to control TB. Each set of choices contains just four main

components - the effectiveness of the vaccine measured by (1) its ability to prevent a TB

breakdown, (2) its ability to reduce the severity of a breakdown, (3) the level of insurance

or loss recovery available to farmers if any cattle in a vaccinated herd do become infected

with TB, and (4) the cost of the annual vaccine per animal (including the vet cost).

Comparing the three choices, if a vaccine that is 60% effective in reducing the risk of a

TB herd breakdown and 80% in the severity of a breakdown, backed by 60%



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insurance/recovery of all losses due to a TB breakdown, and costing £10 per animal is

your preferred choice then you would choose Option A. If you would prefer a higher

reduction of risk of a breakdown backed by 100% compensation and would be prepared

to pay £20 per animal for the vaccine then you would choose Option B. If you do not like

either of these, and would prefer not to have a vaccine and for the current levels of loss

recovery to be paid, then you choose Option C.

During the telephone interview you will be asked to state your preferred choice

from each of the choice sets below. You must choose ONE only of A, B or C.

Choice set 11 A B C

1. Vaccine effectiveness - reduction in the risk of a breakdown 20% 40% 0%

2. Vaccine effectiveness - reduction in the breakdown severity 20% 40% 0%

3. Insurance/loss recovery (as a % of total financial loss from TB) 0% 40% 70%


Choice set 12 A B C





Choice set 13 A B C





Choice set 6 A B C





Choice set 3 A B C





Choice set 20 A B C







250

Choice set 2 A B C





Choice set 9 A B C





Now, please assume that the vaccine developed is 90% effective and is insurance backed

by 100% recovery of the total financial loss due to TB breakdown for the farmer. It

requires one dose per animal per year and its cost includes veterinary or technician time.

22. Would you be willing to pay a cost of £10 per animal for such a vaccine?

YES [ ] NO [ ]

If YES, go to Question 23 below and then proceed directly to Question 25. If NO, go to

Question 24 below.

23. Would you be willing to pay £20 per animal?

YES [ ] NO [ ]

24. Would you be willing to pay £5 per animal?

YES [ ] NO [ ]

25. Can you please explain briefly the reasoning behind your answers to the

choice sets and willingness to pay questions above?

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

26. To what extent do the following statements represent your views?

Please score on a scale of 0-10 where 0 does not represent your views at all and 10

represents your view very well.

Development of a TB cattle vaccine would be the best solution to the TB problem.

Score ……



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A vaccine with a high effectiveness is very important.

Score …………

A vaccine with a low cost is very important.

Score ………..

A high level of insurance/cost recovery for a TB vaccine is very important

Score ……….

Another strategy (other than a cattle vaccine) for controlling TB in cattle is needed

Score ……….

I would be willing to pay something for a vaccine for badgers.

Score ……..

A combined strategy is needed with cattle vaccination, cattle testing, movement controls

and wildlife control (either vaccination or culling)

Score ………

Now if I may ask a few details about yourself

27. What age group are you in?

Less than 30 [ ]

30-39 [ ]

40-49 [ ]

50-59 [ ]

Over 60 [ ]

28. Do you have family members who farm with you or who might succeed you

on the farm?

YES [ ] NO [ ]

29. Is your aim to continue to cattle farm in the foreseeable future?

YES [ ] NO [ ]

If NO, please explain briefly why?

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

Thank you very much for your help with this survey.

Documents

Agricultural appraisal - Defra, UK