Analysis of Farm Business Survey 2011-12 Business ...sciencesearch.defra.gov.uk/...Document=14151_SIP2_WP2.3A_T3_Fi… · Funding for the SIP from Defra and The Welsh Government is

SIP Project 2: Opportunities and Risks for Farming and

the Environment at Landscape Scales (LM0302)

Analysis of Farm Business Survey 2011-12 Business

Management Practices

(WP 2.3A Task 2)

Paul Wilson (University of Nottingham)

February 2017

Background The Sustainable Intensification Research Platform (SIP) is a multi-partner research programme comprising

farmers, industry experts, academia, environmental organisations, policymakers and other stakeholders. The

platform has explored the opportunities and risks of Sustainable Intensification (SI) from a range of perspectives and

scales across England and Wales, through three linked and transdisciplinary research projects:

SIP Project 1 Integrated Farm Management for improved economic, environmental and social performance

SIP Project 2 Opportunities and risks for farming and the environment at landscape scales

SIP Project 3 A scoping study on the influence of external drivers and actors on the sustainability and

productivity of English and Welsh farming

Projects 1 and 2 have investigated ways to increase farm productivity while reducing environmental impacts and

enhancing the ecosystem services that agricultural land provides to society.

Project 2 partners are: University of Exeter (lead), ADAS, Bangor University, Biomathematics and Statistics

Scotland (BioSS), University of Bristol, University of Cambridge, Centre for Ecology and Hydrology (CEH), Eden

Rivers Trust, Fera, Game and Wildlife Conservation Trust (GWCT), James Hutton Institute, University of Kent,

Lancaster University, University of Leeds, Linking Environment And Farming (LEAF), Newcastle University, NIAB,

University of Nottingham, Rothamsted Research, Westcountry Rivers Trust

Acknowledgements This study was made possible through the support provided by Defra within the Sustainable Farm Platform (SIP)

research programme. Thanks are given to the FBS Co-operators who willingly gave of their time and shared their

data with the Research Officers (ROs) from Rural Business Research (RBR). Thanks to Lindsey Clothier and Tony

Pike in Defra for constructive comments on an earlier draft. The views and comments expressed herein are those

of the author alone.

Funding for the SIP from Defra and The Welsh Government is gratefully acknowledged.

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

Welsh Government

Contains public sector information licensed under the Open Government Licence v3.0

Data

[leave blank]

Citations

This report should be cited as:

Wilson, P. (2017). Analysis of Farm Business Survey 2011-12 Business Management Practices. Report for Defra

project LM0302 Sustainable Intensification Research Platform Project 2: Opportunities and Risks for Farming

and the Environment at Landscape Scales

Contact

Michael Winter ([email protected]), Paul Wilson ([email protected])

mailto:[email protected]

mailto:[email protected]

1

Table of Contents

Executive Summary ............................................................................................... 5

1. Background and Introduction ......................................................................... 7

1.2 Aims and Objectives ................................................................................ 10

2 Methodology ................................................................................................. 11

2.1 Sample Selection .................................................................................... 11

2.2 Analysis of FBS Data ............................................................................... 13

3 Results ......................................................................................................... 14

3.1: Summary of Chi-Squared tests ................................................................. 14

3.2: Working with others to achieve environmental benefits ................................ 14

3.3 Environmental monitoring practices ........................................................... 20

3.4 Current practices to reduce greenhouse gas emissions ................................ 25

3.5 Current Practice to adjust to climate change ............................................... 30

3.6 Accessing technical information ................................................................ 35

3.7 Accessing business management information ............................................. 40

3.8: Summary of Logistic Regression Analyses .................................................. 45

4. Discussion ................................................................................................. 47

4.1 Farmer .................................................................................................. 47

4.2 Farm ..................................................................................................... 48

4.3 Farming Finances .................................................................................... 50

4.4 Farm and Farmer Characteristics Summarised ............................................ 50

5. Conclusion and Recommendations ................................................................ 52

5.1 Conclusion ............................................................................................. 52

References ......................................................................................................... 55

Appendix 1: Results of Chi Squared Tests and Logistic Regression Analysis ................ 57

2

List of Tables

Table 1.1: Aspects of Cooperation ……………………………………………………………………………………11

Table 2.1: Potential Explanatory Variables Used in Analysis……………………………………………12

Table 2.2: Collaborative, Environmental and Production Variables Used in Analysis..……13

Table 2.3: Logistic Regression Analysis Dependent Variable Definitions .………………………14

Table 3.1: Summary of Statistical Significance of Factors by Groups ……………………………16

Table 3.2: Summary of Logistic Regression Factors by Groups……………………………….…….47

List of Figures

Figure 3.1: Percentage of ways of working with others to deliver environmental benefits

by education level ………………………………………………………………………………………………………………17


by Farm Type……………………………………………………………………………………………………………………..17


by Government Office Region……………………………………………………………………………………………18


by Farm Business Income (£/farm) groups .……………………………………………………………………18


by Agricultural Output: Agricultural Input Ratio groups.…………………………………………………19


by proportion of utilised agricultural area (UAA) owned groups……………………………………..19


by farmer age (in years) groups……………………………………………………………………………………….20


by segmentation groups…………………………………………………………………………………………………….20

Figure 3.9: Percentage of Environmental monitoring practices relating to biodiversity,

conservation and habits by education level………………………………………………………………………22


conservation and habits by Farm Type……………………………………………………………………………..22


conservation and habits by Government Office Region……………………………………………………23


conservation and habits by Farm Business Income (£/farm) groups……………………………..23


conservation and habits by Agricultural Output: Agricultural Input Ratio groups………….24


conservation and habits by proportion of utilised agricultural area (UAA) owned

groups…………………………………………………………………………………………………………………………………24


conservation and habits by farmer age (in years) groups……………………………………………….25


conservation and habits by segmentation groups…………………………………………………………….25

3

Figure 3.17: Percentage of practices to reduce greenhouse gas (GHG) emissions by

education level……………………………………………………………………………………………………………………27

Figure 3.18: Percentage of practices to reduce greenhouse gas (GHG) emissions by Farm

Type………………………………………………………………………………………………………………….........27


Government Office Region…………………………………………………………………………………………………28


Business Income (£/farm) groups…………………………………………………………………………..28


Agricultural Output: Agricultural Input Ratio groups………………………………………………………..29


proportion of utilised agricultural area (UAA) owned groups……………………………………………29

Figure 3.23: Percentage of practices to reduce greenhouse gas (GHG) emissions by farmer

age (in years) groups………………………………………………………………………………………......30


segmentation groups………………………………………………………………………………………………………….30

Figure 3.25: Percentage of current practices to adjust to climate change by education

level…………………………………………………………………………………………………………………………………….32

Figure 3.26: Percentage of current practices to adjust to climate change by Farm

Type…………………………………………………………………………………………………………………………………….32

Figure 3.27: Percentage of current practices to adjust to climate change by Government

Office Region……………………………………………………………………………………………………………………….33

Figure 3.28: Percentage of current practices to adjust to climate change by Farm Business

Income (£/farm) groups…………………………………………………………………………………….33

Figure 3.29: Percentage of current practices to adjust to climate change by Agricultural

Output: Agricultural Input Ratio groups……………………………………………………………………………34

Figure 3.30: Percentage of current practices to adjust to climate change by proportion of

utilised agricultural area (UAA) owned groups…………………………………………………………34

Figure 3.31: Percentage of current practices to adjust to climate change by farmer age

(in years) groups…………………………………………………………………………………………………………….35

Figure 3.32: Percentage of current practices to adjust to climate change by segmentation

groups………………………………………………………………………………………………………….35

Figure 3.33: Access to technical information by education level…………………………………….37

Figure 3.34: Access to technical information by Farm Type……………………………………………37

Figure 3.35: Access to technical information by Government Office Region………………….38

Figure 3.36: Access to technical information by Farm Business Income (£/farm)

groups…………………………………………………………………………………………………………………………………38

Figure 3.37: Access to technical information by Agricultural Output: Agricultural Input

Ratio groups……………………………………………………………………………………………………………………….39

Figure 3.38: Access to technical information by proportion of utilised agricultural area

(UAA) owned groups………………………………………………………………………………………………………….39

Figure 3.39: Access to technical information by farmer age (in years) groups…………….40

Figure 3.40: Access to technical information by segmentation groups………………………….40

Figure 3.41: Access to business management information by education level………………42

Figure 3.42: Access to business management information by Farm Type…………………….42

4

Figure 3.43: Access to business management information by Government Office

Region…………………………………………………………………………………………………………………………………43

Figure 3.44: Access to business management information by Farm Business Income

(£/farm) groups………………………………………………………………………………………………………………….43

Figure 3.45: Access to business management information by Agricultural Output:

Agricultural Input Ratio groups……………………………………………………………………………………….…44

Figure 3.46: Access to business management information by proportion of utilised

agricultural area (UAA) owned groups………………………………………………………………………………44

Figure 3.47: Access to business management information by farmer age (in years)

groups…………………………………………………………………………………………………………………………………45

Figure 3.48: Access to business management information by segmentation groups…..45

5

Executive Summary

Understanding the social and economic drivers and constraints of collaboration

represent important research enquiries within Defra’s Sustainable Intensification

Farm Platform (SIP) research programme.

Building upon previous literature and the results from Defra (2013) this study

draws upon data collected within the 2011/12 FBS for England for 1399 farms; the

hypotheses that there was no association between farmer responses towards

working with others to achieve environmental benefits (WOEB), environmental

monitoring practices (EMP), greenhouse gas reduction practices (GHGRP), current

practices to adjust to climate change (CPCC), access to technical information (ATI)

and access to business management information (ABMI), and explanatory factors

of Farm Type, Farm Size, Government Office Region (GOR), Farm Business Income

(FBI), Agricultural Output value to Agricultural Input costs ratio (AO:AI), proportion

of Utilised Agricultural Area owned (UAA Prop Owned), Education and Farmer Age

were tested. In addition, for a sub-sample of 522 farms the hypotheses were tested

in relation to Segmentation groups.

With respect to working with others to achieve environmental benefits, farm

businesses with managerial personnel who have obtained further or higher

education and are achieving greater levels of farm business performance are most

likely to collaborate with others for environmental benefit.

Farm businesses with further or higher education on the managerial team,

achieving greater levels of Farm Business Income (FBI) (but lower levels of

Agricultural Output to Agricultural Input ration (AO:AI)) are more likely to

undertake environmental monitoring practices, indicating the importance of

non-agricultural income with respect to environmental monitoring.

Younger farmers in the North East, on farms with managerial input at further or

higher education level, are most likely to undertake interventions towards

reducing greenhouse gas emissions; improved nutrient management is most

likely to occur on farms with degree-level educated farmers and farm-type specific

influences of practices to reduce greenhouse gas emissions exist as appropriate to

particular farming enterprises.

Farms with further or higher education within the managerial team are most likely

to undertake intervention practices to adjust to climate change; clear farm

type influences exist, with water efficiency being of greater importance on Dairy

and both water efficiency and water quality being greater on General Cropping and

Horticulture (combined) farms.

Farm businesses with greater levels of FBI and AO:AI ratios, with a member of the

managerial team with further or higher education, and classified as ‘Modern Family

Businesses’, have a greater reliance on accessing technical information

supplied for a charge; technical advice is also linked to enterprise – farm-type

specific factors.

Characteristics of farm businesses accessing business management

information include greater use of advice on farms achieving larger levels of FBI

and AO:AI ratios and having higher levels of educational attainment.

6

While others have identified the typical weakness of farm structural variables as

explanatory factors of collaborative activities (Emery and Franks, 2012), this report

finds that farm structural and outcome variables (e.g. farm type and FBI) are

associated with actions towards working with others to deliver environmental

benefits.

The findings reinforce outcomes from previous studies, in particular with respect to

the importance of human (Mathijs, 2003) and social capital (Pretty and Ward,

2001; Coleman, 1998) factors as captured within this report by indicators including

education, connectedness via farmer discussion groups and farmer self-

segmentation analysis.

Extending the concept of ‘human’ and ‘social’ capital and applying this to the

sustainable intensification of agriculture, it is recommended that the focus of

support should be towards enhancing ‘agricultural managerial capacity’ in particular

via mechanisms that develop such capacity, for example investment in business

benchmarking (House of Lords, 2016) and technical events that engage farmers in

an applied context.

Recommendations flowing from the findings of this report include cross-sector

Government-Industry-Academia investment to support further and higher

agricultural research and education, incentivising agri-tech solutions alongside

embedded training, enhancing business benchmarking and providing regional

flexibility in government supported (e.g. RDP) training courses.

7

1. Background and Introduction

Understanding the social and economic drivers and constraints of collaboration within the

context of Sustainable Intensification (SI) represents a central part of Defra’s Sustainable

Intensification Platform (SIP) research programme. Within this context, the current report

provides analysis of previously collected data, that draws upon Defra’s Farm Business

Survey (FBS) research programme, to address Objective 2.3a (Task 3). The preceding

tasks within Objective 2.3a relate to a review of literature on collaborative activities (Task

1) and case studies (Task 2). Consequently, the focus of this report is on the analysis of

FBS data, placing this in the context of task 1, the literature review. Specifically, the

report analyses attitudes of, and actions undertaken with respect to, collaborative

activities, environmental monitoring and measures taken to mitigate against climate

change, together with analysis of farmer attitudes towards their desire (or otherwise) to

know more about environmental management and practices, working with others to

deliver environmental benefits, and how environmental activities fit within the wider

working of the farm. This report seeks to build upon previous work undertaken by Defra

(2013), analyses examining cooperation and performance (e.g. Wilson et al., 2014) and

the ecosystems services literature (e.g. Emery and Franks, 2012). However, as noted

above the report does not seek to undertake a full analysis of the literature in this field,

but rather to highlight a selection of directly relevant pieces of work prior to the analysis

of data.

Defra’s (2013) report drew upon data from the English Farm Business Survey Business

Management Practices module in 2011/12, comparing these data with results from a

previous 2007/08 module across a range of factors important to SI, including accessing

information, working with others to achieve environmental benefit and attitudes towards

the environment. Within their analysis, Defra identify that a small, but statistically

significant increase in accessing advice across a number of communication channels

occurred. Defra (op cit.) note the importance of farm size and farmer age as key drivers

of business advice with smaller farm business and older farmers being less likely to access

advice. Key regional differences with respect to accessing technical information were

identified; the North West and Yorkshire and Humber regions were more likely to access

technical advice through the farming media and older farmers were less likely to access

Rural Development Programme funded technical advice. With respect to environmental

monitoring large farms and those in the South East were identified as being more likely to

undertake environmental monitoring; with respect to farm business performance Defra

(op cit.) identified that higher performing farms were more likely to undertake

environmental monitoring, albeit that this result was not statistically significant. Farm

type differences were also observed, in particular with respect to soil testing being

undertaken on 92% of General Cropping farms in contrast to 57% on Lowland Grazing

Livestock farms. Examining environmental collaborative practices specifically, Defra

(2013) note that farm type influences exist with Cereal farms being the most likely to work

with others to achieve environmental benefits, and Pig farms the least likely. It is

informative to note that lower performing farms were more likely than higher performing

business to not collaborate in respect to working with others to achieve environmental

benefit; moreover regional differences were also observed, with farmers in the North East

and Yorkshire and Humber being the most likely to not collaborate, and those in the South

East and South West, the least likely to not work with others to achieve environmental

benefits. while farm type influences exist, regional and farm business performance

differences were found to not be statistically significant with respect to working with others

to achieve environmental benefit.

The subject of cooperation and collaboration has recently received renewed attention

within Defra as evidenced by the report by Wilson et al. (2014) into Farm Business

Innovation, Cooperation and Performance. The focus of the 2014 report was primarily

around identifying factors associated with innovation and performance, within the context

of cooperation or collaborative activities (relatively narrowly defined, e.g. machinery

8

sharing or contract rearing), with this latter aspect forming a central theme of the

literature review of Wilson et al. Supplementary to Wilson et al. (op cit.) the literature

review embedded within this current SIP Objective 2.3a programme has extended the

literature analysis previously presented to Defra, in particular to provide a more holistic

view of collaboration / cooperation, within the specific objectives of SI and in particular

achieving positive environmental outcomes at the landscape scale. The remainder of this

section highlights a number of key points that have emerged from the literature, while not

seeking to repeat the literature review previously presented, but to highlight key areas

which will facilitate discussion of the results derived from the data analysis within this

report. Within the literature, the terms “collaboration” and “cooperation” are frequently

used interchangeably to define any actions whereby farmers are working together to

achieve either a common goal or to gain mutual benefits, broadly defined. However,

“collaboration” most frequently refers to collective or joint action that provides an

environmental benefit, while “cooperation” tends to be used to define actions pertaining

to production, marketing or business activities.

Other authors have focused upon the importance of ‘social capital’ within agricultural-

environmental sustainability contexts (e.g. Mathijs 2003) within the context that social

bonds and norms of behaviour are of importance in delivering sustainable solutions, in

particular where collective action is required (Pretty and Ward, 2001) and given that it can

be viewed as input to a production process (Coleman, 1998). Within the context of

delivery of the Countryside Stewardship Scheme (CSS), Mathjis (op cit.) notes the

importance of farmer ‘openness’ to contacts with professionals and non-professional as

drivers of willingness to engage in CSS activities. Pretty and Ward (op cit.) note that

“connectedness, networks and groups” form one of the four central aspects of social capital

– others being “relations of trust”, “reciprocity and exchanges”, “common rules, norms

and sanctions” (Pretty and Ward, 2001, pp 211). Others have argued that while it is

challenging to capture metrics of social capital, the educational attainment of individuals

provides an objective measure of ‘human capital’ (Schermer et al., 2011), while other

indicators taken together also influence social capital – for example, age, gender, health,

family characteristics, education, attitudes, values and geographic association (Lehtonen,

2004). Hence, within the context of sustainable intensification of agriculture, contacts and

levels of engagement with others (e.g. via discussion groups), education levels (e.g.

School versus degree in Agriculture), age of decision maker, attitudes (e.g. self-identify

of the farmer) and association (e.g. regionally or collectively within a type of farming

system) arguably provide a range of indicators that can be viewed as social capital within

an agricultural context.

Within the ecosystems services literature the importance of the appropriate landscape

scale has been highlighted as a necessary, but system-specific, condition of successful

intervention, albeit that collaborative actions across farms represents a small proportion

of the management activities funded thus far via Agri-Environment Schemes (AES) in the

UK. Considering this within the context of data pertaining to individual farm businesses

suggests that issues of farm type and particular regions may potentially influence

collaborative activities while farm business performance is likely to be a smaller influencing

factor (Defra 2013). Pragmatically, collaboration (and cooperation) is specifically

influenced by geography, physical distance and personal relationships. Individual farmers

that are willing to collaborate and seek out others with whom to collaborate with may be

constrained by farmers who are geographically close, but who do not wish to collaborate

or cooperate. With respect to environmental outcomes, successful collaboration may

require contiguous land parcels to be linked for effective collaboration. In terms of

production cooperation, similar farm types in close proximity may be required for success,

or alternatively complementary farm types (e.g. straw for manure arrangements between

livestock and arable farms). However, while a number of factors influence collaboration,

it was also noted (Emery and Franks 2012) that with respect to the delivery of AES

activities, some authors have identified patterns across structural variables such as farm

9

type, size, farmer age and finances. Meanwhile, evidence does suggest that existing

engagement with AES activities is likely to facilitate future engagement.

Fundamental drivers of, and barriers, to collaboration and cooperation include issues of

personal objectives, experiences and values which in turn are potentially influenced by

education, exposure to information and farmer age; the two former aspects being

potentially modifiable with respect to policy delivery. Barriers to success include lack of

communication and mutual understanding, farmer’s individual desire for independence,

attitudes towards risk, in particular with respect to machinery sharing and the use of

machinery at the most appropriate time. In these contexts social contacts and access to

information can be important factors. The formal and informal nature of collaborative

practices is also important to recognise – some farmers may view collaboration or

cooperative practices as “being neighbourly”, while others may view similar activities

explicitly as cooperation.

Table 1.1 draws upon Wilson et al.’s (2014) summary of cooperation enablers and

constraints and has been extended on the basis of the key findings from the literature

review in SIP 2. In summary, a range of factors are seen as enabling aspects of

cooperation. Financial benefits, directly achieved, or via economies of scale and efficiency

improvements, are clear cooperation enablers and tend to revolve around production or

resource (labour and machinery) sharing activities. Social interactions and structures that

facilitate interactions are also enabling factors of cooperation – in particular via discussion

groups and strong cohesive farmer and social networks; trust between members is a clear

enabler towards cooperation. With respect to environmental outcomes, clear scheme aims,

developed in consultation with farmers and delivered through small or modest sized

collaborative groups are effective, particularly when there is not a requirement for the

whole farm to be included in a scheme. Group member skills are also important,

encompassing business and practical skills. Compatibility of labour and machinery

availability between parties enables cooperation; it is interesting to note that this can be

derived via farms with the same enterprises (e.g. two combinable cropping farms), or

farms with mutually beneficial machinery with respect to timing of use (e.g. dairy and beef

farms working together for silage making). Some farmers view strong relationships with

contractors as a form of collaboration that is more typically viewed and conceptualised

within the context of machinery rings or sharing arrangements. Consultants, advisers and

‘innovation brokers’ are additional enabling factors towards cooperation, while trust, often

gained from past experiences or family connections, additionally aids cooperation.

Interactions along the marketing or value chain, in contrast to arms-length trading, also

enable cooperation. Factors associated with similar or different farm types can additionally

aid or constrain collaboration; however the clear message being that benefits need to

accrue to all parties. Weaker associations of collaboration include the different attitudes

held between early and later group adopters, and the need for strong governance and

clear member benefits to communicated and achieved.

10

Table 1.1: Aspects of Cooperation

Cooperation Enabling Cooperation Constraining Level of confidence

Clear financial benefit / economies of scales / increase efficiency of production

No demonstrable financial or other benefit, including economic failure experiences

+++

Labour and machinery pooling +++

Farmer discussion groups / social cohesion Social gaps between farmers +++

Trust between farmers Uncertainty over motivations of others ++

Flexibility over choice of other farmers to work with to achieve environmental outcomes, including small group size with clear scheme aims

Requirement to work with specific neighbours or large number of group members

++

Farmer involvement in scheme design and environmental schemes not requiring the whole farm to be included

Limitations in institutional support / infrastructure to members

++

Strong communication, practical and business skills of group members

++

Compatibility of labour, machinery or technology to sharing between members

Timeliness constraints over shared resources

++

Strong contractor or machinery sharing relationships or prospects

Negative past experiences over machinery sharing, including cost sharing disputes

++

Advisers and innovation brokers Lack of external influence on the group and farmer desire for independence

++

Value or marketing chain interactions Arms-length trading and ‘weak’ cooperative management in particular with respect to output prices achieved

++

Complementary farm types (e.g. combinable cropping and intensive pigs)

Biosecurity ++

Inaugural members of cooperation group Joining established group / motivations for cooperation non-aligned with those of

existing members.

+

Dedication of members with clear leadership / decision making processes

+

Key to level of confidence: + weak; ++ moderate; +++ strong. Source: Based upon Wilson et al. (2014), with additions drawn SIP 2 literature review.

1.2 Aims and Objectives

This analysis will examine the above attitudes, actions and activities in relation to a range

of farm and farmer characteristic data including: farmer biographical (e.g. age,

education); economic (e.g. Farm Business Income [FBI]); geographical; farm type; farm

size; and agricultural business performance (e.g. analysis of the above collaborative

practices in relation to agricultural performance in addition to whole farm business

performance) for 1399 farm businesses across England. For a sub-set of 522 of the above

1399 farm businesses, the impact of self-categorisation drawing upon attitudinal and

behavioural segmentation approaches will also be examined.

11

2 Methodology

2.1 Sample Selection

Data was taken from the Farm Business Survey (FBS) 2011/12 drawing upon the core FBS

data relating to physical and financial farm performance, farm characteristics and business

management practices data on 1399 farm businesses across England. Drawing on these

1399 observations provides the largest possible data set for analysis, however, a few of

the these farms fell below the FBS threshold and would not have been included in the

formal data set for the 2011/12 year. Moreover, the data analysis presented in the tables

and charts in this report use unweighted data and averages may therefore not reflect the

full background population. In a few cases practices or defining structural characteristics

(e.g. farm type) have been combined to permit data analysis and presentation. The

specific variables drawn from the FBS for the analysis in this report are detailed in Table

2.1 (potential explanatory variables) and Table 2.2 (collaborative, environmental and

production practices variables). In addition, data on 522 of these farm businesses were

also cross-referenced to self-segmentation data pertaining to the principal farm decision-

maker from the FBS 2009/10 year, where data were collected during the period February

to September 2010.

Table 2.1: Potential Explanatory Variables Used in Analysis

FBS Core Data

Farm Type Cereals; Dairy; General Cropping; Horticulture; LFA [Less Favoured Area]

Grazing Livestock; Lowland Grazing Livestock; Miked; Pigs and Poultry

(combined group).

Government Office Region

(GOR)

NE=North East; NW=North West; Y&H= Yorkshire and the Humber;

EM=East Midlands; WM=West Midlands; EE=East of England; SE=South

East; SW=South West.

Farm Business Income £/farm

(FBI)

FBI groups given in £/farm: <0; 0-30k; 30-60k; 60-90k; 90-120k; 120-

200k; 200k+.

Agricultural Output:

Agricultural Input ratio

(AgO:AgI)

Agricultural Output value divided by Agricultural Input cost where measures

<1 refer to costs exceeding value of output and >1 refers to value of outputs

exceeding costs: <0.75; 0.75-1; 1-1.25; >1.25.

Utilised Agricultural Area

proportion owned by farmer

(UAA Prop Owned)

Proportion of utilised agricultural area (UAA) owned by the farmer groups:

<0.25; 0.25-0.5; 0.5-0.75; >0.75.

FBS Business Management

Practices

Highest education level and

details of individuals with

managerial responsibility

(Education)

In increasing level: 1) No FE.HE= No further or higher level education; 2)

Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture,

related subject or in business management, accounting, marketing,

economics or related subject; 3) Degree.Bus.Other= Degree in business

management, accounting, marketing, economics or related subject or any

other subject; 4) Degree.Ag= Degree in agriculture or a related subject;

5) PG= Postgraduate qualification in business management or related

subject.

Age of the youngest member

of the farm with managerial

responsibility (Age)

Farmer age groups (in years): <30; 30-39; 40-49; 50-59; 60-69; 70+.

FBS Segmentation Analysis

Self-Segment Group

(Segment)

C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family

Business; CE=Challenged Enterprise.

12

Table 2.2: Collaborative, Environmental and Production Variables Used in Analysis

Group Abbreviation Definition of Activity

Ways o

f w

ork

ing w

ith

oth

er

to d

eliver

environm

enta

l benefits

(WO

EB)

NC No collaboration

FDDGN Farmer-driven discussion groups/networks of farmers

FDCEABNF Farmer-driven coordination of environmental activities and benefits with neighbouring farms

BPFDIOA As a by-product from farmer-driven initiatives which have other aims e.g.

shooting syndicates

PETPB Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts

AETPB Active engagement through third-party bodies e.g. RSPB, FWAG, GAME

conservancy and wildlife trusts

Environm

enta

l

monitoring

pra

ctices (

EM

P)

NP No practices

WBSI Wild birds. E.g. bird counts (self-initiated)

WBNSI Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance

scheme)

OMSI Other monitoring, e.g. floral, habitats, wild animals (self-initiated)

OMNSI Other monitoring, e.g. floral, habitats, wild animals (not self-initiated)

ST Soil testing

Gre

enhouse g

as

reduction

pra

ctices

(GH

GRP)

NI No intervention

INM Improved nutrient management

ISMM Improved slurry/manure management

ISD Improved soil drainage

LHAD Livestock health and adjustments to diet

FELET Fuel efficient/low emissions tractors

Other Other

Curr

ent

pra

ctices t

o

adju

st

to c

lim

ate

change (

CPCC

)

NI No intervention

WE Water efficiency

WQ Water quality

LUCEP Land use change and environmental protection

LS Livestock sustainability

CS Crop sustainability

SM Soil management

SK Sharing knowledge

SA Seeking advice

Access t

o t

echnic

al

info

rmation (

ATI)

NI None identified

TOF Through talking to other farmers

FM Through the farming media e.g. internet sites, trade magazines

ED Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies

DG Through discussion groups, farm walks or workshops

TANC Through technical advice supplied with no direct charge, e.g. input supplier

TAWC Through technical advice supplied for a charge

RAHT Through RDP-funded initiatives with a strong animal health theme

RTT Through RDP-funded initiatives with a strong technical theme

Access t

o b

usin

ess

managem

ent

info

rmation

(ABM

I)

NI None identified

TOF Through talking to other farmers

FM Through the farming media e.g. internet sites, trade magazines

ED Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies

DG Through discussion groups, farm walks or workshops

ANC Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS)

AWC Through specific business advice supplied for a charge (e.g. consultant)

RBMT Through RDP-funded initiatives with a strong business management theme

Note: Farms can undertake one or more activity within each category. Analysis presented for each group shows that the percentage undertaken in any group can sum to >100%. With respect to WOEB and EMP, farms classificited as “not applicable” (e.g. intensive holdings/protective cropping) were excluded from analysis.

13

2.2 Analysis of FBS Data

Drawing upon FBS data for 2011/12, data were extracted for the variables noted in Tables

2.1 and 2.2. The focus of the analysis was to test the hypotheses that there was no

association between the responses from farmers towards WOEB, EMP, GHGRP, CPCC, ATI

and ABMI and the potential explanatory factors of Farm Type, GOR, FBI, AgO:AgI, UAA

Prop Owned, Greatest level of Educational Attainment of Individuals with Managerial

Responsibility, Age of the Youngest Person of the Farm Team with Managerial

Responsibility, and for the reduced sample of 522 farms, Segmentation group.

Specifically, the analysis sought to establish if the presence or absence of particular farm

or farmer characteristics may explain differences in farmer responses to towards WOEB,

EMP, GHGRP, CPCC, ATI and ABMI. As the data pertaining to the variables of interest

represented count, or observational data only, these non-continuous or categorical data

were tested via a Chi-Squared test1 in order to provide overview analysis of the data

pertaining to the broad influence of potential explanatory or correlated variables. Note

however that this level of statistical testing does not seek to determine the causal effects

of individual variables.

Further analysis was therefore undertaken following the Chi-Squared tests via logistic

regression analysis in Genstat (17th edition) to identify the key drivers within each of the

categories identified above. Table 2.3 shows the dependent variable for each group. A

logistic regression was run for each dependent variable against the following independent

variables (Farm Business Income [£/farm], Age of Youngest Manager [years], Proportion

of Utilised Agricultural Area owned by the farm business [proportion], Government Office

Regions (GORs) [one of 8 GORs in Table 2.1 set as factors], Farm Type [one of 8 Farm

Types set as factors], Education [one of 5 Education codes in Table 2.1 set as factors].

Table 2.3: Logistic Regression Analysis Dependent Variable Definitions

Group Definition of Dependent Binary Variable Mean value

WOEB 2 or more practices undertaken 0.147

EMP 3 or more practices undertaken 0.208

GHGRP 3 or more practices undertaken 0.189

CPCC 4 or more practices undertaken 0.187

ATI 5 or more practices undertaken 0.416

ABME 4 or more practices undertaken 0.445

Key: Ways of working with other to deliver environmental benefits (WOEB); Environmental monitoring practices (EMP); Greenhouse gas reduction practices (GHGRP); Current practices to adjust to climate change (CPCC); Access to technical information (ATI); Access to business management information (ABMI).

1 A continuous variable can take any value- for example farm income, whereas categorical data can only take a

finite number of values, for example farm type.

14

3 Results

3.1: Summary of Chi-Squared tests

Table 3.1 provides an overview of the results derived from the Chi-Squared significance

tests undertaken on the data. Appendix 1, Tables A.1.1 to A.1.48 details the results of

the individual tests, while Figures 3.1 to 3.48 inclusive in this chapter provide a visual

representation of the results within particular areas of interest. Within Table 3.1 it is

interesting to note that the most common factors for which significant differences arise

across the activities examined are Farm Business highest education level present

(individuals with managerial responsibility), Farm Type, Government Office Region (GOR)

and Farm Business Income (FBI); however there is a strong correlation between certain

Farm Types and GORs given the regional clustering of particular Farm Types and hence

the results need to be interpreted in this context as discussed further in Section 4. For a

number of areas, farmer age (defined as the age of the youngest person on the farm with

managerial responsibility) additionally has been found to be a significant factor. The

Utilised Agricultural Area proportion owned (UAA Prop Owned) was found to be significant

factor in a small number of cases. It is informative to note that while overall farm

profitability, as measured by Farm Business Income (FBI) in £/farm is identified as a main

driver, the more narrowly defined metric of specific agricultural performance (The ratio of

Agricultural Output value to Agricultural Input costs (AgO:AgI)) was found to be marginally

less of a significant factor with respect to the range of activities examined in comparison

to the business level FBI metric. Farmer self-segmentation group analysis revealed fewer

significant influences with respect to environmental, climate change or greenhouse gas

practices, and only reveals modestly significant results with respect to accessing technical

and business management information. However this may be related to the smaller

number of data points available for analysis, particularly for some segments.

3.2: Working with others to achieve environmental benefits

Farm businesses where school level qualifications represented the highest level of

educational achievement of any individual with managerial responsibility undertook fewer

collaborative activities; this was particularly the case with respect to farmer driven

discussion groups and networks and passive or active engagement through third party

bodies (Figure 3.1). Farm type groupings (Figure 3.2) were observed to have a significant

impact on working together to achieve environmental benefits, in particular with respect

to active engagement through third party bodies. Regional influences (Figure 3.3)

demonstrate that farmers in the Yorkshire and the Humber region were less likely to

undertake collaborative environmental practices, while farmers in the South East and

South West were more likely to undertake collaborative practices. Greater collaboration

via passive or active engagement through third party bodies was observed in the West

Midlands, East of England, South East and South West. Lower levels engagement through

farmer discussion groups and networks was observed in Yorkshire and Humber, West

Midlands and the East of England. A significant influence of farm business income groups

(Figure 3.4) was identified with collaborative practices increasing as FBI increases. There

is an overall significant impact of agricultural performance groups, as measured by the

ratio of agricultural output value to agricultural input costs (Figure 3.5), indicating that

those farmers achieving the lowest Agricultural Input - Output Ratios undertake greater

levels of collaborative practises. Similarly, there was no significant influence of the

proportion of the utilised agricultural area owned on collaborative environmental activities

(Figure 3.6). Farmer age groups (Figure 3.7) did not have a significant influence on

collaborative practices, with the exception of lower levels of passive engagement through

third party bodies in the 40-49 year old farmer group. Segmentation group had no clear

influence on collaborative environmental practice (Figure 3.8), albeit that there was a

trend for Pragmatist and Modern Family Businesses to engage to a lower extent with

farmer discussion groups and networks to deliver environmental benefits.

15

Table 3.1: Summary of Statistical Significance of Factors by Groups

G

roup

Activity

Education

Farm

Type

GO

R

FBI

AgO

:AgI

UAA P

rop

Ow

ned

Age

Segm

ent

Ways o

f

work

ing w

ith

oth

er

to d

eliver

environm

enta

l

benefits

(WO

EB)

NC *** *** **

FDDGN *** #1 *** #2 #4 * #5

FDCEABNF ** #1 *** #2 *** #4 * #5

BPFDIOA *** *** ***

PETPB ** ** *** #3 ** ** ***

AETPB *** ** *** #3 ***

Environm

enta

l

monitoring

pra

ctices

(EM

P)

NP *** *** *** ***

WBSI * ***

WBNSI ** ** *** **

OMSI *** **

OMNSI *** *** * **

ST *** *** *** ** * ***

Gre

enhouse

gas r

eduction

pra

ctices

(GH

GRP)

NI *** *** *** *** *** ** ***

INM *** *** ** *** *** *** ***

ISMM ** *** ** *** ** ** *** **

ISD * *** ** *** * ***

LHAD ** *** ** ** **

FELET ** *** *** *** ** ***

Curr

ent

pra

ctices t

o

adju

st

to c

lim

ate

change (

CPCC

)

NI *** *** *** *** **

WE ** *** *** *** *

WQ * *** ***

LUCEP * ** *** ***

LS *** *** ** **

CS * *** *** ***

SM *** *** *** *** * ** **

SK *** * * ** * **

SA *** ** **

Access t

o t

echnic

al

info

rmation (

ATI)

TOF * *

FM

ED *** ** *** *

DG *** ** ** *** * *** **

TANC

TAWC *** *** *** *** *** ** * ***

RAHT *** *** *** *** *

RTT *** *** ***

Access t

o b

usin

ess

managem

ent

info

rmation (

ABM

I) NI * * - - - * -

TOF ***

FM ***

ED *** *** *** *** * **

DG *** ** *** ** ** **

ANC *** * *

AWC *** #6 *** ** * * #7

RBMT *** * #6 * ** * #7

Key: See Tables 2.1 and 2.2: Statistical significance level (*=90%; **=95%; ***=99%). #j = j activities combined with corresponding joint significant noted.

16


by education level. Key: NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of

farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; PG= Postgraduate qualification in business management or related subject.


by Farm Type. Key: NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers;

FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts.

0

10

20

30

40

50

60

70

80

No FE.HE Clg.NDC.Cert.Ag.Bus Degree.Bus.Other Degree.Ag Postgrad.BusMngt

Per

cen

tage

NC FDDGN FDCEABNF BPFDIOA PETPB AETPB

0

10

20

30

40

50

60

70

Cereals Dairy GeneralCropping

Horticulture LFA GrazingLivestock

LowlandGrazing

Livestock

Mixed, Pigsand Poultry

Per

cen

tage

NC FDDGN/FDCEABNF BPFDIOA PETPB AETPB

17


by Government Office Region. Key: NC=No collaboration; FDDGN=Farmer-driven discussion

groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West.


by Farm Business Income (£/farm) groups. Key: NC=No collaboration; FDDGN=Farmer-driven

discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies

e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. FBI groups given in £/farm.

0

10

20

30

40

50

60

70

80

90

NE NW Y&H EM WM EE SE SW

Per

cen

tage

NC FDDGN/FDCEABNF BPFDIOA PETPB/AETPB

0

10

20

30

40

50

60

70

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Per

cen

tage


18


by Agricultural Output: Agricultural Input Ratio groups. Key: NC=No collaboration;

FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs.


by proportion of utilised agricultural area (UAA) owned groups. Key: NC=No collaboration;

FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. Proportion of utilised agricultural area (UAA) owned by the farmer groups.

0

10

20

30

40

50

60

<0.75 0.75-1 1-1.25 >1.25

Per

cen

tage


0

10

20

30

40

50

60

70

<0.25 0.25-0.5 0.5-0.75 >0.75

Per

cen

tage


19


by farmer age (in years) groups. Key: NC=No collaboration; FDDGN=Farmer-driven discussion

groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. Farmer age groups (in years).


by segmentation groups. Key: NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks

of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise.

0

10

20

30

40

50

60

70

<30 30-39 40-49 50-59 60-69 70+

Per

cen

tage

NC FDDGN/FDCEABNF BPFDIOA PETPB AETPB

0

10

20

30

40

50

60

70

C/LC/CE P MFB

Per

cen

tage

NC FDDGN BPFDIOA PETPB AETPB

20

3.3 Environmental monitoring practices

As observed for working with others to deliver environmental practices (section 3.2) farm

businesses where school level qualifications represented the highest level of educational

achievement of any individual with managerial responsibility undertook fewer

environmental monitoring practices (Figure 3.9). Significant impacts of farm type

groupings (Figure 3.10) on environmental monitoring practices were observed, in

particular for soil testing which was carried out on a larger proportion Cereals, Dairy,

General Cropping and Mixed farms as would be expected a priori; in addition, Pig and

Poultry farm types were observed to have the greatest proportion of businesses

undertaking no practices. The Yorkshire and Humber, North West and South West regions

had greater proportions of farmers that undertook no environmental monitoring practices

(Figure 3.11). Of particular note is the lower than expected levels of self-initiated wild

bird counts and other self-initiated monitoring in the Yorkshire and Humber region. Note

that while South Western self-initiated monitoring levels were in line with other regions,

non-self-initiated monitoring in the South West is notably lower. A significant influence

of farm business income (Figure 3.12) was identified with a clear decrease in farm business

undertaking no environmental monitoring practices as FBI increases; it is informative to

note the increase in soil testing as FBI increasing, indicating the interaction of Farm Type

and FBI performance within these data when examining the results from Figures 3.10 and

3.12 tighter. The impact of agricultural performance group (Figure 3.13) was identified

with respect to soil testing increasing with Agricultural Output to Agricultural Input ratio,

while a reverse non-significant trend is observed for other monitoring that is self-initiated.

With respect to the proportion of the utilised agricultural area owned (Figure 3.14), it is

interesting to note the significantly lower levels of other non-self-initiated environmental

monitoring on farms with greater than 50% owned utilised agricultural area. Farmer age

groups (Figure 3.15) demonstrated a significant negative trend for undertaking soil testing

as age increases, while no other significant impacts of age group were identified. Farmer

segmentation group analysis indicates a lower level of soil testing undertaken on Custodian

and Lifestyle Choice and Challenged Enterprise (combined) groups (Figure 3.16).

21


conservation and habits by education level. Key: NP=No practices; WBSI=Wild birds. E.g. bird counts

(self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; PG= Postgraduate qualification in business management or related subject.


conservation and habits by Farm Type. Key: NP=No practices; WBSI=Wild birds. E.g. bird counts

(self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing.

0

10

20

30

40

50

60

70

80

Per

cen

tage

NP WBSI WBNSI OMSI OMNSI ST

0

10

20

30

40

50

60

70

80

90

Cereals Dairy GeneralCropping

Horticulture LFA GrazingLivestock

LowlandGrazing

Livestock

Mixed Pigs &Poultry

Per

cen

tage


22


conservation and habits by Government Office Region. Key: NP=No practices; WBSI=Wild birds.

E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West.


conservation and habits by Farm Business Income (£/farm) groups. Key: NP=No practices;

WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. FBI groups given in £/farm.

0

10

20

30

40

50

60

70

80


Per

cen

tage


0

10

20

30

40

50

60

70

80

90

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Per

cen

tage


23


conservation and habits by Agricultural Output: Agricultural Input Ratio groups. Key: NP=No

practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs.


conservation and habits by proportion of utilised agricultural area (UAA) owned groups. Key: NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. Proportion of utilised agricultural area (UAA) owned by the farmer.

0

10

20

30

40

50

60

70

80

<0.75 0.75-1 1-1.25 >1.25

Per

cen

tage


0

10

20

30

40

50

60

70

<0.25 0.25-0.5 0.5-0.75 >0.75

Per

cen

tage


24


conservation and habits by farmer age (in years) groups. Key: NP=No practices; WBSI=Wild

birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other

monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing.


conservation and habits by farmer age segmentation groups. Key: NP=No practices; WBSI=Wild

birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprises.

0

10

20

30

40

50

60

70

80

<30 30-39 40-49 50-59 60+

Per

cen

tage


0

10

20

30

40

50

60

70

80

C LC/CE P MFB

Per

cen

tage


25

3.4 Current practices to reduce greenhouse gas emissions

Where school level qualifications represented the highest level of educational achievement

of any individual with managerial responsibility, farm businesses were observed to

undertake fewer practices to reduce GHG emissions (Figure 3.17). Improved nutrient

management was greatest amongst degree educated farmers (and lowest amongst farm

businesses having no further or higher education). Farm type groups (Figure 3.18) were

observed to have a significant impact on GHG reduction practices. Of particular note are

the greater levels of General Cropping and Horticulture (combined), Grazing Livestock

(LFA and Lowland) and Pig and Poultry farm types to have recorded no GHG reduction

practices (this may in part be due to the nature of the specific questions within the survey).

As anticipated, improved slurry/manure management was more frequently observed on

livestock farms, in particular on dairy farms. Improvement to livestock health and

adjustments to diets were greatest on Dairy and Pig and Poultry farms; fuel efficiency or

low emissions tractors were more frequently observed on Cereals and Mixed farms.

Regionally, farms in the Yorkshire and Humber and East of England regions recorded

greatest levels of no interventions with respect to GHG reduction practices, while farms in

the North East and East Midlands recorded the lowest level of ‘no interventions’ (Figure

3.19). Improved slurry/manure management was greatest in the North West and South

West (were larger concentrations of dairy farms are located), but also high in the North

East. Note that the West Midlands recorded the greatest proportion of livestock health

and adjustment to diet practices. Substantial and significant regional differences were

observed with respect to investment in fuel efficient/low emissions tractors, with the North

East, East Midlands and South East recording greater levels in this category. A significant

influence of farm business income (Figure 3.20) on GHG reduction practices was observed,

with a clear increase in improved nutrient management as FBI increases. More generally,

there is a decrease in the proportion of farm businesses undertaking no practices as FBI

increases. With respect to the agricultural output: agricultural input ratio groups (Figure

3.21) the proportion of farm undertaking no practices decreases as the ratio increases and

conversely, the proportion of farms undertaking improved nutrient management increase.

With respect to tenure, farmers with more than 75% of their UAA owned recorded the

greatest proportion of ‘no intervention’ in GHG reduction practices, while those with 50-

75% UAA owned recorded the lowest level of ‘no intervention’; notably this group recorded

a greater proportion that undertook improved nutrient management and had invested in

fuel efficient / low emissions tractors (Figure 3.22). There was a clear and significant

linkage between farmer age groups (Figure 3.23), with the proportion recording ‘no

intervention’ increasing with farmer age; the reverse trend is broadly observed with

respect to levels of improved slurry/manure management and improved nutrient

management decreasing with farmer age. With respect to farmer segmentation groups

(Figure 3.24) improved slurry or manure management was more likely to be observed on

Lifestyle Choice and Challenged Enterprises (combined) and Modern Family Businesses.

26


education level. Key: NI=No intervention; INM=Improved nutrient management; ISMM=Improved

slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells.. No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; PG= Postgraduate qualification in business management or related subject.


Type. Key: NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure

management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells.

0

10

20

30

40

50

60

70P

erce

nta

ge

NI INM ISMM ISD LHAD FELET

0

10

20

30

40

50

60

Cereals Dairy GeneralCropping andHorticulture

LFA GrazingLivestock

LowlandGrazing

Livestock

Mixed Pigs & Poultry

Per

cen

tage


27


Government Office Region. Key: NI=No intervention; INM=Improved nutrient management;

ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells. NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West.


Business Income (£/farm) groups. Key: NI=No intervention; INM=Improved nutrient management;

ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells. FBI groups given in £/farm.

0

10

20

30

40

50

60

70


Per

cen

tage


0

10

20

30

40

50

60

70

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Per

cen

tage


28


Agricultural Output: Agricultural Input Ratio groups. Key: NI=No intervention; INM=Improved

nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’

due to small number of observations (<5) in a number of cells. Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs.


proportion of utilised agricultural area (UAA) owned groups. Key: NI=No intervention;

INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells. Proportion of utilised agricultural area (UAA) owned by the farmer.

0

10

20

30

40

50

60

<0.75 0.75-1 1-1.25 >1.25

Per

cen

tage


0

10

20

30

40

50

60

<0.25 0.25-0.5 0.5-0.75 >0.75

Per

cen

tage


29

Figure 3.23: Percentage of practices to reduce greenhouse gas (GHG) emissions by farmer

age (in years) groups. Key: NI=No intervention; INM=Improved nutrient management; ISMM=Improved

slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells.


segmentation groups. Key: NI=No intervention; INM=Improved nutrient management; ISMM=Improved

slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet;

FELET=Fuel efficient/low emissions tractors; Excludes ‘Other’ due to small number of observations (<5) in a number of cells. C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise.

0

10

20

30

40

50

60

70

<30 30-39 40-49 50-59 60+

Per

cen

tage


0

10

20

30

40

50

60

C LC/CE P MFB

Per

cen

tage


30

3.5 Current Practice to adjust to climate change

Farm businesses where school level qualifications represented the highest level of

educational achievement of any individual with managerial responsibility undertook fewer

practices to adjust to climate change (Figure 3.25); broadly as the level of education

increased the proportion of farm businesses that undertake no practices to adjust to

climate change decreased. Significant results with respect to sharing knowledge

demonstrated that, in general, as education level increased, practices towards sharing

knowledge increased. Farm businesses where no further or higher education was present

amongst the managerial personnel were least likely to seek advice on practices to adjust

to climate change. Farm type groupings had a significant impact on climate change

adjustment practices (Figure 3.26) with the greatest proportions of no practices observed

on Grazing Livestock (LFA and Lowland) and Mixed, Pig and Poultry (combined) farms.

Water efficiency was more overserved on Dairy and General Cropping and Horticulture

farms (combined), while water quality and land use change were of greatest importance

on General Cropping and Horticulture (combined) farms. As anticipated livestock and crop

sustainability were more frequently cited on cropping and livestock farm types

respectively, with soil management dominate on Cereals farms. Regional influences

(Figure 3.27) demonstrated that farmers in the Yorkshire and Humber region were more

likely to undertake ‘no interventions’ towards adjusting to climate change; by contrast

East Midlands’ farmers recorded the lowest level of ‘no intervention’. Regional differences

were also observed with respect to water efficiency, water quality, land use change and

environmental protection, livestock sustainability and crop sustainability. A significant

influence of farm business income was identified with respect to practices to adjust to

climate change (Figure 3.28) with the proportion of farms undertaking no intervention

broadly declining as FBI increases. Significant influence of agricultural performance

groups (Figure 3.29), as measured by the ratio of agricultural output value to agricultural

input costs was only noted with respect to livestock sustainability, and soil management,

most probably reflecting farm type influences. The influence of the proportion of the

utilised agricultural area owned on current practices to adjust to climate change (Figure

3.30) was most evident with respect to the lower level of soil management on farms with

greater than 75% owned UAA. Farmer age groups (Figure 3.31) demonstrates a

significant influence of age on ‘no intervention’ towards practices to adjust to climate

change; specifically a greater proportion of farmers in the 60+ age bracket recorded no

intervention towards climate change adaptation practices. It is informative to note that

there are substantial proportional differences for a number of practices with respect to

adjusting to climate change by segmentation group (Figure 3.32) albeit that these are

largely not statistically significant. In particular, note the large proportion of modern

family businesses undertaking water efficiency practices and the large proportion of

Lifestyle Choice and Challenge Enterprises farm businesses undertaking no interventions

in comparison to the other groups.

31

Figure 3.25: Percentage of current practices to adjust to climate change by education

level. Key: NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and

environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag= College national diploma/certificate in agriculture or related subject; Clg.NDC.Cert.Bus= College national diploma/certificate in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus= Degree in business management, accounting, marketing, economics or related subject; Degree.Other= Degree (any other subject); Postgrad.BusMngt= Postgraduate qualification in business management or related subject.

Figure 3.26: Percentage of current practices to adjust to climate change by Farm Type. Key: NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice.

0

10

20

30

40

50

60P

erce

nta

ge

NI WE WQ LUCEP LS CS SM SK SA

0

10

20

30

40

50

60

70

Cereals Dairy General Croppingand Horticulture


Lowland GrazingLivestock

Mixed, Pigs andPoultry

Per

cen

tage


32

Figure 3.27: Percentage of current practices to adjust to climate change by Government

Office Region. Key: NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change

and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West.

Figure 3.28: Percentage of current practices to adjust to climate change by Farm Business

Income (£/farm) groups. Key: NI=No intervention; WE=Water efficiency; WQ=Water quality;

LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. FBI groups given in £/farm.

0

10

20

30

40

50

60


Per

cen

tage


0

10

20

30

40

50

60

70

80

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Per

cen

tage


33

Figure 3.29: Percentage of current practices to adjust to climate change by Agricultural

Output: Agricultural Input Ratio groups. Key: NI=No intervention; WE=Water efficiency; WQ=Water

quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs.

Figure 3.30: Percentage of current practices to adjust to climate change by proportion of

utilised agricultural area (UAA) owned groups. Key: NI=No intervention; WE=Water efficiency;

WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice.

0

10

20

30

40

50

60

<0.75 0.75-1 1-1.25 >1.25

Per

cen

tage


0

10

20

30

40

50

60

<0.25 0.25-0.5 0.5-0.75 >0.75

Per

cen

tage


34

Figure 3.31: Percentage of current practices to adjust to climate change by farmer age

(in years) groups. Key: NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use

change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice.

Figure 3.32: Percentage of current practices to adjust to climate change by segmentation

groups. Key: NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and

environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise.

0

10

20

30

40

50

60

<30 30-39 40-49 50-59 60+

Per

cen

tage


0

5

10

15

20

25

30

35

40

45

50

C LC/CE P MFB

Per

cen

tage


35

3.6 Accessing technical information

Farm business education level (education level of any individual on the farm with

managerial responsibility) had a significant impact on accessing technical information.

Farm businesses without further or higher education had a significantly lower use of the

farming media and a significantly lower use of obtaining technical information supplied for

a direct charge, from events and demonstrations and discussion groups or from RDP-

funded initiatives (Figure 3.33). There was also a positive trend for greater use of technical

information supplied for a charge in relation to increasing higher levels of farm business

education. Farm businesses with no further or higher education used events and

demonstrations, discussion groups and advice supplied with a charge to a lower extent

than other groups. With respect to farm type influences (Figure 3.34), a significantly

greater use of RDP-funded initiatives with respect to animal health theme / technical

theme within the livestock farm types was observed. Taking advice supplied for a charge

was lower on Grazing Livestock (LFA and Lowland) farms; differences were also observed

in level of use of events and demonstrations and discussion groups across farm types.

Regional influences (Figure 3.35) were observed, specifically with respect to the East of

England, South East and East Midlands having greater use of advice supplied for a direct

charge; the South West region recorded a greater use of RDP-funded initiatives (animal

health and technical) which may in part be explained by availability of RDP-funded events

in this region. Overall there was a significant influence of FBI group (Figure 3.36) on

access to technical information, with a general increase in taking advice supplied for a

charge as FBI increases; it is informative to also note the significant difference in the use

of events and demonstrations and discussion groups, which typically increases as FBI

increases. With respect to the influence of the ratio of agricultural output value to

agricultural input costs (AO:AI), a significant relationship was observed for an increase in

taking advice supplied for a direct charge and greater AO:AI ratio. There was no overall

impact of the proportion of the utilised agricultural area owned on uptake of technical

advice, with the exception of the greater use of technical advice supplied for a charge

within the 0.25-0.5 and 0.5-0.75 proportion groups (Figure 3.38). Farmer age groups

(Figure 3.39) demonstrate a significant influence of age, specifically with respect to a trend

for the uptake of RDP-funded initiatives and attending discussion groups to decrease as

farmer age increases. Segmentation group analysis demonstrated that Modern Family

Businesses were most likely to draw upon technical advice supplied for a direct charge,

while Modern Family Businesses and Pragmatists were most likely to make use of

discussion groups (Figure 3.40).

36

Figure 3.33: Access to technical information by education level. Key: TOF=Through talking to

other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells. No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; PG= Postgraduate qualification in business management or related subject.

Figure 3.34: Access to technical information by Farm Type. Key: TOF=Through talking to other

farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

0

10

20

30

40

50

60

70

80

90

100P

erce

nta

ge

TOF FM ED DG TANC TAWC RAHT RTT

0

10

20

30

40

50

60

70

80

90

100



LowlandGrazing

Livestock


Per

cen

tage

TOF FM ED DG TANC TAWC RAHT/RTT

37

Figure 3.35: Access to technical information by Government Office Region. TOF=Through

talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells. NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West.

Figure 3.36: Access to technical information by Farm Business Income (£/farm) groups. TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells. FBI groups given in £/farm.

0

10

20

30

40

50

60

70

80

90

100


Per

cen

tage


0

10

20

30

40

50

60

70

80

90

100

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Per

cen

tage


38

Figure 3.37: Access to technical information by Agricultural Output: Agricultural Input

Ratio groups. TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites,

trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy

firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Figure 3.38: Access to technical information by proportion of utilised agricultural area

(UAA) owned groups. TOF=Through talking to other farmers; FM=Through the farming media e.g. internet

sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice

supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

0

10

20

30

40

50

60

70

80

90

100

<0.75 0.75-1 1-1.25 >1.25

Per

cen

tage


0

10

20

30

40

50

60

70

80

90

100

<0.25 0.25-0.5 0.5-0.75 >0.75

Per

cen

tage


39

Figure 3.39: Access to technical information by farmer age (in years) groups. Key

TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Figure 3.40: Access to technical information by segmentation groups. Key TOF=Through

talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells. C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise.

0

10

20

30

40

50

60

70

80

90

100

<30 30-39 40-49 50-59 60+

Per

cen

tage


0

10

20

30

40

50

60

70

80

90

100

C LC/CE P MFB

Per

cen

tage

TOF FM ED DG TANC TAWC RAHT/RTT

40

3.7 Accessing business management information

Farm business education level had a significant impact on the use of business management

information (Figure 3.41). As the education level of any individual with managerial

responsibility increased, there were a greater proportion of farm businesses drawing upon

advice supplied for a charge, and those with a business or other degree, or a postgraduate

qualification were more likely to access RDPE advice, but less likely to access advice from

the farming media. Farm businesses with no further or higher education were less likely

to access business advice via events and demonstrations or discussion groups. In line

with usage of technical information (section 3.6), as education level increased, the usage

of advice supplied for a direct charge also increased. There was a significant impact of

farm type groupings on the use of business management information (Figure 3.42), with

respect to obtaining business advice from events and demonstrations being greater on

Cereals and Dairy farms and lowest on farms; taking advice supplied for no charge was

greatest on Mixed farms. The impact of regional differences (Figure 3.43) was observed

with respect to farmers in the North West and Yorkshire and Humber drawing most upon

advice from other famers, the low level of advice from the farming media in the South

East, and lower usage of events and demonstrations for business advice in the West

Midlands, South East and South West. Farmers in the North West and East Midlands were

most likely to draw on business advice from discussion groups.. There was a significant

influence of farm business income (Figure 3.44), in particular with the use of business

advice supplied for a direct charge, and advice from events and demonstrations, and

discussion groups, increasing as FBI increases. With respect to the influence of agricultural

output to agricultural input ratio (Figure 3.45), those businesses recording an AO:AI of >

1.25 recorded an overall greater use of business advice from a range of sources. It is

interesting to note that there is a trend for farmers who own more than 75% of their

utilised agricultural area to have a greater likelihood of recording no use of business

management information (Figure 3.46) albeit this result draws upon data pertaining to

small proportions. Farmer age was observed to have a significant impact on the use of

business management information (Figure 3.47); as age increases obtaining business

advice from a range of sources decreases. Segmentation group analysis demonstrated

that Modern Family Businesses and Pragmatists were most likely to draw upon advice from

events and demonstrations, and discussion groups (Figure 3.48).

41

Figure 3.41: Access to business management information by education level. Key: NI=None

identified; TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; PG= Postgraduate qualification in business management or related subject.

Figure 3.42: Access to business management information by Farm Type. TOF=Through

talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

0

10

20

30

40

50

60

70

80

90P

erce

nta

ge

NI TOF FM ED DG ANC AWC RBMT

0

10

20

30

40

50

60

70

80

90



LowlandGrazing

Livestock


Per

cen

tage

TOF FM ED DG ANC AWC RBMT

42

Figure 3.43: Access to business management information by Government Office Region. TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells. NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West.

Figure 3.44: Access to business management information by Farm Business Income

(£/farm) groups. TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites,

trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells. FBI groups given in £/farm.

0

10

20

30

40

50

60

70

80

90

100


Per

cen

tage

TOF FM ED DG ANC AWC/RBMT

0

10

20

30

40

50

60

70

80

90

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Per

cen

tage


43

Figure 3.45: Access to business management information by Agricultural Output:

Agricultural Input Ratio groups. TOF=Through talking to other farmers; FM=Through the farming media

e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Figure 3.46: Access to business management information by proportion of utilised

agricultural area (UAA) owned groups. NI=None identified; TOF=Through talking to other farmers;

FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme.

0

10

20

30

40

50

60

70

80

90

<0.75 0.75-1 1-1.25 >1.25

Per

cen

tage


0

10

20

30

40

50

60

70

80

90

<0.25 0.25-0.5 0.5-0.75 >0.75

Per

cen

tage

NI TOF FM ED DG ANC AWC RBMT

44

Figure 3.47: Access to business management information by farmer age (in years) groups. TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme; Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Figure 3.48: Access to business management information by segmentation groups. NI=None identified; TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise.

0

10

20

30

40

50

60

70

80

90

<30 30-39 40-49 50-59 60+

Per

cen

tage


0

10

20

30

40

50

60

70

80

90

C LC/CE P MFB

Per

cen

tage

NI TOF FM ED DG ANC AWC/RBMT

45

3.8: Summary of Logistic Regression Analyses

Table 3.2 provides an overview of the results derived from the logistic regression analyses.

Appendix 1, Tables A.1.49 to A.1.54 details the results of the final regression models for

each case. When examining the results within Table 3.2, note that the dependent variable

in each case is a broad indication of the number of different practices undertaken within

any particular category; it does not in itself indicate the depth to which each of these

practices may be undertaken by farm businesses, but rather provides a broad indication

of overall engagement across different activities. With this caveat in mind, it is

nevertheless interesting to note that Farm Business Income has a significant and positive

effect on all practice levels. Education level is also important, in particular farm businesses

with no further or higher education are significantly less likely to engage in working with

others for environmental benefit, access technical and business management advice and

undertake practices to adjust to climate change. By contrast, farm businesses with

personnel holding a degree in agriculture or a postgraduate degree are significantly more

likely to work with others and undertake environmental monitoring practices, in

comparison to farm businesses with college level qualifications as the highest level of

managerial education. Regionally, relative to the East Midlands, the North East

demonstrates greater levels of engagement across all practices, while farm businesses in

the North West are significantly more likely to work with others to achieve environmental

benefit and also more likely to access technical and business advice from a greater range

of sources. Farm businesses in the South West are less likely to undertake a wide range

of different practices with respect to environmental monitoring, greenhouse gas reduction

practices, adapting to climate change and accessing technical and business management

information. Other regional influences include the lower likelihood of a wide range of

greenhouse gas reduction practices being adopted in Yorkshire and Humber and the East

of England, and lower levels of engagement in practices to adjust to climate change in

Yorkshire and Humber. In addition, farm businesses that access a smaller range of

technical advice sources are likely to be found in the West Midlands and East of England,

while farm businesses in the West Midlands are also likely to access business advice from

a wide range of sources. With respect to farm types, relative to Cereals farms, Dairy farms

are less likely to work with others to achieve environmental benefits and are also less

likely to undertake environmental monitoring, but more likely to undertake greenhouse

gas reduction practices. Horticulture businesses are less likely to undertake environmental

monitoring, greenhouse gas reduction practices, adapt to climate change or access a wider

range of business management information sources. Lowland Grazing Livestock and Pig

and Poultry farms are less likely to undertake environmental monitoring than Cereals

farms. Farmer age was positively associated with working with others and undertaking

environmental monitoring, but negatively associated with greenhouse gas reduction

practices. Finally, note that the proportion of the utilised agricultural area owned does not

have a significant impact on practice levels.

46

Table 3.2: Summary of Logistic Regression Factors by Groups

Gro

up

Facto

r

WO

EB

EM

P

GH

GRP

CPCC

ATI

ABM

I

Education:

refe

rence

gro

up

Clg

.ND

C.

Cert

.Ag.B

us No FE.HE - - - - - - - -

Degree.Bus.Other

Degree.Ag ++ +

PG ++ ++

Farm

Type:

refe

rence

gro

up C

ere

als

Dairy - - - - + ex

General Cropping ex

Horticulture - - - - - - ex - -

LFA Grazing Livestock

ex

Lowland Grazing Livestock

- ex

Mixed ex

Pig & Poultry - -

GO

R

refe

rence g

roup

East

Mid

lands

NE ++ ++ ++ ++ ++ ++

NW ++ ++ ++

Y&H - - - -

WM - - - -

EE - - - -

SE ++

SW - - - - - - - - - -

FBI (£/farm) NI ++ ++ ++ ++ ++ +

AO:AI (prop) ex ex ex ex ex ex

Age (years) + + - - ex ex ex

Key: Practices: Ways of working with other to deliver environmental benefits (WOEB); Environmental monitoring practices (EMP); Greenhouse gas reduction practices (GHGRP); Current practices to adjust to climate change (CPCC); Access to technical information (ATI); Access to business management information (ABMI). Education: 1) No FE.HE= No further or higher level education; 2) Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; 3) Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; 4) Degree.Ag= Degree in agriculture or a related subject; 5) PG= Postgraduate qualification in business management or related subject. GOR NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West. Indicators: positive and significant at 95% (++); positive and significant at 90% (+); negative and significant at 95% (- -); negative and significant at 90% (-); not significant ( ); excluded from regression because all factors in group or variable was not significant at 90% or greater (ex).

47

4. Discussion

The sections below aim to summarise the key findings from the analysis of FBS data. The

chapter is structured around three main themes: i) “Farmer”, focusing upon farmer age,

education and self-segmentation analysis; ii) “Farm”, examining the impact of Farm Type,

Size, GOR and UAA proportion owned; iii “Farming Finances”, considering the analysis of

farm business and agricultural cost centre performance.

4.1 Farmer

One of the main findings from the results of the data analysis relate to the biographical

characteristics of the ‘farmer’ that were observed to influence actions and practices across

the six categories analysed: ways of working with others to deliver environmental benefits

(WOEB); environmental monitoring practices (EMP); greenhouse gas reduction practices

(GHGRP); Current practices to adjust to climate change (CPCC); access to technical

information (ATI); access to business management information (ABMI). Farmer education

(defined as the highest educational qualification of a member of the farm business with

managerial responsibility) was observed to be a key factor influencing across all six

categories and from the logistics regression analysis, with a common finding being the

significantly greater proportion of farm businesses where no one with managerial

responsibility has further or higher education, undertaking no collaboration (WOEB), no

practices (EMP) and no interventions / no practice identified (GHGRP, CPCC, ABMI).

Typically farm businesses with no member of the managerial responsibility having further

or higher education were less likely to take part in discussion groups and other networks.

As education levels increased on farm, there was an increase in seeking and sharing

knowledge, both technical and business related, and farm businesses with higher levels of

education were more likely to obtain advice supplied for a charge. It is argued that these

key findings relating to education are partly influenced by a link to farmer age, with a

greater proportion of older farmers typically having no further or higher education,

however, the link to farmer age is only partial, in particular because the data specifically

captures the highest level of qualification of anyone in the farm business with managerial

responsibility. Moreover, and arguably of more direct importance, is that education is

acting as an enabler to cooperation, collaboration and engagements with both other

farmers and agricultural professionals more widely. Baranchenko and Oglethorpe (2012)

identified education as an enabling factor towards cooperative membership that in turn

had environmental collaboration potential. Mills et al. (2011) identified previous

acquaintances with other farmers as an enabling factor towards collaboration which may

not directly flow from educationally attainment level, but is arguably likely to be in part

driven by the enabling factor of like-mindedness farmers working together (Emery and

Franks, 2012); more generally there has been a growing interest in issues of human

(Mathjis, 2003) and social capital (Pretty and Ward, 2001; Coleman, 1998) and social

cohesion with respect to farmer decision making (e.g. Barnes et al., 2013; Schermer et

al., 2011; Wilson et al., 2014), within which education will arguably play a crucial role.

The influence of farmer age (within these data defined as the youngest member of the

farm business with managerial responsibility) on the six categories examined is less well-

defined than with respect to the highest educational qualification groupings. This lower

statistical evidence is also demonstrated when examining the broad findings of the logistic

regression analysis. However, clear themes do emerge, with decreasing levels of soil

testing, interventions to reduce GHG emissions, interventions to adjust climate change

and uptake of RDP training (both technical and business related) as farmer age increases.

With respect to environmental activities, Emery and Franks (2012) found no influence of

farmer’s age on collaboration, and it is informative to note that significant results in respect

to farmer age from this study largely pertain to activities at the individual farm business

level rather than working with other farmers per se. However, there is some evidence

from the logistic regression analysis that older farmers are more likely to work with others

to achieve environmental benefit and undertake environmental monitoring practices. The

48

analysis of farmer segmentation groups, drawing upon a sub-set of the data, resulted in

weaker levels of significant relationships across the six categories examined, albeit that

the results draw on more modest sample sizes with some groups combined to permit data

presentation and analysis, ng. and this needs to be interpreted with this caveat in mind.

Specifically, ‘pragmatist’ and ‘modern family bsinesses’ were more likely to undertake soil

testing, and ‘modern family businesses’ were more likely to access technical advice

supplied for a charge. The custodians and lifestyle choice and challenged enterprise

(combined) groups were also less likely to undertake soil testing. Previous analyses of

farmer segmentation within England (Garforth and Rehman, 2006; Defra, 2008; Wilson et

al. 2013) have identified the importance of farmer-life-stage and agricultural economic

conditions at the time of data capture to be important in farmer self-segmentation

analyses and that most farmers exhibit characteristics of more than one segment, and will

also move between segments over time (Defra, 2008; Wilson et al., 2013). It is

informative to note that while the findings from this current study demonstrate weaker

statistical results with respect to examining farmer segmentation groupings, this absence

of statistical significance does not directly demonstrate absence of impact by segmentation

group. Others have identified the personal rationale of farmers for engaging with

enhancing biodiversity (Farmar-Bowers and Lane, 2009) which would a priori be

hypothesised to be most strongly correlated with the custodian segment. It is however

important to note that the smaller sample size (522) and slight difference in data capture

time frames between the farmer segmentation data and the self-reported data on

environmental, advice and collaborative practices analysed within this study, are potential

caveats to the findings generated.

4.2 Farm

The key ‘farm’ characteristics examined within the data were farm type, Government Office

Region (GOR) and the proportion of the UAA owned by the farmer. Significant influences

of farm type were identified across all six categories examined. With respect to ways of

working with others to deliver environmental benefits, Cereals and General Cropping farms

were least likely to be undertaking no practices with Mixed, Pigs and Poultry (combined)

and Dairy farms more likely to undertake no practices, in line with findings from Defra

(2013). This is demonstrated in part through differences in passive and active engagement

with professional bodies and farmer discussion groups. The logistic regression analysis

demonstrated lower levels of overall practice uptake on Horticulture farms, albeit that this

could be a reflection of the options available within the survey questions rather than the

actual practice uptake. Farm type differences were also observed with respect to

environmental monitoring with crop-based farm types more engaged with soil testing

(Defra, 2013), and practices to reduce greenhouse gas emissions were also in line with

expectations relating to the particular practice relevant to the farm type (e.g. improved

slurry manure management on livestock farms). In part, these findings, with respect to

environmental collaboration or environmental monitoring practices contrast with Emery

and Franks’ (2012) analysis of Agri-Environmental Scheme (AES) practices, who find a

lack of farm type impact. With respect to practices to adjust to climate change, enterprise

specific factors were more likely to be observed on particular farm types (e.g. soil

management on cropping farms), while it is informative to note that water efficiency or

water quality practices were more frequently observed on Dairy and General Cropping and

Horticulture (combined) farms. Farm type differences in accessing technical advice in part

followed directly from specific advice categories (e.g. greater uptake of animal health RDP-

funded initiatives on livestock farms), but also that there was a lower uptake of technical

advice supplied for a direct charge on Grazing Livestock farms. This latter lower uptake

of charged-for-advice in the Grazing Livestock farm types was also present for business

advice supplied for a charge, alongside lower uptake by Mixed farms. Hence, these

findings in part reinforce a priori expectations with respect to particular production-specific

practices, but also highlight key farm type differences with respect to environmental

collaborative practices, engagement more generally and seeking paid-for business advice.

49

Given the influence of farm type identified above, and the strong farm type – regional

groupings that exist given the geographic-specific nature of the clustering of particular

farm types in England, the significant differences observed with respect to GOR is in part

anticipated. However, with respect to ways of working with others to deliver

environmental benefit and environmental monitoring practices, the influence of GOR

appears to be of greater significance than that of farm type. GOR influences represented

one of the two key distinguishing factors (alongside education) across the six categories

examined from the Chi-Squared analysis. In particular, farms in the Yorkshire and Humber

region were less likely to work with others to achieve environmental benefits, in contrast

to the greater practice levels of the South East (Defra, 2013) and South West. Passive or

active engagement to deliver environmental benefits was observed to be greater across

the West Midlands, East Midlands, South East and South West. With respect to

environmental monitoring, farms in the Yorkshire and Humber region were also observed

to undertake fewer practices than observed for the other regions. Yorkshire and Humber

region farms additionally were observed to record greater levels of no interventions to

reduce GHG emissions alongside the East of England region; these contrast with farms in

the North East and East Midlands that recorded the lowest level of no interventions. With

respect to adjusting to climate change, farms in the East Midlands recorded the lowest

level of ‘no interventions’ while farms in Yorkshire and Humber recorded the greatest levels

of no interventions. With respect to accessing technical information, the South West

region was observed to access greater levels of RDP-funded initiatives, whilst in terms of

business information, the North East and North West recorded the greatest proportional

use of RDP-business related information; such regional variation with respect to access to

technical and business information is a function of availability of the training and scheme

initiatives, which in recent years have been focused on particular regions (Defra, 2015).

The East of England and the South East recorded greater use of technical information

supplied for a charge; in terms of business advice the North East, Yorkshire and Humber

and West Midlands drew most heavily on advice supplied for no charge Previous

collaboration and cooperation studies have frequently focused upon particular regions

rather undertaking cross-regional comparisons. Within England, Wilson et al., (2014)

identified examples of cooperation across farm types (e.g. arable and intensive pig or

poultry units viva straw-for-manure arrangements) that whilst not explicitly geographically

defined, are, in this example, most likely to occur in mainly arable regions.

In terms of UAA tenure / ownership levels, there was a lower uptake of non-self-initiated

environmental monitoring on farms that own more than 50% of their UAA; additionally

those farmers who owned more than 75% of their UAA recorded the greatest proportion

to undertake no intervention towards GHGRP. Contrasting this latter result, farmers who

owned 50-75% of their UAA undertook greater levels of improved nutrient management

practices and also invested more in fuel efficient / low emissions tractors. Land ownership

was weakly associated with water efficiency, with uptake of water efficiency practices

increasing with the UAA proportion owned. Other UAA proportion owned impacts were

restricted to the greater use of technical information supplied for a charge for farmers

owning 25-75% of their UAA and the lower uptake of business management information

by farmers owning more than 75% of their UAA. It is informative to note that farmers

who own the larger proportion of their UAA were less likely to record interventions towards

GHGRP and uptake of business management information. This later result perhaps reflects

the greater need of farmers who own less than 75% of their UAA to undertake efficient

technical and business practices in order to ensure sufficient returns to meet rental

obligations. However, with respect to working with others to achieve environmental

benefits and undertaking environmental monitoring practices, tenure impacts were

insignificant. This contrasts with Emery and Franks (2012) who note the need to include

landlords in successful AES delivery as a barrier to uptake, albeit that AES participation

requires a greater level of formality than the collaborative and environmental practices

analysed within this report.

50

4.3 Farming Finances

With respect to financial performance as analysed via Farm Business Income (FBI) and

Agricultural Output - Agricultural Input (AO:AI) ratio groups, those farm business

achieving greater FBI were more likely to undertake collaborative practices to benefit the

environment, undertake some environmental monitoring, practices to reduce greenhouse

gas emission and adjust to climate change and moreover obtain technical and business

advice supplied for a direct charge; this finding was also reinforced by the logistic

regression analysis. The FBI metric captures the overall farm business performance; the

AO:AI metric provides a measure of the Agricultural component of the business. With

respect to AO:AI it is informative to note the contrasting and complementary analyses

with FBI performance. In line with the broad findings with respect to FBI groups, farms

achieving greater AO:AI ratios are more likely to undertake soil testing, undertake some

form of greenhouse gas reduction practices, and obtain technical and business advice

supplied for a direct charge. By contrast, farm businesses with lower AO:AI performance

are more likely to undertake greater levels of collaboration to achieve environmental

benefit and other self-initiated environmental monitoring. This contrast between

environmental monitoring and collaborative practices with respect to business / agriculture

cost centre performance is in line with Defra’s (2013) analysis that identified non-

significant differences between economic performance bands and these practices. The

analysis presented herein indicates that those businesses achieving greater FBI

performance (encompassing Agriculture, Single Payment, Diversification and Agri-

Environment cost centres) are arguably driven to undertake greater levels of monitoring

and collaboration as a function of their diversified or agri-environment activities, rather

than their agricultural activities and performance per se. Implications of this finding

include the direct link between payment for ecosystem services and the environmental

activity or output achieved (Burton et al., 2008) and that targeting of environmental

policies to achieve specific objectives is arguably a resource efficient policy tool

(Armsworth et al., 2012).

4.4 Farm and Farmer Characteristics Summarised

Combining the main findings from the above results indicates that with respect to working

with others to achieve environmental benefits, farm businesses with personnel in the

managerial team who have obtained further or higher education, achieving greater levels

of FBI (in contrast to AO:AI) are most likely to collaborate. By contrast farm businesses

that are more specialist livestock producers, achieving lower levels of FBI, which do not

have a member of the farm managerial team that has undertaken further or higher

education, are least likely to collaborate for this purpose. With respect to environmental

monitoring practices, farms with managerial personal without further or higher

education, who are in the South West are least likely to undertake monitoring practices,

while farms achieving greater levels of FBI but lower levels of AO:AI are more likely to

undertake environmental monitoring, indicating the importance of non-agricultural income

with respect to environmental monitoring. Older farmers are more likely to not undertake

soil testing. Examining practices to reduce greenhouse gas emissions, the results

demonstrate that younger farmers in the North East, on farms with managerial input at

further or higher education level, are most likely to undertake some interventions towards

reducing greenhouse gas emissions. By contrast, older farmers in the Yorkshire and

Humber, East of England and South West regions, who have not undertaken further or

higher education, are least likely to undertake greenhouse gas reduction practices.

Improved nutrient management is most likely to occur on farms with degree-level

educated farmers. Farm-type specific influences of practices to reduce greenhouse gas

emissions exist, in-line with appropriate activities for the particular farm type. With

respect to practices to adjust to climate change, farm businesses with further or higher

education levels amongst the managerial team, are most likely to undertake intervention

practices. By contrast, farmers in the Yorkshire and Humber region, in farm businesses

without further or higher education are least likely to undertake practices to adjust to

climate change. Clear farm type influences exist, with water efficiency and / or water

51

quality being of greater importance on Dairy, and General Cropping and Horticulture,

farms. In terms of accessing technical information, farm businesses who achieve

greater levels of FBI and AO:AI ratios, have someone on the managerial team that has

obtained further or higher education, and classified as a ‘modern family business’, have a

greater reliance on technical information supplied for a charge. By contrast farm

businesses without further or higher education as part of the managerial team, have a

greater reliance on the farming media and lower use of information supplied for a direct

charge. Technical advice is also linked to enterprise-farm type specific factors.

Characteristics of farmers accessing business management information include

greater use of advice with farms achieving greater levels of FBI and AO:AI ratio, having

higher levels of educational attainment.

52

5. Conclusion and Recommendations

5.1 Conclusion

Within Defra’s Sustainable Intensification Platform (SIP) research programme,

understanding the social and economic drivers and constraints of collaboration represent

an important are of enquiry. This report builds upon the literature review undertaken

within the SIP and previous analysis by Defra (2013). Drawing upon data collected within

the 2011/12 FBS for England for 1399 farms, this report has tested the hypotheses that

there was no association between the responses from farmers towards WOEB, EMP,

GHGRP, CPCC, ATI and ABMI and explanatory factors of Farm Type, GOR, FBI, AgO:AgI,

UAA Prop Owned, Farm Size, Education, and Age. Moreover for a sub-sample of 522 farms

the hypotheses were tested in relation to Segmentation groups. The Chi-Squared analysis

was followed by logistic regression analysis to provide analysis of a broad level of practice

uptake while controlling for compounding factors.

Key empirical drivers of collaboration, cooperation, uptake of practices and accessing

information were identified from this report. These included farm business managerial

education level - in particular with an absence of further or higher education associated

with lower practice uptakes. Clear themes also emerged with respect to farmer age, with

respect to lower levels of training uptake, practices to adjust to climate change and reduce

GHG emissions and undertake soil testing, all decreasing as farmer age increased. Farm

businesses achieving greater levels of FBI but lower levels of AO:AI ratios were more likely

to undertake collaborative practices and environmental monitoring, highlighting the

importance of non-agricultural income sources in respect to these areas (Burton et al.,

2008). With respect to ‘farm’ characteristics, both farm type and regional influences were

identified as drivers, being mindful that within England there is a correlation between farm

type and region given regional clustering of particular farm types. Cereals and General

Cropping farms (Defra, 2013) were more likely to engage in collaborative practices and

undertake environmental monitoring, while more specialist livestock producers (Pigs and

Poultry and Dairy) were least likely to collaborative for environmental benefit. Both farm

type and regional influences were present with respect to accessing enterprise specific

knowledge, in particular as observed by the South West accessing greater levels of RDP-

funded technical information. Farm type influences largely confirmed a priori expectations

of undertaking GHG reduction practices and accessing technical information of relevance

to livestock or arable farm types, while farm type influences environmental collaboration

or monitoring practices to a lesser extent. Tenure influences included lower uptake of

non-self-initiated environmental monitoring on farms owning more than 50% of their UAA,

and on farms with more than 75% of their UAA owned, there was a greater proportion

undertaking no GHG reduction practices (and lower uptake of business management

information); water efficiency practices were more likely to be found on farms with more

than 75% of the UAA owned.

While previous studies have identified that farm structural variables are typically only

weakly- or are un- correlated with collaborative activities (Emery and Franks, 2012),

evidence in this report indicates that farm-type specific activities are most likely to

enhance the ability of farm businesses to adjust to market, environmental and social

pressures in the future. The findings reinforce outcomes from previous studies, in

particular that decisions pertaining to collaboration, uptake of environmental practices and

accessing information are largely farmer personality dependent (Wilson et al., 2014),

which in turn is argued to reflect ‘social’ capital factors (Barnes et al., 2013; Schermer et

al., 2011), of which education levels is argued to be a key determinant and a direct

indicator of ‘human’ capital (Mathijs, 2003). Arguably what is required within the context

of sustainable intensification of agriculture, is to focus support towards enhancing

‘agricultural managerial capacity’. While farm businesses have been identified as

accessing technical and business advice (e.g. use of agronomists), these are arguably

“production inputs” to agriculture and do not in themselves enhance ‘agricultural

53

managerial capacity’. However mechanisms that would develop such capacity include

investment in business benchmarking (House of Lords, 2016) and technical events that

engage farmers in an applied context, as evidenced in this report via the uptake of

enterprise-specific RDP training.

5.2 Recommendations

This report has highlighted that individual drivers of farmer collaboration and cooperation

are largely dependent upon farmer characteristics, which reflect the ‘social capital’ of

farmers as captured by their level of engagement with positive ‘nudging’ behaviours (e.g.

Agri-Environmental Schemes and engagement with professional bodies [e.g. Linking

Farming and the Environment, LEAF]) (Barnes, et al., 2013). Social capital is argued to

be influenced by uptake of further and higher education (which is a direct indicator of

‘human capital’), the supply chain with which the business operates, the level of

engagement in non-farming activities, and the broader landscape within which the farm

operates. For example, on the basis of the results presented herein, a Cereal farm that

additionally undertakes non-agricultural business activities and is operated by a

managerial team that includes personnel with further or higher education is likely to be

engaging in collaborative environmental practices, be monitoring environmental

outcomes, obtaining business and technical advice supplied for a charge and also attending

discussion groups, undertaking practices towards greenhouse gas reduction and to adjust

to climate change; this farm business is more likely to be self-characterised as a “modern

family business”. By contrast, a specialist livestock farm business (e.g. Pig, Poultry, Dairy)

with little or no non-farming income activities, operated by an older farmer without further

or higher education, achieving low levels of FBI is considerably less likely to engage with

external activities, undertake collaborative practices, environmental monitoring or

activities and practices to adjust to climate change and reduce greenhouse gas impacts;

this farm business is more likely to be self-characterised as a “challenged enterprise”

business.

In order to support Defra’s Sustainable Intensification Platform, cross-agency investment

and working will be required. Specifically, government, industry and academia should

invest in further and higher agricultural education, in particular towards facilitating

provision of research and teaching to support courses that focus upon the key areas of

Defra’s Sustainable Intensification programme. In so doing, this engagement should be

collaborative with the wider agricultural-environment-food nexus to embed a positive

approach to external engagement of farmers with the direct supply chain, customers and

land custodians and users; examples of these practices currently exist (e.g. LEAF).

In recognising the impact of farmer age on sustainable intensification practices, there

needs to be recognition of the time frames required to achieve wide-spread uptake

of agricultural practices to adjust to a changing climate and reduce GHG emissions from

agricultural production. However, agricultural-technological solutions (e.g. fuel efficient

tractors) exist that were not significantly influenced by farmer age groups. Therefore

incentivising agri-tech solutions that are embedded within agricultural plant and

machinery are likely to enhance uptake of sustainable intensification practices; these

investments should be supported by training programmes that are, in turn, backed by

farmer representation groups (e.g. National Farmers Union).

Key farm type and regional differences have been identified with respect to collaboration

activities towards environmental monitoring. To address these disparities, it will be

necessary to more fully understand the social drivers that underlie farmer behaviour across

different farm types and regions, in order to provide targeted, type and regionally

specific programmes of support. Evidence of successful RDP uptake was found to be

both regional and farm type specific in the South West of England; hence future

government and industry supported training programmes should continue to provide

regional flexibility with respect to programme content, context and method of

54

delivery. Moreover, it is recommended that investment in business benchmarking

services aimed at farm businesses will additionally enhance ‘agricultural managerial

capacity’ which is argued to be central to achieving sustainable intensification objectives.

55

References

Armsworth, P.R., Acs, S., Dallimer, M., Gaston, K.J., Hanley, N and Wilson, P. (2012).

The cost of policy simplification in conservation incentive programs. Ecology Letters,

15(5), 406-14. DOI: 10.1111/j.1461-0248.2012.01747.x

Baranchenko, Y. and Oglethorpe, D. (2012) The Potential Environmental Benefits of Co-

Operative Businesses Within the Climate Change Agenda. Business Strategy and the

Environment, 21, pp. 197-210.

Barnes, A.P., Willock, J., Toma, L. (2013). Comparing a ‘budge’ to a ‘nudge’: farmer

responses to voluntary and compulsory compliance in water quality management regimes. Journal of Rural Studies, 32, 448-459.

Burton, R.J.F, Kuczera, C. and Schwarz, G. (2008). Exploring Farmers’ Cultural

Resistance to Voluntary Agri-environmental Schemes. Sociologia Ruralis 48(1) 16-37 DOI: 10.1111/soru.2008.48

Coleman, J.S. (1988). Social Capital and the Creation of Human Capital, American Journal of Sociology, 94, S95-S120.

Defra (2008). Understanding Behaviours in a Farming Context, Defra Agricultural

Change and Environment Observatory Discussion Paper.

Defra (2013). Business Management Practices on Farm 2011/12, England.

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/201043/

fbs-businessmanagement-statsnotice-22may13.pdf. Accessed 15/1/016.

Defra (2015). 2010 to 2015 government policy: economic growth in rural areas.

https://www.gov.uk/government/publications/2010-to-2015-government-policy-

economic-growth-in-rural-areas/2010-to-2015-government-policy-economic-growth-in-

rural-areas. Accessed 9 July 2015.

Emery, S. B. and Franks, J. R. (2012) The potential for collaborative agri-environment

schemes in England: Can a well-designed collaborative approach address farmers'

concerns with current schemes? Journal of Rural Studies, 28(3), pp. 218-231.

Farmar-Bowers, Q. and Lane, R. (2009). Understanding farmers’ strategic decision-

making processes and the implications for biodiversity conservation policy, Journal of

Environmental Management, 90, 1135-1144.

Garforth, C and Rehman T. (lead authors) (2006) Research to Understand and Model the

Behaviour and Motivations of Farmers in Responding to Policy Changes (England).

Report to Defra, available at:

http://www.defra.gov.uk/evidence/economics/foodfarm/reports/documents/Behaviour.p

df Defra, Accessed 6.1.2011

House of Lords (2016). Responding to price volatility: creating a more resilient

agricultural sector inquiry. EU Energy and Environment Sub Committee.

http://www.parliament.uk/price-volatility-agricultural-resilience Accessed 2 June 2016.

Lehtonen, M. (2004). The environment-social interface of sustainable development:

capabilities, social capital, institutions, Ecological Economics, 49, 199-214.

Mathijs, E. (2003). Social capital and farmers’ willingness to adopt countryside

stewardship schemes, Outlook on Agriculture, 32 (1), 13-16.

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/201043/fbs-businessmanagement-statsnotice-22may13.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/201043/fbs-businessmanagement-statsnotice-22may13.pdf

https://www.gov.uk/government/publications/2010-to-2015-government-policy-economic-growth-in-rural-areas/2010-to-2015-government-policy-economic-growth-in-rural-areas



http://www.defra.gov.uk/evidence/economics/foodfarm/reports/documents/Behaviour.pdf

http://www.defra.gov.uk/evidence/economics/foodfarm/reports/documents/Behaviour.pdf

http://www.parliament.uk/price-volatility-agricultural-resilience%20Accessed%202%20June%202016

56

Mills, J., Gibbon, D., Ingram, J., Reed, M., Short, C. and Dwyer, J. (2011) Organising

Collective Action for Effective Environmental Management and Social Learning in Wales.

The Journal of Agricultural Education and Extension, 17(1), pp. 69-83.

Pretty, J and Ward, H (2001). Social Capital and the Environment, World Development,

29(2), 209-227.

Schermer, M., Renting, H. and Oostindie, H. (2011) Collective farmers’ marketing

initiatives in Europe: diversity, contextuality and dynamics. International Journal of the

Sociology of Agriculture and Food, 18(1), pp. 1-11.

Wilson, P., Harpur, N. and Darling R. (2013). Explaining Variation in Farm and Farm

Business Performance in Respect to Farmer Segmentation Analysis: Implications for

Land Use Policies. Land Use Policy, 30(1), 147-156.

doi:10.1016/j.landusepol.2012.03.006

Wilson, P. Lewis, M and Ackroyd, J. (2014). Farm Business Innovation, Cooperation and

Performance Report submitted to DEFRA Farm Business Economics Division, August

2014. Available at http://www.ruralbusinessresearch.co.uk/

http://www.ruralbusinessresearch.co.uk/

57

Appendix 1: Results of Chi Squared Tests and Logistic Regression Analysis

Table A1.1: Percentage of ways of working with others to deliver environmental benefits

by education level. Note a farm can undertake more than one practice.

No FE.HE Clg.NDC.Cert. Ag.Bus

Degree. Bus.Other Degree.Ag

Postgrad. BusMngt

Chi Sq Sig

NC 69 56 35 43 34 0.0000 *** FDDGN 4 8 15 13 21 0.0000 ***

FDCEABNF 3 5 9 9 9 0.0183 ** BPFDIOA 9 14 14 22 26 0.0005 *** PETPB 17 24 32 30 32 0.0130 ** AETPB 7 13 27 18 26 0.0000 ***

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; Postgrad.BusMngt= Postgraduate qualification in business management or related subject. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.2: Percentage of ways of working with others to deliver environmental benefits

by Farm Type. Note a farm can undertake more than one practice.

Cere

als

Dairy

Genera

l

Cro

ppin

g

Hort

iculture

LFA G

razin

g

Liv

esto

ck

Low

land

Gra

zin

g

Liv

esto

ck

Mix

ed,

Pig

s a

nd

Poultry

Chi Sq

Sig

NC 45 63 47 55 58 54 60 0.1273 FDDGN/FDCEABNF 19 10 17 16 18 10 8 0.1779

BPFDIOA 23 9 19 8 11 14 4 0.0005 ***

PETPB 27 22 31 31 18 24 16 0.0432 ** AETPB 14 8 18 11 13 19 22 0.0152 **

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

58


by Government Office Region (GOR). Note a farm can undertake more than one practice.

NE NW Y&H EM WM EE SE SW Chi Sq Sig

NC 61 65 77 61 56 55 42 42 0.0003 *** FDDGN/ FDCEABNF 20 19 8 16 8 9 21 14 0.0040 ***

BPFDIOA 21 15 10 16 10 17 11 16 0.2636 PETPB/AETPB 21 27 14 29 42 40 53 51 0.0000 ***

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms;

BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).


by Farm Business Income (£/farm) group. Note a farm can undertake more than one

practice.

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Chi Sq Sig

NC 58 64 58 48 53 48 35 0.0118 ** FDDGN 9 7 7 10 13 9 13 0.3138

FDCEABNF 9 4 4 8 7 6 6 0.4931 BPFDIOA 12 8 17 22 14 15 23 0.0002 *** PETPB 23 20 24 25 21 25 39 0.0451 ** AETPB 16 11 11 13 16 17 19 0.3490

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS Farm Business Income (£/farm) groups. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).


by Agricultural Output: Agricultural Input Ratio groups. Note a farm can undertake more

than one practice. <0.75 0.75-1 1-1.25 >1.25 Chi Sq Sig

NC 42 56 57 56 0.2569 FDDGN 11 10 7 8 0.4862

FDCEABNF 14 6 3 6 0.0002 *** BPFDIOA 11 15 14 17 0.3985 PETPB 35 22 25 21 0.0436 ** AETPB 24 16 10 13 0.0014 ***

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS refer to Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

59

Table A 1.6: Percentage of ways of working with others to deliver environmental

benefits by proportion of utilised agricultural area (UAA) owned groups. Note a farm can

undertake more than one practice.

<0.25 0.25-0.5 0.5-0.75 >0.75 Chi Sq Sig

NC 51 57 52 57 0.5378

FDDGN 10 9 10 8 0.7426

FDCEABNF 8 7 4 5 0.1888

BPFDIOA 17 9 15 14 0.2836

PETPB 23 27 29 23 0.4699

AETPB 16 13 8 14 0.1532

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS refer to proportion of utilised agricultural area (UAA) owned by the farmer; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).


by farmer age (in years; youngest person with managerial responsibility) groups. Note a

farm can undertake more than one practice.

<30 30-39 40-49 50-59 60-69 70+ Chi Sq Sig

NC 56 49 58 52 58 61 0.7165 FDDGN/FDCEABNF 14 16 16 15 11 11 0.6310

BPFDIOA 18 16 14 16 12 9 0.5349 PETPB 23 27 13 26 23 20 0.0036 *** AETPB 8 13 15 14 12 11 0.6099

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS Farmer age groups (in years). Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).


by segmentation groups. Note a farm can undertake more than one practice.

C/LC/CE P MFB Chi Sq Sig

NC 55 59 49 0.4927 FDDGN/ FDCEABNF 13 7 5 0.0797 *

BPFDIOA 14 15 16 0.8721 PETPB 27 20 30 0.1494 AETPB 15 11 11 0.4372

Key: ROWS NC=No collaboration; FDDGN=Farmer-driven discussion groups/networks of farmers; FDCEABNF=Farmer-driven coordination of environmental activities and benefits with neighbouring farms; BPFDIOA=As a by-product from farmer-driven initiatives which have other aims e.g. shooting syndicates; PETPB=Passive engagement (e.g. discussion groups) through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts; AETPB=Active engagement through third-party bodies e.g. RSPB, FWAG, GAME conservancy and wildlife trusts. COLUMNS C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

60

Table A1.9: Percentage of Environmental monitoring practices relating to biodiversity,

conservation and habits by education level. Note a farm can undertake more than one

practice.



Postgrad. BusMngt

Chi Sq Sig

NP 36 22 20 14 13 0.0000 *** WBSI 31 32 37 32 40 0.7896

WBNSI 9 13 19 18 21 0.0100 ** OMSI 25 24 30 27 38 0.3852

OMNSI 8 11 19 22 23 0.0000 *** ST 43 63 60 74 72 0.0000 ***

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; Postgrad.BusMngt= Postgraduate qualification in business management or related subject. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.10: Percentage of Environmental monitoring practices relating to biodiversity,

conservation and habits by Farm Type. Note a farm can undertake more than one practice.

Cere

als

Dairy

Genera

l

Cro

ppin

g

Hort

iculture

LFA

Gra

zin

g

Liv

esto

ck

Low

land

Gra

zin

g

Liv

esto

ck

Mix

ed

Pig

s a

nd

Poultry

Chi Sq Sig

NP 12 27 13 27 33 28 15 44 0.0000 ***

WBSI 39 29 41 32 26 33 33 19 0.0618 *

WBNSI 17 7 19 16 16 14 12 7 0.0200 **

OMSI 27 21 27 23 25 33 26 18 0.2752

OMNSI 17 8 15 13 16 15 12 7 0.1725

ST 79 59 81 49 42 44 77 40 0.0000 ***

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil

testing. COLUMNS Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

61


conservation and habits by Government Office Region (GOR). Note a farm can undertake

more than one practice.


NP 13 33 42 24 17 16 14 30 0.0000 ***

WBSI 42 27 9 40 29 43 32 30 0.0000 ***

WBNSI 24 12 12 12 9 17 25 5 0.0000 ***

OMSI 36 21 7 33 26 33 23 24 0.0002 ***

OMNSI 22 13 11 11 11 14 26 5 0.0000 ***

ST 66 50 52 59 65 64 72 53 0.1311

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A1.12: Percentage of Environmental monitoring practices relating to biodiversity,

conservation and habits by Farm Business Income (£/farm) group. Note a farm can


<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Chi Sq Sig

NP 28 33 25 19 16 16 9 0.0000 ***

WBSI 34 30 36 34 29 25 38 0.4794

WBNSI 13 9 11 15 19 19 21 0.0131 **

OMSI 32 26 28 24 22 18 28 0.3475

OMNSI 10 10 11 16 19 17 18 0.0763 *

ST 53 46 53 67 77 77 83 0.0000 ***

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS Farm Business Income (£/farm) groups. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A.1.13: Percentage of Environmental monitoring practices relating to biodiversity,

conservation and habits by Agricultural Output: Agricultural Input Ratio groups. Note a

farm can undertake more than one practice. <0.75 0.75-1 1-1.25 >1.25 Chi Sq Sig

NP 30 24 23 19 0.4006

WBSI 35 34 31 37 0.6288

WBNSI 19 14 13 11 0.2821

OMSI 33 28 24 21 0.1642

OMNSI 15 13 13 11 0.8282

ST 41 59 62 70 0.0163 **

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS refer to Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

62


conservation and habits by proportion of utilised agricultural area (UAA) owned groups.

Note a farm can undertake more than one practice.

<0.25 0.25-0.5 0.5-0.75 >0.75 Chi Sq Sig

NP 23 22 18 26 0.3205 WBSI 31 25 40 33 0.1930 WBNSI 17 12 13 12 0.1986 OMSI 27 17 31 25 0.1182 OMNSI 18 16 10 11 0.0148 ** ST 58 67 65 58 0.5096

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS refer to proportion of utilised agricultural area (UAA) owned by the farmer; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).


conservation and habits by farmer age (in years; youngest person with managerial

responsibility) groups. Note a farm can undertake more than one practice.

<30 30-39 40-49 50-59 60+ Chi Sq Sig

NP 21 19 23 25 27 0.5320

WBSI 32 32 31 31 38 0.5442

WBNSI 12 13 14 14 13 0.9826

OMSI 23 27 21 25 34 0.0386 **

OMNSI 12 15 11 14 13 0.6726

ST 69 68 64 56 51 0.0868 *

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS Farmer age groups (in years). Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).


conservation and habits by segmentation group. Note a farm can undertake more than

one practice.

C LC/CE P MFB Chi Sq Sig

NP 37 22 25 18 0.1051

WBSI 36 35 28 30 0.6746

WBNSI 13 15 13 15 0.9625

OMSI 31 31 21 23 0.2571

OMNSI 13 9 13 11 0.8823

ST 40 50 72 73 0.0092 ***

Key: ROWS NP=No practices; WBSI=Wild birds. E.g. bird counts (self-initiated); WBNSI= Wild birds. E.g. bird counts (not self-initiated, e.g. as part of assurance scheme); OMSI=Other monitoring, e.g. floral, habitats, wild animals (self-initiated); OMNSI= Other monitoring, e.g. floral, habitats, wild animals (not self-initiated); ST=Soil testing. COLUMNS C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

63

Table A 1.17: Percentage of practices to reduce greenhouse gas (GHG) emissions by

education level. Note a farm can undertake more than one practice.



Postgrad. BusMngt

Chi Sq Sig

NI 58 40 37 34 35 0.0000 *** INM 22 35 46 43 43 0.0000 ***

ISMM 17 28 32 27 27 0.0190 ** ISD 20 26 22 29 35 0.0666 *

LHAD 7 13 16 12 18 0.0139 ** FELET 15 24 17 20 20 0.0403 **

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS No FE.HE= No further or higher level education;

Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; Postgrad.BusMngt= Postgraduate qualification in business management or related subject. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘Other’ due to small number of observations (<5) in a number of cells.


Farm Type. Note a farm can undertake more than one practice.

Cere

als

Dairy

Genera

l

Cro

ppin

g a

nd

Hort

iculture

LFA

Gra

zin

g

Liv

esto

ck

Low

land

Gra

zin

g

Liv

esto

ck

Mix

ed

Pig

s a

nd

Poultry

Chi Sq Sig

NI 34 27 47 55 56 34 56 0.0000 ***

INM 46 43 37 22 24 39 15 0.0000 ***

ISMM 13 57 8 25 22 31 24 0.0000 ***

ISD 36 30 23 23 17 31 11 0.0000 ***

LHAD 4 23 2 10 12 15 24 0.0000 ***

FELET 35 23 18 16 13 28 5 0.0000 ***

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘Other’ due to small number of observations (<5) in a number of cells.

Table A. 1.19: Percentage of practices to reduce greenhouse gas (GHG) emissions by

Government Office Region (GOR). Note a farm can undertake more than one practice.


NI 33 47 59 30 42 53 37 45 0.0015 ***

INM 33 29 26 44 34 27 40 36 0.0445 **

ISMM 32 32 23 26 27 16 19 30 0.0141 **

ISD 34 31 17 32 29 20 25 20 0.0159 **

LHAD 18 11 11 12 20 6 11 10 0.0146 **

FELET 38 14 11 39 10 13 31 15 0.0000 ***

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99. Excludes ‘Other’ due to small number of observations (<5) in a number of cells.

64

Table A 1.20: Percentage of practices to reduce greenhouse gas (GHG) emissions by by

Farm Business Income (£/farm) group. Note a farm can undertake more than one practice.

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Chi Sq Sig

NI 46 63 44 29 40 26 20 0.0000 ***

INM 30 21 31 43 39 45 61 0.0000 ***

ISMM 25 17 25 34 29 36 25 0.0005 ***

ISD 23 17 23 29 31 35 40 0.0000 ***

LHAD 15 10 13 10 8 13 12 0.5225

FELET 18 9 19 23 31 30 41 0.0000 ***

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS Farm Business Income (£/farm) groups Chi Sq= Chi-squared test

result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99. Excludes ‘Other’ due to small number of observations (<5) in a number of cells.


Agricultural Output: Agricultural Input Ratio groups. Note a farm can undertake more

than one practice. <0.75 0.75-1 1-1.25 >1.25 Chi Sq Sig

NI 56 49 39 35 0.0047 ***

INM 21 30 37 45 0.0022 ***

ISMM 15 24 28 19 0.0100 **

ISD 19 22 28 26 0.1246

LHAD 10 13 12 2 0.0177 **

FELET 11 18 23 24 0.0239 **

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS refer to Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘Other’ due to small number of observations (<5) in a number of cells.


proportion of utilised agricultural area (UAA) owned groups. Note a farm can undertake


<0.25 0.25-0.5 0.5-0.75 >0.75 Chi Sq Sig

NI 44 38 31 47 0.0469 **

INM 36 33 48 31 0.0087 ***

ISMM 29 31 31 21 0.0153 **

ISD 21 32 32 25 0.0569 *

LHAD 12 14 14 10 0.4880

FELET 25 26 34 15 0.0000 ***

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS refer to proportion of utilised agricultural area (UAA) owned by the farmer; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘Other’ due to small number of observations (<5) in a number of cells.

65

Table A.1.23: Percentage of practices to reduce greenhouse gas (GHG) emissions by

farmer age (in years; youngest person with managerial responsibility) groups. Note a farm

can undertake more than one practice.

<30 30-39 40-49 50-59 60+ Chi Sq Sig

NI 25 33 38 48 58 0.0000 ***

INM 45 44 40 30 22 0.0000 ***

ISMM 41 36 31 20 12 0.0000 ***

ISD 39 33 27 20 21 0.0006 ***

LHAD 15 15 13 11 6 0.0122 **

FELET 25 24 22 20 15 0.1295

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS Farmer age groups (in years). Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘Other’ due to small number of observations (<5) in a number of cells.

Table A.1.24: Percentage of practices to reduce greenhouse gas (GHG) emissions by

segmentation group. Note a farm can undertake more than one practice.


NI 55 55 46 38 0.2919

INM 23 35 33 42 0.1875

ISMM 12 29 22 33 0.0321 **

ISD 22 24 22 29 0.6073

LHAD 8 13 11 6 0.4474

FELET 18 13 19 24 0.4255

Key: ROWS NI=No intervention; INM=Improved nutrient management; ISMM=Improved slurry/manure management; ISD=Improved soil drainage; LHAD=Livestock health and adjustments to diet; FELET=Fuel efficient/low emissions tractors. COLUMNS C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘Other’ due to small number of observations (<5) in a number of cells.

Table A. 1.25: Percentage of current practices to adjust to climate change by education

level. Note a farm can undertake more than one practice.



Postgrad. BusMngt

Chi Sq Sig

NI 42 33 27 29 20 0.0070 ***

WE 25 33 39 34 42 0.0476 **

WQ 18 22 20 23 32 0.1941

LUCEP 14 20 22 23 27 0.0579 *

LS 21 23 26 21 20 0.8158

CS 12 19 21 20 22 0.0931 *

SM 31 44 47 49 55 0.0015 ***

SK 6 12 21 13 25 0.0001 ***

SA 7 17 20 16 23 0.0003 ***

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; Postgrad.BusMngt= Postgraduate qualification in business management or related subject. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

66

Table A 1.26: Percentage of current practices to adjust to climate change by Farm Type.


Cere

als

Dairy

Genera

l

Cro

ppin

g

and

Hort

iculture

LFA

Gra

zin

g

Liv

esto

ck

Low

land

Gra

zin

g

Liv

esto

ck

Mix

ed,

Pig

s a

nd

Poultry

Chi Sq Sig

NI 24 30 27 41 40 33 0.0003 ***

WE 21 49 46 29 27 26 0.0000 ***

WQ 23 26 25 21 20 19 0.0685 *

LUCEP 27 19 19 18 18 22 0.0480 **

LS 10 30 7 30 29 34 0.0000 ***

CS 33 11 26 10 16 26 0.0000 ***

SM 63 45 43 32 34 45 0.0000 ***

SK 18 13 12 8 10 12 0.0538 *

SA 20 15 14 12 12 13 0.1897

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.27: Percentage of current practices to adjust to climate change by Government

Office Region (GOR). Note a farm can undertake more than one practice.


NI 31 38 56 17 24 32 35 40 0.0000 ***

WE 21 37 18 37 44 27 35 32 0.0026 ***

WQ 12 19 6 22 15 34 20 25 0.0000 ***

LUCEP 19 16 8 22 21 26 11 23 0.0018 ***

LS 22 27 13 38 41 17 10 15 0.0000 ***

CS 20 6 10 28 26 22 16 13 0.0000 ***

SM 51 36 29 55 50 43 42 35 0.0035 ***

SK 9 13 10 19 12 10 14 8 0.0572 *

SA 11 13 12 21 21 15 15 9 0.0200 **

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

67

Table A. 1.28: Percentage of current practices to adjust to climate change by Farm

Business Income (£/farm) group. Note a farm can undertake more than one practice.

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Chi Sq Sig

NI 38 44 38 26 29 20 16 0.0000 ***

WE 32 27 28 33 37 39 49 0.0059 ***

WQ 15 17 20 26 24 23 39 0.0004 ***

LUCEP 18 14 17 25 20 24 31 0.0033 ***

LS 23 24 20 24 24 23 17 0.7524

CS 15 12 13 22 24 25 33 0.0000 ***

SM 32 32 37 51 47 57 69 0.0000 ***

SK 12 8 11 17 15 15 15 0.0364 **

SA 19 9 14 17 15 17 20 0.0286 **

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS Farm Business Income (£/farm) groups. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.29: Percentage of current practices to adjust to climate change by Agricultural

Output: Agricultural Input Ratio groups. Note a farm can undertake more than one

practice. <0.75 0.75-1 1-1.25 >1.25 Chi Sq Sig

NI 37 35 33 26 0.4673

WE 26 29 34 39 0.1859

WQ 20 19 23 25 0.4507

LUCEP 25 18 18 22 0.3379

LS 29 23 22 11 0.0193 **

CS 18 16 19 17 0.6740

SM 31 41 44 51 0.0895 *

SK 13 10 13 9 0.2691

SA 17 14 13 17 0.5717

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS refer to Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.30: Percentage of current practices to adjust to climate change by proportion

of utilised agricultural area (UAA) owned groups. Note a farm can undertake more than

one practice.

<0.25 0.25-0.5 0.5-0.75 >0.75 Chi Sq Sig

NI 33 31 29 35 0.5655

WE 27 33 34 34 0.2744

WQ 19 27 25 21 0.3406

LUCEP 21 19 20 19 0.8300

LS 25 27 26 20 0.1650

CS 19 19 18 17 0.9597

SM 47 51 48 38 0.0337 **

SK 16 14 13 10 0.0518 *

SA 16 15 15 13 0.6516

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS refer to proportion of utilised agricultural area (UAA) owned by the farmer; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

68

Table A 1.31: Percentage of current practices to adjust to climate change by farmer age

(in years; youngest person with managerial responsibility) groups. Note a farm can


<30 30-39 40-49 50-59 60+ Chi Sq Sig

NI 27 29 32 32 44 0.0227 **

WE 31 38 34 32 25 0.1156

WQ 24 22 23 20 21 0.8682

LUCEP 22 19 18 22 16 0.4321

LS 29 25 24 23 15 0.0433 **

CS 17 21 19 18 15 0.6266

SM 52 47 43 44 32 0.0468 **

SK 10 13 12 15 7 0.0281 **

SA 11 15 15 17 10 0.1159

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS Farmer age groups (in years). Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.32: Percentage of current practices to adjust to climate change by segmentation

group. Note a farm can undertake more than one practice.


NI 33 42 32 29 0.5931

WE 37 25 31 46 0.0744 *

WQ 15 15 20 28 0.1301

LUCEP 12 11 19 23 0.1914

LS 25 20 23 14 0.2702

CS 16 11 17 18 0.7405

SM 36 36 45 47 0.5370

SK 8 13 11 16 0.5176

SA 8 15 12 16 0.5554

Key: ROWS NI=No intervention; WE=Water efficiency; WQ=Water quality; LUCEP=Land use change and environmental protection; LS=Livestock sustainability; CS=Crop sustainability; SM=Soil management; SK=Sharing knowledge; SA=Seeking advice. COLUMNS C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A. 1.33: Access to technical information by education level. Note a farm can


No FE.HE

Clg.NDC.Cert. Ag.Bus

Degree. Bus.Other

Degree.Ag

Postgrad. BusMngt

Chi Sq Sig

TOF 68 73 65 70 62 0.7587 FM 84 87 80 85 75 0.8586 ED 46 63 65 69 65 0.0016 *** DG 37 60 61 62 60 0.0000 *** TANC 72 74 73 75 65 0.9399 TAWC 25 37 37 46 52 0.0001 *** RAHT 7 12 20 14 17 0.0093 *** RTT 5 10 13 13 12 0.0089 ***

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme. COLUMNS No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; Postgrad.BusMngt= Postgraduate qualification in business management or related subject.Chi Sq= Chi-squared

69

test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A 1.34: Access to technical information by Farm Type. Note a farm can undertake


Cere

als

Dairy

Genera

l

Cro

ppin

g

and

Hort

iculture

LFA

Gra

zin

g

Liv

esto

ck

Low

land

Gra

zin

g

Liv

esto

ck

Mix

ed

Pig

s

and

Poultry

Chi Sq Sig

TOF 66 78 64 83 69 68 66 0.1702 FM 88 89 81 88 82 87 78 0.8873 ED 67 63 59 58 54 62 55 0.6190 DG 59 66 51 44 50 61 46 0.0350 ** TANC 71 79 70 68 70 81 76 0.7341 TAWC 51 46 44 12 20 33 37 0.0000 *** RAHT 11 38 7 24 25 28 24 0.0000 ***

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong

technical theme. COLUMNS Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A 1.35: Access to technical information by Government Office Region (GOR). Note

a farm can undertake more than one practice.


TOF 75 81 88 73 64 61 64 70 0.0767 * FM 94 85 92 90 80 82 79 85 0.8517 ED 73 67 65 69 44 54 60 57 0.0434 ** DG 54 59 60 61 40 42 64 56 0.0120 ** TANC 85 73 78 72 76 66 68 76 0.7201 TAWC 21 31 34 43 34 47 46 24 0.0000 *** RAHT 15 16 6 8 5 4 8 27 0.0000 *** RTT 15 11 5 7 7 5 4 18 0.0000 ***

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme. COLUMNS NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A. 1.36: Access to technical information by Farm Business Income (£/farm) group.


<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Chi Sq Sig

TOF 68 68 74 72 70 70 69 0.9673 FM 76 83 86 90 84 88 88 0.8636 ED 60 47 61 66 64 64 82 0.0011 *** DG 47 41 54 62 61 68 75 0.0000 *** TANC 73 69 74 78 73 73 75 0.9595 TAWC 36 22 31 37 42 61 62 0.0000 *** RAHT 13 9 12 15 13 17 10 0.1796

70

RTT 8 7 8 11 14 11 14 0.1752

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme. COLUMNS Farm Business Income (£/farm) groups. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

71

Table A 1.37: Access to technical information by Agricultural Output: Agricultural Input

Ratio groups. Note a farm can undertake more than one practice. <0.75 0.75-1 1-1.25 >1.25 Chi Sq Sig

TOF 67 70 70 75 0.8925 FM 77 80 89 84 0.3226 ED 52 56 63 62 0.2362 DG 47 49 57 66 0.0746 * TANC 68 71 76 71 0.6750 TAWC 21 32 39 51 0.0003 *** RAHT 8 14 12 10 0.3835 RTT 6 9 10 11 0.3349

Group 0.2414

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong

technical theme. COLUMNS refer to Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A 1.38: Access to technical information by proportion of utilised agricultural area

(UAA) owned groups. Note a farm can undertake more than one practice.

<0.25 0.25-0.5 0.5-0.75 >0.75 Chi Sq Sig

TOF 74 75 72 68 0.6779 FM 90 84 89 82 0.6022 ED 62 60 64 58 0.7667 DG 57 60 64 50 0.1223 TANC 75 76 72 72 0.9114 TAWC 32 45 45 34 0.0264 ** RAHT 13 19 10 11 0.1158 RTT 9 13 4 10 0.1222

Group 0.3060

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme. COLUMNS refer to proportion of utilised agricultural area (UAA) owned by the farmer; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

72

Table A 1.39: Access to technical information by farmer age (in years; youngest person

with managerial responsibility) groups. Note a farm can undertake more than one practice.

<30 30-39 40-49 50-59 60+ Chi Sq Sig

TOF 84 78 71 70 59 0.0620 * FM 89 87 86 85 79 0.8103 ED 68 66 61 63 50 0.0782 * DG 60 66 57 54 45 0.0072 *** TANC 76 79 73 75 64 0.4528 TAWC 37 41 41 33 32 0.0783 * RAHT 19 17 13 12 4 0.0001 *** RTT 12 13 13 8 3 0.0003 ***

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme. COLUMNS Farmer age groups (in years). Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A 1.40: Access to technical information by segmentation group. Note a farm can



TOF 66 62 72 68 0.8482 FM 74 71 87 87 0.5018 ED 44 45 61 61 0.1953 DG 34 42 56 65 0.0224 ** TANC 62 67 78 67 0.3644 TAWC 32 29 35 55 0.0081 *** RAHT/RTT 8 13 22 22 0.0628 *

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; TANC=Through technical advice supplied with no direct charge, e.g. input supplier; TAWC= Through technical advice supplied for a charge; RAHT=Through RDP-funded initiatives with a strong animal health theme; RTT= Through RDP-funded initiatives with a strong technical theme. COLUMNS C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

73

Table A. 1.41: Access to business management information by education level. Note a

farm can undertake more than one practice.

No FE.HE

Clg.NDC.Cert. Ag.Bus

Degree. Bus.Other

Degree.Ag

Postgrad. BusMngt

Chi Sq Sig

NI 7 4 7 3 8 0.0767 * TOF 51 62 49 56 52 0.1428 FM 72 77 64 74 58 0.3975 ED 35 58 53 62 57 0.0000 *** DG 29 50 50 51 57 0.0000 *** ANC 54 61 66 63 50 0.4298 AWC 20 29 32 38 45 0.0003 *** RBMT 2 8 12 9 13 0.0008 ***

Key: ROWS NI=None identified; TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS COLUMNS No FE.HE= No further or higher level education; Clg.NDC.Cert.Ag.Bus= College national diploma/certificate in agriculture, related subject or in business management, accounting, marketing, economics or related subject; Degree.Ag= Degree in agriculture or a related subject; Degree.Bus.Other= Degree in business management, accounting, marketing, economics or related subject or any other subject; Postgrad.BusMngt= Postgraduate qualification in business management or related subject. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

Table A 1.42: Access to business management information by Farm Type. Note a farm

can undertake more than one practice.

Cere

als

Dairy

Genera

l

Cro

ppin

g a

nd

Hort

iculture

LFA

Gra

zin

g

Liv

esto

ck

Low

land

Gra

zin

g

Liv

esto

ck

Mix

ed

Pig

s

and

Poultry

Chi Sq Sig

NI 57 64 52 70 53 52 48 0.0826 * TOF 78 75 63 82 71 78 72 0.3394 FM 60 59 49 49 43 54 50 0.1375 ED 52 58 39 39 41 45 38 0.0099 *** DG 61 58 58 59 57 69 55 0.8323 ANC 38 45 26 18 21 23 29 0.0000 *** AWC 7 10 4 9 8 8 7 0.1794 RBMT 57 64 52 70 53 52 48 0.0826 *

Key: ROWS NI=None identified; TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

74

Table A 1.43: Access to business management information by Government Office Region

(GOR). Note a farm can undertake more than one practice.


TOF 61 71 74 65 45 57 45 50 0.0011 *** FM 87 83 87 85 65 76 54 66 0.0014 *** ED 62 62 56 66 41 51 43 46 0.0047 *** DG 44 55 50 56 37 40 46 39 0.0325 ** ANC 73 56 68 51 70 59 49 61 0.0801 * AWC/RBMT 42 40 24 35 28 40 41 36 0.1422

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS NE=North East; NW=North West; Y&H= Yorkshire and the Humber; EM=East Midlands; WM=West Midlands; EE=East of England; SE=South East; SW=South West; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A. 1.44: Access to business management information by Farm Business Income

(£/farm) group. Note a farm can undertake more than one practice.

<0 0-30k 30-60k 60-90k 90-120k 120-200k 200k+

Chi Sq Sig

TOF 52 54 63 60 56 58 54 0.7363 FM 67 69 78 81 74 71 75 0.6116 ED 55 38 51 62 63 54 73 0.0000 *** DG 41 33 47 53 47 53 63 0.0003 *** ANC 56 54 59 65 69 58 66 0.4089 AWC 32 17 26 28 33 42 58 0.0000 *** RBMT 13 6 6 8 7 8 10 0.1101

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS Farm Business Income (£/farm) groups. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

75

Table A 1.45: Access to business management information by Agricultural Output:

Agricultural Input Ratio groups. Note a farm can undertake more than one practice. <0.75 0.75-1 1-1.25 >1.25 Chi Sq Sig

TOF 49 56 58 64 0.4247 FM 69 71 75 77 0.7278 ED 39 53 53 60 0.1067 DG 38 42 46 61 0.0363 ** ANC 56 58 60 65 0.8157 AWC 20 30 29 38 0.0492 ** RBMT 8 10 6 6 0.0820 *

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS refer to Agricultural Output value divided by Agricultural Input cost where measures <1 refer to costs exceeding value of output and >1 refers to value of outputs exceeding costs; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A 1.46: Access to business management information by proportion of utilised

agricultural area (UAA) owned groups. Note a farm can undertake more than one practice.

<0.25 0.25-0.5 0.5-0.75 >0.75 Chi Sq Sig

NI 3 5 4 7 0.0857 * TOF 64 55 62 53 0.1399 FM 77 70 78 71 0.6152 ED 52 51 55 52 0.9670 DG 48 48 54 42 0.2040 ANC 60 64 64 57 0.6356 AWC 31 34 35 26 0.1265 RBMT 9 11 8 6 0.1121

Key: ROWS NI=None identified; TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS refer to proportion of utilised agricultural area (UAA) owned by the farmer; Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

76

Table A 1.47: Access to business management information by farmer age (in years;

youngest person with managerial responsibility) groups. Note a farm can undertake more

than one practice.

<30 30-39 40-49 50-59 60-69 Chi Sq Sig

TOF 68 61 59 56 50 0.2848 FM 78 74 78 74 65 0.3763 ED 56 57 55 53 41 0.0617 * DG 47 56 46 46 35 0.0174 ** ANC 70 69 60 57 52 0.0772 * AWC 25 35 33 27 23 0.0666 * RBMT 13 11 9 7 3 0.0035 **

Key: ROWS TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS Farmer age groups (in years). Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%). Excludes ‘None identified’ due to small number of observations (<5) in a number of cells.

Table A 1.48: Access to business management information by segmentation group. Note

a farm can undertake more than one practice.


NI 10 9 4 5 0.1399

TOF 52 51 60 53 0.6818 FM 66 62 77 75 0.5493 ED 30 36 55 56 0.0164 ** DG 29 29 48 55 0.0143 ** ANC 48 53 60 56 0.6603 AWC/RBMT 33 31 32 48 0.0900 *

Key: ROWS NI=None identified; TOF=Through talking to other farmers; FM=Through the farming media e.g. internet sites, trade magazines; ED=Through events and demonstrations e.g. meetings organised by banks/accountancy firms/levy bodies; DG=Through discussion groups, farm walks or workshops; ANC=Through advice supplied with no direct charge, e.g. casual discussion with bank manager or accountant, or subsidised specific advice (e.g. FBAS); AWC= Through specific business advice supplied for a charge (e.g. consultant; RBMT=Through RDP-funded initiatives with a strong business management theme. COLUMNS C=Custodian; LC=Lifestyle Choice; P=Pragmatist; MFB=Modern Family Business; CE=Challenged Enterprise. Chi Sq= Chi-squared test result (p-value); Sig=statistical significance level (*=90%; **=95%; ***=99%).

77

Table A 1.49: Logistic Regression of Working with Others to Achieve Environmental

Benefit.

Parameter estimate s.e. t(*) t pr. antilog of estimate

Constant -13.189 0.431 -30.59 <.001 1.87E-06

Farm Business Income 0.000000722 3.28E-07 2.2 0.028 1

East of England -0.138 0.295 -0.47 0.639 0.8707

North East 2.049 0.366 5.6 <.001 7.758

North West 0.971 0.301 3.22 0.001 2.641

South East 0.571 0.288 1.98 0.047 1.77

South West 0.375 0.265 1.41 0.157 1.455

West Midlands 0.182 0.317 0.57 0.566 1.2

Yorkshire & Humber -0.621 0.462 -1.35 0.178 0.5373

Dairy -0.624 0.273 -2.29 0.022 0.5355

General Cropping 0.119 0.278 0.43 0.668 1.127

Horticulture -0.379 0.342 -1.11 0.268 0.6846

LFA Grazing Livestock -0.075 0.28 -0.27 0.789 0.9279 Lowland Grazing Livestock 0.011 0.234 0.05 0.961 1.012

Mixed 0.133 0.259 0.51 0.608 1.142

Pig & Poultry -0.617 0.412 -1.50 0.134 0.5394

Degree.Ag 0.549 0.181 3.04 0.002 1.731

Degree.Bus.Oth 0.389 0.265 1.47 0.142 1.476

No.FE.HE -0.688 0.238 -2.89 0.004 0.5027

PG 0.878 0.28 3.14 0.002 2.405

Age 0.01269 0.0066 1.92 0.055 1.013

Table A 1.50: Logistic Regression of Environmental Monitoring Practices.


Constant -12.17 0.343 -35.51 <.001 5.19E-06

Farm Business Income 6.94E-07 2.66E-07 2.61 0.009 1

East of England -0.065 0.200 -0.32 0.747 0.9375

North East 2.255 0.253 8.9 <.001 9.539

North West 0.158 0.262 0.6 0.546 1.171

South East 0.525 0.211 2.48 0.013 1.69

South West -0.774 0.241 -3.22 0.001 0.4611

West Midlands -0.347 0.258 -1.35 0.178 0.7065


Dairy -0.488 0.219 -2.23 0.026 0.6136


Horticulture -0.74 0.318 -2.33 0.02 0.4771

LFA Grazing Livestock -0.318 0.248 -1.29 0.199 0.7275 Lowland Grazing Livestock -0.339 0.205 -1.65 0.098 0.7121

Mixed -0.099 0.214 -0.46 0.642 0.9054

Pig & Poultry -1.016 0.377 -2.7 0.007 0.3622

Degree in Agriculture 0.278 0.159 1.75 0.08 1.321 Degree in Business/other 0.121 0.252 0.48 0.63 1.129 No Further or Higher Education -0.286 0.176 -1.63 0.103 0.751

Postgraduate Degree 0.594 0.242 2.45 0.014 1.81

Age 0.00922 0.00557 1.66 0.098 1.009

78

Table A 1.51: Logistic Regression of Greenhouse Gas Reduction Practices.


Constant -10.817 0.32 -33.83 <.001 2.01E-05

Farm Business Income 0.000000986 0.000000279 3.54 <.001 1

East of England -0.827 0.244 -3.39 <.001 0.4372

North East 1.383 0.272 5.08 <.001 3.986

North West 0.137 0.23 0.60 0.55 1.147

South East 0.213 0.218 0.98 0.328 1.238

South West -0.679 0.207 -3.28 0.001 0.5071

West Midlands -0.292 0.229 -1.27 0.203 0.7469


Dairy 0.364 0.193 1.89 0.059 1.439


Horticulture -1.316 0.381 -3.46 <.001 0.2682


Mixed 0.073 0.231 0.32 0.753 1.076

Pig & Poultry -0.404 0.316 -1.28 0.202 0.6679

Age -0.02004 0.00538 -3.73 <.001 0.9802

Table A 1.52: Logistic Regression of Practices to Adjust to Climate Change.


Constant -11.698 0.192 -60.92 <.001 8.31E-06

Farm Business Income 0.000000751 2.56E-07 2.93 0.003 1

East of England -0.316 0.207 -1.53 0.127 0.7287

North East 1.099 0.327 3.36 <.001 3.002

North West 0.041 0.249 0.16 0.87 1.041

South East -0.276 0.249 -1.11 0.267 0.7587

South West -0.553 0.209 -2.64 0.008 0.5754

West Midlands 0.109 0.213 0.51 0.61 1.115


Dairy 0.101 0.204 0.5 0.62 1.106


Horticulture -0.764 0.303 -2.52 0.012 0.4656


Mixed 0.196 0.229 0.85 0.393 1.216

Pig & Poultry -0.465 0.306 -1.52 0.128 0.6279

Degree in Agriculture 0.004 0.165 0.03 0.98 1.004 Degree in Business/other 0.225 0.231 0.97 0.33 1.253 No Further or Higher Education -0.719 0.189 -3.8 <.001 0.4873


79

Table A 1.53: Logistic Regression of Accessing Technical Information.


Constant -11.14 0.114 -97.36 <.001 1.45E-05


East of England -0.592 0.16 -3.71 <.001 0.5532

North East 1.505 0.197 7.64 <.001 4.506

North West 0.608 0.151 4.02 <.001 1.837

South East 0.205 0.156 1.32 0.188 1.227

South West -0.281 0.138 -2.04 0.041 0.7551

West Midlands -0.458 0.18 -2.54 0.011 0.6327

Yorkshire & Humber 0.056 0.174 0.32 0.747 1.058

Degree in Agriculture 0.097 0.109 0.89 0.375 1.102 Degree in Business/other 0.051 0.165 0.31 0.759 1.052 No Further or Higher

Education -0.328 0.115 -2.85 0.004 0.72


Table A 1.54: Logistic Regression of Accessing Business Management Information.


Constant -10.923 0.131 -83.37 <.001 1.80E-05


East of England -0.172 0.139 -1.24 0.216 0.8416

North East 1.571 0.185 8.48 <.001 4.81

North West 0.474 0.15 3.16 0.002 1.607

South East -0.034 0.163 -0.21 0.835 0.9666

South West -0.476 0.14 -3.41 <.001 0.621

West Midlands -0.668 0.185 -3.61 <.001 0.5129


Dairy 0.097 0.134 0.73 0.468 1.102


Horticulture -0.526 0.183 -2.88 0.004 0.5907

LFA Grazing Livestock 0.052 0.157 0.33 0.742 1.053 Lowland Grazing Livestock -0.131 0.148 -0.89 0.373 0.8768

Mixed -0.034 0.159 -0.22 0.829 0.9662

Pig & Poultry -0.23 0.181 -1.27 0.204 0.7944

Degree in Agriculture 0.046 0.106 0.43 0.665 1.047 Degree in Business/other -0.027 0.168 -0.16 0.874 0.9738 No Further or Higher Education -0.474 0.115 -4.14 <.001 0.6226


Documents

Analysis of Farm Business Survey 2011-12 Business ...sciencesearch.defra.gov.uk/...Document=14151_SIP2_WP2.3A_T3_Fi… · Funding for the SIP from Defra and The Welsh Government is