THE RATIONALE FOR DEFRA INVESTMENT IN R&D UNDERPINNING THE GENETIC IMPROVEMENT OF CROPS

AND ANIMALS (IF0101)

FINAL REPORT TO DEFRA

DOMINIC MORAN§ ANDREW BARNES

ALISTAIR MCVITTIE

SAC COMMERCIAL LTD SAC LAND ECONOMY AND ENVIRONMENT GROUP

12 MARCH 2007

§ dominic.moran@sac.ac.uk

Acknowledgements

Many people have given their time and forbearance in the preparation of this report. We would particularly like to thank participants in our original discussion groups, and those who took the trouble to respond to our electronic survey of the wider research community and stakeholders. Geoff Simm and Roger Sylvester Bradley were instrumental in marshalling the outputs of the expert discussion groups. Peter Amer (AbacusBio Ltd) provided the basic model onto which we have pasted further assumptions that underlie the livestock analysis. Steve Hoad fielded numerous questions about plant science with good grace. The views and conclusions expressed necessarily echo the views we have heard during this process. But their expression here is entirely our own.

Executive Summary

Defra is facing an expanding range of environmental challenges that may require reorientation of its policy and spending priorities. With this shift in emphasis comes a need to reconsider policy options for meeting these objectives and, by extension, the weight put on different forms of research and development (R&D) that provide a policy evidence base.

This policy shift inevitably poses questions about the government role in addressing market failure, and delineating responsibility for delivering public versus private good outcomes. This in turn leads to questions about the role and effectiveness of research, in this case in genetics, as a proximate investment option for delivering public good objectives. An important consideration is whether a genetic approach can respond to government priorities, and whether it can be judged as economically efficient.

Clarifying this question requires an exercise in research prioritisation; a process of clarifying policy objectives, specifying and prioritising options to meet these objectives, and appraising their economic viability. This study sets out the process of evaluating the role of plant and animal genetic research against new policy objectives. It then attempts to provide a forward-looking evidence-base on the economic efficiency of prospective Defra-funded R&D options in plant and animal genetics.

There are strategic reasons why government might want to maintain a presence in genetic research in plants and animals, but the value of the options afforded by a genetic research base are not easy to quantify. Most of the existing studies on economic returns to agricultural R&D are ex-post - i.e. looking backwards. While these are informative, they tend to be based on conventional private productivity (i.e. yield) returns, which are not the focus of Defra's new predominantly public goods priorities. Much less literature is focussed on genetic traits that confer public good benefits, or is of an ex-ante variety. Some studies of the potential returns to transgenic crops can provide indicative information on yield returns.

Ex-ante analysis has its own drawbacks in the extent of subjectivity attached to the methodological approach to selecting priorities, and predicting future research outcomes. It can also be further complicated by the need to place a value on the public good outputs of research, which are predominantly non-market welfare effects.

Methodological literature on the evaluation of R&D points to the dangers of cherry-picking research results to evaluate. It is also cautious about the extent to which a rate of return approach can provide sufficient policy evidence. This is because it can be artificial to assess the returns to a project in isolation from the wider research environment in which it has been developed, conducted, and ultimately used. Indeed, intended and ultimate use made of genetic research can sometimes diverge significantly. Much basic and applied science research often gives rise to wider social benefits than are not captured in an obvious observable outcome. To this extent, the linear approach adopted here probably underestimates the returns to public investment.

Despite the difficulties observed in rate of return analysis, it is important to attempt to demonstrate returns, and especially in the case of non market returns that are becoming more central to policy.

This study combined an options appraisal and an economic appraisal of candidate research to target Defra policy objectives. Rather than a detailed inventory of research options, the aim was to give an overall sense of the economic efficiency of genetic based R&D. The options appraisal considered and scored a long list of research ideas and themes that could be matched with a specific policy objective (e.g. climate change mitigation, water quality, biodiversity etc).

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The resulting shortlist was then subjected to a more detailed economic appraisal that consisted of a cost-benefit and a cost-effectiveness analysis of selected research areas. Both stages were informed by expert and stakeholder input.

The two basic questions are as follows. First, for the candidate research, and making a distinction between private and public good returns, is there evidence that benefits or public good returns to research outweigh the costs to the extent normally required for public sector spending decisions? The second question concerns the relative cost-effectiveness of the candidate research. Specifically, for the attainment of a given amount of a given objective, is a genetics approach the least cost means of delivery?

The first question is a more exacting approach to appraising public spending on research. But the indicator commonly derived (economic rate of return) by this method, does not necessarily indicate whether interventions yield near term benefits or the relative magnitude of the returns. Accordingly, the study undertakes a comparison of relative costs of different methods for achieving specified objective outcomes, with a preference for larger outcome delivery in the near term. These alternative methods could include other research avenues, or other policy levers such as market-based instruments, all of which have different costs implications.

On the cost-benefit comparison, our analysis suggests that both animal and plant genetic research targeting public good outcomes, can yield rates of return that stand comparison with other public sector investments. While the distinction between public and private returns is arguable, under reasonable assumptions, animal genetic improvement can yield rates of return ranging between 11 - 18%. Plant research may deliver rates ranging 21-61%. These rates of returns are calculated over a limited time horizon and compare to a standard Treasury discount rate of 3.5%. They compare favourably to those derived from other public (or public-private) sector investments. Both sets of figures are sensitive to assumptions about adoption rates among target groups. We have made more stringent assumptions about adoption in livestock because of the slow timeframe of accumulations of benefits.

Our findings are in line with the literature on returns to agricultural R&D, which demonstrates high returns to research focussed mainly on private yields. In contrast, most of the evidence here is based on the potential environmental public good returns, and greenhouse gas benefits in particular. We demonstrate how these returns can contribute to meeting at least two time bound policy targets related to the Kyoto Protocol and the Water Framework Directive. More generally the analysis suggests the extent to which plant and livestock genetics may contribute to an agriculture that makes a positive net contribution to the environment.

Our conclusions add to the suggestion in the aforementioned literature, that given high rates of return, agricultural research has generally seen systematic under-investment. This argument is all the more compelling given general scientific consensus on the rates of return that can be anticipated from further developments in life sciences, and the delivery of both environmental and health benefits from investment in animal and plant genetic research. Our findings on the second question suggest that genetics offers a cost-effective option to address specific policy objectives. A major advantage here is that the benefits of genetic improvement are permanent and cumulative. Current investment obviates recurrent long term spending. Even though present value of these costs of alternatives can be low, they still favour a genetic approach providing adoption assumptions hold. Moreover, it is unclear how government can bring about alternative investments without potentially costly regulation.

We note that such cost-effectiveness comparisons may be misleading because alternative interventions will typically be delivering on a basket of incommensurate benefits. Even if we restrict the analysis of effectiveness to greenhouse gas outcomes, the relative costs of implementing alternative policies (e.g. market-based instruments) are difficult to trace.

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While demonstrating the potential for direct returns to prospective Defra support, our analysis abstracts from the real adoption hurdles that exist in plant and, to a lesser extent, animal science between publicly funded innovation and private sector adoption. While these hurdles exist, part of the returns presented here will remain notional. We offer some observations on how public good objectives can be advanced through research that is targeted on emerging markets for environmental goods and services (e.g. organics, biofuel and biomass).

We note that the current challenges in affecting policy through genetics are a legacy of historical reforms in plants and animal research. Specifically, both sectors are characterised by differing forms of commercialisation and cooperation, and concerns about excludability that can limit the influence of publicly funded research. Broadly speaking, ruminant livestock genetics is relatively more cooperative, with a low likelihood of small enterprises being able to capture sufficient additional market share to justify long term private research investments. Hence public funding has a more significant influence on R&D.

Private sector stakeholders are appreciative of Defra's role and the interface it offers between public research and their needs. While not always fulfilling all needs, most acknowledged the benefits of having this interface. Defra needs to focus on barriers to research adoption, and review technology transfer models that are being advocated by themselves and other partners in research funding. Researchers have a role in considering how their research delivers on public good objectives and in providing a better evidence base for their proposals.

We suggest that the evidence presented here actually understates the likely economy wide benefits of genetic research in the UK. Some of these benefits are difficult to quantify, and relate to the option value of a science base that is capable of feeding into the private sector in a variety of indirect ways.

In summary, we are confident that future investment in plant and livestock genetic research offers an economically efficient option as part of any policy portfolio to deliver public goods requirements from agriculture.

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Contents

Executive summary

1. PURPOSE AND STRUCTURE OF THIS REPORT 8

1.1 INTRODUCTION 81.2 BACKGROUND AND RATIONALE 111.3 IDENTIFYING PUBLIC AND PRIVATE ROLES 131.4 THE RESEARCH PROCESS 141.5 WHAT IS PUBLIC VALUE? 161.6 APPRAISING RESEARCH PRIORITIES 171.7 OPTIONS APPRAISAL 18

Determining priorities 18Economic appraisal 20

1.8 MEASURING BENEFITS OR RETURNS TO RESEARCH OUTLAYS 22Returns to agricultural research and development 23Impact studies related to genetic improvement research 25

2. DETERMINING OBJECTIVES 27

2.1 INTRODUCTION 272.2 INITIAL SCREENING 282.3 MATCHING RESEARCH WITH OBJECTIVES 29

3. WEB BASED SURVEY 33

3.1 INTRODUCTION 333.2 SURVEY RESULTS 34

Summary of survey responses 34Importance of research themes 34Overall assessment of priorities 36

4. ECONOMIC ANALYSIS OF RESEARCH PRIORITIES 39

4.1 INTRODUCTION 394.2 DEFINING RELEVANT POLICY TARGETS 39

Climate change 39Water Framework Directive 40Other policy commitments 40Organic targets 41Biofuel targets 41

4.3 ECONOMIC ANALYSIS 414.4 INCREMENTAL COST-EFFECTIVENESS ANALYSIS 434.5 RATE OF RETURN ANALYSIS ON SELECTED RESEARCH PROJECTS 44

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4.6 LIVESTOCK 45Method 45Survey results on livestock genetics research projects 45Model of gene flow through the industry 46Unit economic benefits of farm productivity changes 48Unit economic benefits of greenhouse gas emissions 48Unit economic benefits of reduced disease burden 48Farm gate implications 48Animal welfare implications 49Timeframe of impact 49Calculations 49Results and Discussion 49Conclusions 52

4.7 PLANTS 52Baseline 53Costs 53Benefits 53Adoption Profile 54Deriving rates of return 55

4.8 DISCUSSION 584.9 COUNTERFACTUALS 594.10 REDUCING GREENHOUSE GAS EMISSIONS – COMPARING GENETICS TO ALTERNATIVE APPROACHES 61

Livestock emissions 61Plant emissions 63Incremental cost effectiveness analysis 65

5. LINKS BETWEEN DEFRA FUNDING AND PRIVATE SECTOR RESEARCH STRUCTURES 69

5.1 INTRODUCTION 695.2 LIVESTOCK SCIENCE 705.3 PLANT SCIENCE 715.4 GENERAL OBSERVATIONS 71

6. CONCLUSIONS 73

7. References 75

List of annexes

Annex 1. Livestock and plant breeding industry structuresAnnex 2. Main policy and appraisal statements documentsAnnex 3. Project list and scoring criteria tablesAnnex 4. Project Prioritisation ResultsAnnex 5. Example Screen Shots of the Web Based SurveyAnnex 6. Summary of responses from the web based surveysAnnex 7. External costs and benefits arising from agricultureAnnex 8. Structure of livestock genetics benefits assessment modelAnnex 9. Experts in discussion groups and respondent institutions to electronic surveyAnnex 10. People contacted for stakeholder section

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Annex 11. Original Research Specification Documents

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List of tables

Table 1.1: Previous UK Studies into Returns to Agricultural R&D 24

Table 2.1: Outline of Defra Policy Objectives 28Table 2.2: Identification of Defra Policy Goals from the Expert Groups 29Table 2.3: Animal Research Areas and Projects 33Table 2.4: Plant Research Areas and Projects 33

Table 4.1: WFD detailed timetable for implementation 41Table 4.2: Summary of mean survey results for 4 different research projects with a view to

influencing the rate and direction of genetic progress in UK livestock industries 48Table 4.3: Discounted cumulative benefits (£000’s) from a 1% increase in performance

characteristics after 10 years with 100% adoption for 5 sectors of the UK sheep and suckler beef industries using a discount rate of 3.5%. 51

Table 4.4: Present values of research costs and benefits, and net present values (£000’s) for 4 projects targeting improvements in the rate and direction of industry genetic progress in sheep and suckler beef breeding programs 52

Table 4.5: Outline of Major Parameters Adopted within the Modelling Process 54Table 4.6: Median rates of yield increase and nitrogen input reduction resulting from plants

genetics projects. 55Table 4.7: Rates of Return and price variance (50% lower prices), for Crop Projects,

percentages 57Table 4.8: Non-genetics approaches to reducing agricultural GHG emissions. 60Table 4.9: Comparison of genetics research with alternative approaches to methane

reduction in dairy cattle. 62Table 4.10: Comparison of genetics research with alternative approaches to N2O reduction in

arable farming. 64Table 4.11: Incremental cost effectiveness ratios for livestock genetics and counterfactual

approaches 67Table 4.12: Incremental cost effectiveness ratios for plant genetics and counterfactual

approaches (oilseed rape). 68Table 4.13: Incremental cost effectiveness ratios for plant genetics and counterfactual

approaches (wheat). 68

List of figures

Figure 1.1: Outline of research process. 10Figure 1.2: A Model of the Research Process with Agricultural Science 15Figure 1.3: Defining stages of research planning 19

Figure 3.1: Mean importance ratings of the research themes from the livestock survey. 36Figure 3.2: Mean importance ratings of the research themes from the plants survey. 37Figure 3.3: Assessment of research priorities for livestock 38Figure 3.4: Assessment of research priorities for plants 39

Figure 4.1: Framework for economic analysis. 43

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1. Purpose and structure of this report

1.1 Introduction

This report presents the results of Defra project ‘The rationale for Defra investment in R&D underpinning the genetic improvement of crops and animals’ (IF0101/CTX0512).

The project had two main objectives: first to help identify the elements of future research in both plant and animal genetics that most closely meet Defra’s policy objectives; second, to determine the relative efficiency of public spending on these priorities using a cost-effectiveness or a cost benefit criterion. These elements contribute part of the evidence base that could enable Defra to decide on the merits of genetics as part of its future research and policy portfolio.

More specifically the objectives were to:

Identify where, and what, research underpinning farm species genetic improvement can contribute to Defra’s objectives

Identify the most cost-effective alternative or currently implemented means to achieving the same outcomes, including other policy tools.

Conduct an incremental cost-effectiveness analysis comparing additional research to the best or currently implemented intervention for achieving specific outcomes , that will take into account the timescale for delivery and Defra time-bound targets

From the cost-benefit analysis, assess whether further investment in genetic research would be an efficient addition to existing policy instruments

Analyse the wider deliverables of Defra-funded R&D to underpin genetic improvement, and identify the implications of a significant reduction in Defra’s investment in this area.

The project used qualitative and quantitative methods to gain an understanding of both priorities, and the economic efficiency of key genetic research for enabling Defra to meet time bound policy objectives. This analysis involved input from both UK and international stakeholders. Economic efficiency is defined in terms of whether genetic investments yield a respectable economic rate of return over the lifetime of the public investment, or are demonstrably more cost-effective relative to any feasible alternative approach.

This report is organized into six sections. Section one outlines the rationale and methodological background, and considers the public role in plant and animal genetic research as a context for conducting this study. The section then briefly reviews the evidence on the returns to agricultural R&D, and genetic research in particular. With a view to undertaking a forward looking appraisal, the section then considers the methodologies available to prioritise research, and to answer the key questions posed by this research. This requires us to consider the literature covering research priority setting and the methods for subsequently appraising the identified priorities.

Section two addresses Defra policy priorities, and how these priorities can be met by genetic research. As a basis for the economic analysis, we provide a preliminary indication as to what the SAC team has identified as headline goals for publicly funded research. With the help of input from expert group workshops, we then identify the candidate research that matches the broad policy objectives.

Section three takes the identified research themes developed by the expert groups and presents them to a wider group of stakeholders. The aim of this exercise is to further our understanding of both costs and the likely public good returns to conducting genetic research.

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Some of this information can be used in an ex-ante economic appraisal. We also wished to elicit further information on research counterfactuals. In other words, the next best means of reaching the associated policy objective.

Section four develops the economic evidence base for determining the efficiency of public spending on plant and animal genetic research. First we specify the relevant policy targets. Next, our analysis considers the balance of economic costs and benefits in relation to specific research to meet these targets. The key question is whether the public good returns outweigh the projected research costs. We discuss what the counterfactual policy options might be, and offer an assessment of whether the genetic options are cost-effective relative to reasonable counterfactuals.

Section five considers the links between publicly funded research and private R&D investment.

Section six draws out general conclusions and offers caveats for decision makers.

Sections have been written to provide the fullest rationale and background to a non economist readership, which we assume to be the main audience for this report. The methodological background of section one is central to the way we have approached this problem, but may be skipped by those with some familiarity with options and investment appraisal. Figure 1.1 sets out a basic route map for the research process followed in this study.

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Figure 1.1: Outline of research process.

LIST REVIEWSTRATEGY GOALS

RESEARCH PORTFOLIO

ANIMAL EXPERT PANELHORIZON SCANNING

PLANT EXPERT PANELHORIZON SCANNING

PRIORITY RESEARCHINTERVENTIONS

SELECTIONS

PRIORITY RESEARCHINTERVENTIONS

SELECTIONS

DELPHI SURVEY OF WIDER SCIENTIFIC SAVING

DELPHI SURVEY OF WIDER SCIENTIFIC SAVING

PRIORITY RESEARCHPORTFOLIOS DETERMINED

COSTED

ECONOMIC ANALYSIS

FINAL REPORTING

STAGE 1

STAGE 2

STAGE 33

STAGE4

STAGE5

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1.2 Background and rationale

Genetic research aims to improve the performance of plants and animals by altering their genetic complement to include genes, alleles and/or traits not originally found. Improvements can be affected by a range of techniques that include selection and modification. Whereas genetic selection generally works to isolate a desired set of genes out of what's already present, genetic modification is changing or adding.

This ability to select for and modify an organism's genetic makeup has innumerable uses for researchers trying to understand the basic biology of plants and animals, including humans. Moreover the pace of innovations can potentially be much quicker. In applied plant science, the research is helping the development of agricultural crops that are better for the environment and for consumers. In applied animal studies, the work is improving the way animals are bred, leading to better health for livestock and people and pets. Genetic modification is the only existing tool available for producing certain vaccines, drugs and diagnostics.

Genetic modification can improve the precision of existing selective breeding methods. By transferring only certain genes from one plant or animal to another, researchers can in theory introduce one specific trait without also transferring dozens of unwanted traits, as often occurs in selective breeding. The important potential advantage here is that this precision may obviate many of the trade offs that have characterised selective breeding; it is now potentially possible for plants and animals to be developed for public good objectives without compromising the productivity or yield that underlies the private incentive to produce. In practice, both livestock and plant breeding are characterised by a mixture of complementary selection and modification techniques. While such precision is a long term goal, biological complexity means that unanticipated outcomes are still possible.

Nevertheless researchers claim that research and development at the genetic level offers a vast range of potentially cost-effective interventions to deal with environmental and health problems. Discoveries can greatly accelerate the accumulation of social benefits and these benefits are permanent and cumulative. In a changing global environment, it has become apparent that genetics offers potential shortcuts for dealing with a range of environmental and health public good problems. Some go as far as to suggest that life sciences offer the potential for major step changes in economic prosperity1 and human wellbeing that surpass anything delivered by physical sciences of the last fifty years. Obviously the potential can only be realised to the extent that countries have access to new innovations and trained capacity to absorb innovation. While these may be borrowed from elsewhere, there are strategic reasons why countries might want to invest in the capacity to undertake genetic research to offer options and flexibility to confront future change.

But much of this rhetoric is based on prospective breakthroughs. While the benefits may turn out to be significant, the current evidence base on this is somewhat selective and rarely carried out across a portfolio of activities. This means that R&D sponsors will, as always, inevitably back winners and losers, and all that this entails for research funding. Moreover, while cumulative, the longer development and adoption profiles for genetic R&D outputs do not always compare favourably to near term solutions. In some cases, the full benefits may not be obvious for generations, and such time frames rarely coincide with political cycles and the agendas of those who work by them.

Notwithstanding the time handicap, it is important to recall that the progress in genetics and genomics has been rapid, and, that there is every reason to expect genetics to be the

1 Lawrence Summers writing in the Financial Times “America must not surrender its lead in life sciences ” FT January 29th 2007

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underpinning of continued high rates of return to agricultural R&D. Moreover, the political impatience, or time preference, needs to be balanced against the public interest in a domestic science capacity, which draws on and contributes to a private sector apparatus that in many ways benefits from publicly funded science. There are basic irreversibilities in research structures that mean that lost capacity can take many years to replace. Both precautionary and option value arguments can be used to support existing investments in the area.

In many OECD countries the progress on biotechnology is inextricably linked to the emotive debate on transgenic organisms, public understanding and indeed participation in scientific progress. These discussions have invariably slowed the rate at which private and public research synergies might have been realised. Increasingly the debate around negative eventualities is being counter-balanced by emerging discussion of the potential gains from genetic engineering, which are highlighting the private and public good benefits of genetic interventions.

The potential returns to genetic research provide a compelling rationale for public funding of basic and applied research in the field. Defra’s historical spending is around £7 million each year and has been responsible for maintaining an important body of domestic selection material. But to date there has been little consideration of how the outputs of this spending match up to private and public good objectives. Instead, the research will typically have been justified in terms of its broad contribution to national productivity, and a considerable emphasis on yield and ex-post evidence. Much less attention is given to how government objectives may change, and to developing ex-ante evidence as to how genetics can match new priorities.

As stated in the initial invitation, “Defra has invested in genetic improvement research to enhance the sustainability of agricultural production by reducing the intensity of the use of external inputs and reducing adverse impacts on the environment whilst maintaining profitability. Defra funded R&D typically allows the private sector to draw on the expertise and resources generated in the first instance by research council basic research. R&D which can reasonably be expected to be of more immediate benefit to the industry is left to the private sector, including the levy bodies”.

The statement offers guiding objectives for this study. The broadest is sustainability, which is more specifically defined in terms of affecting resource use; minimizing input use or minimizing units of undesirable outputs (including externalities) per unit if input. This can be interpreted as both a public and private good objective. The concept of resource use facilitates the selection of specific targets for research. Secondly, the issues of profitability, and the relationship between public research and private end users. This statement effectively identifies three sets of actors implicated in delivering both private and public benefits from genetic research: publicly funded researchers, research council funding, and the private sector. It implicitly acknowledges interdependency and leakage between the public and private roles and the incremental role played by public funding to deliver increasingly public good outcomes through a structure that is essentially driven by private incentives (i.e. traditional productivity).

The challenge for this project is to determine whether the incremental spending by Defra is demonstrably offering value for money. In more technical terms, whether this spending is either cost beneficial or cost-effective. But this comes with a caveat that measuring the return to this role assumes we can fully untangle and compartmentalize the returns as accruing neatly to either public or private investment. While this may not be the case, the appraisal will be partial.

Annex 1 contains further background on the structure of both livestock and plant genetics development in the UK.

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1.3 Identifying public and private roles

There are several reasons for government involvement in different stages of the scientific research chain, which spans basic research through to development and beyond via extension. Barnes (2001) summarises arguments that have been advanced for supporting government involvement within agricultural R&D. Essentially most arguments centre on the use of public funding to correct market failures. The theory of market failure provides the basic rationale for government involvement in research. In the first instance there is a problem related to the appropriability of benefits from research. In essence, the private sector will have less incentive to invest where there are no barriers to appropriation of the outcomes of research. The 'publicness' of some research outputs makes them difficult to protect from competitors. This aspect of publicness can affect the incentive to produce many private goods that cannot be comprehensively protected from competitors. More obviously it extends to all forms of public good output that, by definition, do not have a means to exclude users and beneficiaries. Many of the desirable and undesirable outcomes of plant and animal genetic research have public good properties. For example, the development of low emission plants and animals may not currently appeal to private producers because they cannot capture a return from an output that is enjoyed by all. This output can be either a positive or a negative externality. Government would ideally like to see such spillovers generated and regulated from the private sector. But without incentives or direct regulation, there is no structure for these producers to seek a return from the many beneficiaries.

In this project, the government objective can be guided by considering the fundamental role of government vis a vis natural resource use and market failure. Left to its own devices, a market will not necessarily contribute to resource use efficiency. For example, the pursuit of animal and plant productivity in the traditional sense of yield maximization will not necessarily optimize the abatement of greenhouse gases, or the delivery of optimal animal welfare. In the absence of some form of regulation to internalize external costs, the natural resource base will be exploited in a non-sustainable way. By extension, a sector focused on market goals will under-invest in R&D that generates public goods, such as environmental or social goals. Until there is a regulatory or voluntary alternative, there continues to be a government role to address this form of market failure by promoting the types of research that address public goods and as part of a sustainable development agenda.

Addressing market failure provides the most important rationale to guide government objective setting and, by extension, the types of underlying research to help deliver these objectives. As government commits itself to more global environmental agreements 2, its regulatory role in relation to market failure becomes ever clearer and more compelling. This inevitably displaces the traditional focus on financial sustainability and productivity, which are nevertheless important objectives in their own right. But broadly this leads to an argument where the productivity and efficiency enhancing goals of genetic R&D seem mostly the domain of private investment than the concern of the public sector. Indeed, such intervention for solely productivity-improving research can in fact be tantamount to a distortion in production since resources could be skewed towards protection of inefficiencies.

2 For example, the Kyoto Protocol, the Biodiversity Convention and the EU Water Framework Directive

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1.4 The research process

Barnes (2001) applied market failure arguments to a schematic of the research process, identifying areas that provided strong justification for public intervention, compared to those with less or no justification. A model of the research process is presented in Figure 1.2.

Pure knowledge is predominantly created through basic and applied strategic R&D. Both the natural and physical sciences consist of work that is not directly related to agriculture, but could be considered as an essential background from which scientists can develop solutions to agricultural problems. This becomes more relevant when considering applied strategic work, which consists of scientific work which has an agricultural bias. Nevertheless, this work is at such a fundamental level that it could easily be applied to other areas. For instance work in the fields of cell reproduction and nutrition could be used as a basis for work in both human and livestock fields.

The real distinction emerges when considering applied specific research, which is solely directed at agricultural problems. Critically, the direction and outcome of this work is far more foreseeable in comparison to the previous types. Hence, this type of research can have general goals and can be directed towards a specific sector, e.g. crops and livestock. In addition, as its outcome can be generally determined, it can be divided into both productivity and non-productivity enhancing areas. Non-productivity research in public good areas, such as animal welfare, calls for specific studies in relation to certain categories of animal. Furthermore, from this work actual solutions can be achieved through development work. This would include the introduction or adaptation of technology or processes to achieve specific goals to improve productivity, or exploit specific diversification opportunities. An important aspect of this development process is the role of advice between the end-user and the producer of technology, which can help the adaptation of the final product towards the specific needs of an individual or group.

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Natural and Physical SciencesOther ‘Pure’ Sciences,

e.g. mathematics, physics etc.Basic

Mechanical Sciences

Chemical Sciences Biological Sciences

Social Sciences AppliedStrategic

AppliedSpecific

For example, investigations into crop disease, livestock breeding, and raw

material processing for industrial usage.

For example, investigations into biodiversity, environmental

economics, rural tourism and animal welfare.

For example, pesticide application technology, embryo transfer, robotic milking device and starch processing

mills

For example, waste management information, on-farm

diversification, modified handling crates for poultry

Development

User Groups Advice

Farmers Scientists Policy-Makers Agricultural IndustryOther Industries

Figure 1.2: A Model of the Research Process with Agricultural Science

However, a caveat to the above delineation of research is the interaction that exists between types of R&D. Also of interest to this study, the link between public inputs and private sector activity. A number of writers have emphasised that the research process is characterised by a complex series of indefinable non linear linkages (Thornley and Doyle, 1984; Rosenberg, 1990; Pavitt, 1991; Georghiou 1998; Scott et al., 2001). Consequently, it has to be emphasised that basic, applied and development work are not distinct categories of the research process, but intrinsically connected. This seems to complicate the traditional view that the public sector should be responsible for the majority of basic research, whereas more applied and development work is the concern of the commercial sector. It also means that it is more tricky to compartmentalise the public goods as emerging from one or other type of funding. Therefore, whilst knowledge is created through basic research, it can also be gained from applied and development activities, which may be of benefit to future investigations in basic fields. More critically, an emphasis on applied science will actually create more technology for the advance of agricultural policy goals. Thus, active participation by the public sector in these fields must surely be welfare increasing. This is further emphasised by Rosenberg (1990), who suggested that private firms conduct some basic research to understand new developments in science and to integrate them into their work. There can therefore be no clear division between public basic research and commercially orientated applied R&D and, to a degree, some overlap should occur.

The question of the links between public and private research is addressed in more detail in a later section

Productivity Enhancing Non-Productivity Enhancing

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1.5 What is public value?

Prior to considering the methodology of priority setting, it is helpful to clarify further the terminology in relation to public goods and market failure. This is important since the definition of market failure is not consistently used in the context of plant and animal improvement.

As suggested, government intervention is warranted where markets, in which the private sector operates, fail to deliver the correct level of outputs that are socially beneficial, e.g. clean air. These outputs are so called externalities, which can be positive and negative. Externalities have public good characteristics and economics has a relatively clear definition of these characteristics of which the most relevant is non excludability. In essence, society is diminished by the over-production of damaging externalities, and could benefit from the production of positive externalities. But market failure occurs because neither good nor bad externalities have observable market prices that can act as incentives (i.e. compensation payments for positive externalities, penalty charges for negative ones). The government role is typically to correct and regulate such failures where possible.

Government responsibility spans a range of departmental functions, delivering on a range of objectives and outcomes that are often incommensurate in value terms. The delivery of public and merit goods3 is a common theme, though the definition of publicness of outcomes is not as apparent in some departments as in say environment (Defra) and health. It is common to see public good definitions used interchangeably with less clear terms such as public value. Indeed the Defra evidence and innovation website contains the following definition4 of the latter, advanced by the Work Foundation in 2005:

“the return the public requires and expects for its taxes, created by public services. Citizens will give up resources (e.g. taxes) in return for specific services (education), desirable quality of life outcomes (increased educational attainment) and to see key public values underpinning those services (e.g. ‘universality’) upheld. Trust is a key component of Public Value, which occurs partly as a consequence of providing services, upholding values and delivering quality of life outcomes”.

This slightly more convoluted definition largely corresponds to the more conventional definition of public and merit goods. It basically boils down to the social contract between government and taxpayers for the delivery of outputs that the market typically fails to supply in any socially optimal manner.

This definition of market failure is distinct from the perception of the market failing due to imperfect competition either on the demand or supply side of production. Again economics defines a spectrum of market structures, which, for the production of different goods, ranges from pure competition to pure monopoly. These extremes of the spectrum are useful for measuring the degree of imperfection in any market, and the degree to which producers and consumers may exercise some level of control over prices and quantities of goods produced.

The markets for the production and supply of most goods and services including many plant and animal products can typically be characterized as some level of imperfect competition. While economists like to work with reference to an abstract pure competition benchmark 5, the

3 A good that is under-consumed if provided by the market mechanism because individuals typically consider how the good benefits them as individuals rather than the (largely externality) benefits that consumption generates for others in society- e.g. vaccinations or education. 4 Cited in Michael Harrison, Defra Science and Strategy Team http://www.defra.gov.uk/science/how/documents/EIS14JuneWorkshopPresentation1.pdf 5 This abstraction provides the market and exchange conditions that economists sometimes describe as first best.

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fact is that second best prevails and most markets are somewhere in between on the spectrum. For example, in the case of UK arable crops, plant breeding is characterised by the power of a limited number of companies. This essentially oligopolistic structure among the plant breeding industry can provide a barrier to the transmission of research results to the market and the end user. Market structure and power therefore mediate and affect the ability of government to intervene to supply public goods as was noted above. This may be because there is simply no incentive for oligopilistic producers to adopt research developments that compromise their profitability objectives. In this case the market structure can effectively be a barrier to an attempt to address market failure through an avenue of basic research such as genetics. The problem of market failure will persist, and government must either address it directly within less than perfect market structures (i.e. regulate) or seek alternative avenues for affecting public good supply. This is essentially the role that Defra adopts in recognizing the role of its research funding as a lever to deliver publicly funded research to the private sector.

A more concise discussion of the role of government in the supply of public good can be found in Stiglitz (1999).

1.6 Appraising research priorities

The preceding discussion leads us to draw a somewhat artificial distinction between private and public returns that might be forthcoming from public R&D spending. In reality, these returns are typically jointly produced and the joint return would be considered as an aggregate economic return on public investment abstracting from the distributional incidence of the return. However, if government seeks to target and accentuate the public return to investment then appraisal of the associated rate of return on target research is legitimate irrespective of whether private goods are an additional ancillary return or not.

Public spending on research and development can be appraised in a manner similar to other public spending decisions. Such decisions are typically subject to a process that comprises an appraisal of options, followed by an economic appraisal that reveals the favoured option. An initial options appraisal will be undertaken to identify the favoured short list of technically feasible options to address an objective or problem. Only these projects will then proceed to economic analysis. As we shall see later, it is important to understand the extent and meaning of the word economic in this context.

Assume research is to be directed at a specified policy objective. A program or a specific piece of research may be one option for meeting that objective. The supposition is that it is the most cost effective or economically efficient option for meeting the selected objective, and that the objective is implicitly met at least cost or in a manner that provides best value for money. Strictly speaking the list of options for meeting a policy objective (e.g. climate change adaptation) might include a subset of genetic options, or options from a wider agricultural portfolio of options. Alternatively, there may be a diversity of non-agricultural options that offer society a better return to their cost. For the purposes of this study, the consideration of options will initially consider the genetic portfolio. Once the favoured genetic option is identified, we will consider its economic viability. We will then compare this economic viability to any available methods form the widest counterfactual, which includes alternative policy instruments. In short, we ask whether genetic options should be favoured relative to other agricultural or non-agricultural options for addressing a specified policy objective.

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1.7 Options appraisal

To conduct any options appraisal is to assume we have both a policy objective and options for achieving it. While policy objectives may be a given6, the determination of priority options for addressing any objective is more complex. In limited circumstances a policy objective can be met by only one route, which is clearly and demonstrably cost effective by virtue of being the unique technically feasible solution. Any further options appraisal is obviated. More commonly there are alternative options requiring appraisal. This is true both within the portfolio of research possibilities and between research and other options for addressing the objective. Accordingly, we require some means to identify research options and alternative options and to determine a hierarchy of priorities based on their perceived effectiveness and technical and social viability.

In the first instance in this study we restrict the hierarchy of options to the genetic portfolio. In other words how do we prioritise among competing genetic routes to meeting objectives. Later we consider how our research priorities from this hierarchy measure up with respect to non-genetic or research options.

Determining priorities

Initial priority setting is a forward looking exercise. As the research has not yet been conducted we are looking forward to forecast returns for a research cost outlay. Priority setting has to be seen as a process. In the broader context of research planning, priority setting is but one component in a sequence of steps that finally lead to the allocation of research resources. Priority setting is based on the outcome of research evaluation where alternative research activities are analyzed in terms of their value for society. The evaluation does not yet result in clear priorities since several objectives are usually involved. Priority setting includes the determination of the relative importance of these objectives. Thus it results in a ranking of research alternatives taking into account the weights attached to the objectives. The priorities are used as aids in decision making. Consultations with stakeholders can lead to some adjustments while a consensus is being built. Scenarios using different weights for the objectives will be discussed before taking a decision. Also, new alternatives or ongoing research activities might be considered. The outcome of decision making may be a bundle of research activities to be implemented. Resource allocation still involves further considerations, such as the definition of an optimal portfolio under the given budget constraints, the possibility to fund some research on a partial basis, or the potential to tap additional financial sources. In this sense, priority setting is not identical to resource allocation.

6 A policy objective may be stated in terms of regional or national growth or employment targets. Alternatively, as in the present case, the target may be stated in terms of specific environmental and socio economic objectives

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Figure 1.3: Defining stages of research planning

Several methodological approaches can be used to guide priority setting in research and development. Good guidance can be found in Braunschweig (2000), and Bradford Mills (1998). Norton et al (1992) set out some of the challenges relevant for this project.

The existing literature predominantly employs variants of multicriteria analysis7 to choose between or rank options. Multi criteria analysis (MCA) is a method that facilitates decision making and prioritization in a range of contexts. In essence variants of MCA are commonly used both to set objectives and to rank or rate objectives in terms of a set of criteria which can themselves take on importance weights. With a given scoring format for ranking/rating objectives and for the associated criteria, experts or stakeholders can be asked to derive an aggregate set of priorities.

In terms of the relevance to the current study, the priority setting scenario can be clearly stated in terms of how Defra should allocate research funding across a range of new priority areas or objectives (see below). In the first instance, the answer to this question might be determined internally or in consultation with external stakeholders. In the latter case, the stakeholders may consider a range of policy objectives which may or may not carry equal importance weight depending on whether this is actually laid down by the funding authority. The stakeholders may, as part of the exercise, allocate importance weights to the objective. In a second stage, the objectives may be characterized with several criteria that can be used to assign further weights to that objective. For example, cost of meeting the objective, its environmental or social impacts (good or bad), public acceptability. For each objective, these criteria can then be given a score or weight. Accordingly, both stages can give rise to a set of weights that can be used to rank both can give rise to a grand or aggregate priority for the objectives.

An MCA study may then proceed to consider the range of actual research options that can be mobilized to address the hierarchy of policy objectives. Supposing now an expert group or wider constituency of researchers is engaged to suggest researchable themes underneath each objective. The chances are that there number of ideas will exceed available funding, and that some means must be found to shortlist the highest priority research to meet the priority objectives, which have already been scored. In a third stage then, the individual research ideas can be described by importance criteria that can also be scored. Such research specific criteria might be the cost of conducting the research, the likelihood of success or adoption, and the availability of indigenous research capacity. By ranking or scoring the research theme criteria, it should then be possible to derive a priority list of research themes underneath the objectives. These should then constitute the candidate projects that can be chosen, subject to their meeting a final economic hurdle. In some cases, an MCA can be an endpoint to the process.

7 Multi-criteria analysis manual,http://www.communities.gov.uk/pub/252/MulticriteriaanalysismanualPDF1380Kb_id1142252.pdf

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There is an element of arbitrariness in developing a modified form of MCA to accommodate competing demands and questions. The first is the determination of who gets to set the objectives and their descriptive criteria. Further, who gets to do the scoring at both stages of the research? Also, is this the end of the process? If economic viability is a basic criterion for inclusion of a project in the initial portfolio, then this would be the end of the process. However, the whole purpose of this exercise may be to conduct a MCA of projects that then proceed onto economic appraisal. This is the objective of the current study. The important element is that the preferred MCA approach is transparent and explicit (Kelley et al 1995). There is also considerable merit in the process being participatory in the sense of allowing the effected stakeholders to contribute to various stages.

In this study we determine objectives with reference to existing policy documentation and statements. In setting both this framework, criteria and scoring, there is an element of subjectivity which we will return to later in the report.

Economic appraisal

The outcome of any options appraisal can be an end in itself, but is more normally the starting point for an economic appraisal, which must use a consistent framework for comparing the efficiency or value for money of options. Here, economic refers to a social perspective rather than that taken by a private enterprise, which would be based on limited financial returns to investment outlays. From the social perspective, there may be other costs and benefits in addition to financial net benefits that need to be taken into consideration. These may include externalities that will not be accounted for in private decision-making. The social perspective is that taken by government departments acting to further social welfare. Decisions taken on the basis of social costs and benefits can sometime contradict decisions that look optimal from a private perspective, and vice versa..

Two forms of appraisal are commonly used in both private (financial) and public (economic) spending decisions: cost effectiveness and cost-benefit analysis. Cost effectiveness analysis considers the relative costs of meeting a particular objective by alternative means. As an example, the attainment of a certain level of water quality on a particular stretch of river may be achieved by a range of technical interventions that might include agricultural or non agricultural interventions. The use of genetics as an agricultural intervention may or may not achieve the quality objective at a lower cost than another intervention, especially if other interventions can be delivered in the near term rather than over decades. The important point about cost effectiveness is that it only makes sense to compare interventions that target identical objectives - i.e. in the latter example a specific water quality target. Clearly if there is only one technical option then it is not be possible to consider relative cost effectiveness.

It is important not to ignore time dimension in the consideration of cost effectiveness. Where cost outlays and benefits accrue over a time horizon it is technically correct to reflect the fact that society will prefer benefits sooner than later. Accordingly it is appropriate to convert both to their present value equivalents via the process of discounting. However, because CE normally compares a qualitative numerator with a quantitative (monetary) denominator, this stage is often overlooked. This can lead to erroneous conclusions, and in fact this project invitation is clear on the need to compare near and longer term returns.

Cost effectiveness is normally appropriate to measure value for money in cases where the objective cannot easily be converted into a monetary value. Were this possible in the case of water quality, then a monetary comparison of costs with monetary benefits would be more transparent.

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Cost benefit analysis (CBA) provides a framework to formalize the cost and benefit comparison. CBA helps identify projects or policy reforms that improve social welfare. This is a common objective of government. Descriptions of the theory and practice of CBA can be found in Dreze and Stern (1987) and Little and Mirrlees (1974). The recommended approach to public sector CBA is set out in the Treasury Green Book8. In theory the approach is adequately flexible to reconcile many aspects of social welfare: economic efficiency (the comparison of costs and benefits), economic risk, income distribution, subsistence consumption constraints, political aspirations and even aspects of moral and immoral actions. In practice however, it is usually restricted to applications that address economic efficiency and risk (measurable monetary costs and benefits and estimable probabilities). It is often difficult to factor the other elements into the monetary cost-benefit comparison and they are therefore expected to enter the decision process through different channels. To the extent that these other elements are outside the efficiency measure, CBA thus provides only one of many potentially important inputs into the decision process. Good CBA exercises therefore try to capture as many relevant impacts as possible into an efficiency measure such as a benefit-cost ratio.

Like CE, CBA can be both ex-post and ex-ante. Ex-post studies tend to be more robust in the sense that both cost and benefit information are historically observable and therefore relatively objective. There may be problems with converting these impacts into monetary equivalents, but the fact that they have happened leaves less room for speculation. In contrast, ex-ante CBA can be more complex. The valuation problem remains, but equally complex and contentious is to specify future costs and benefits with any degree of exactitude. The problem of subjectivity in this process has been noted by Shumway (1981), and presents a challenge to scientists to specify accurately what they think their research endpoint will be. Given the fundamentally subjective nature of ex-ante research evaluation, careful selection of stakeholders – i.e. bringing in different perspectives and tapping the best available expertise – and transparent definition of roles and responsibilities will help to reduce subjectivity.

In the context of research prioritization and funding, CBA helps decision-makers allocate scarce resources by determining whether a research priority, that may have been identified by an options appraisal and a prioritization process, is actually likely to improve net social welfare. Alternatively it can help to determine which research among a competing set of viable options should be selected in order to maximize social welfare.

The basic idea behind CBA in this context, is that a project or policy is defined and (typically) an ex-ante estimate is made of social welfare (i.e. net benefits arising) with and without that project. But a project is typically compared with a counterfactual (other projects), including doing nothing. Multiple mutually exclusive projects can be specified with a view to selecting the best measures. In the current context we could be considering alternative genetic routes to mitigate methane emissions, or comparing a genetic and a non genetic approach. Each alternative research project would be characterised by a profile of research costs and benefits or returns through time. If the present value benefits of going ahead with a research area (a project) outweigh the present value costs, then the project is socially desirable from an economic efficiency perspective. The typical indicators for judging efficiency are the Net present value (NPV) and or the Internal rate of return (IRR). Both indicators are derived after converting future benefit and cost streams to their present value equivalents; a process termed discounting.

The decision criterion is that projects with positive net present values are accepted, with higher NPV projects being favoured. Alternatively, the internal rate of return9 presents the 8 http://www.hm-treasury.gov.uk/economic_data_and_tools/greenbook/data_greenbook_index.cfm9 The IRR is sometimes termed an economic rate of return (ERR) in circumstances where the resource flow values (costs and benefits) are in economic rather than financial values – see glossary for this distinction.

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same information as percentage returns to outlay (allowing for time preference). Here the decision rule is to consider favourably any project returning ERR in excess of a test discount or hurdle rate of return. For current purposes this should be the Treasury’s test discount rate for public sector investments, though there are reasons why returns to genetic investments might be benchmarked against higher or lower rates of return. Higher rates might seem appropriate if research returns were being benchmarked against private sector expectations. The case for lower rates of return can be made with reference to the fact that some genetic investments are delivering public good benefits over long time periods. These arguments have recently been set out in the Stern Review on the economics of climate change10.

Arguably the most critical step in the CBA process is not so much estimating the benefits and costs, but in properly defining the “project”. A proper definition of a project involves estimating all of the changes in production that occur as a result of a given activity. In the case of R&D, these benefits can be complex and far reaching. In the light of our earlier discussion on research processes, it is arguably the case that some of the returns to much basic and strategic research are improperly captured in observable endpoints. For now however, we assume that it is possible to make a reasonable estimation of how research outcomes approximate policy objectives.

The next section considers the range of benefits and their traditional measurement. While our focus here is on ex-ante analysis investment decisions are often informed by ex-post CBA evidence. If a process has historically been efficient and will continue with no change and then all things being equal, funding is justified. But in the context of rapid scientific advance, we are more likely to be considering novel techniques and innovations that can potentially have a significant affect on future cost and benefit streams.

1.8 Measuring benefits or returns to research outlays

The benefit and cost streams to derive the NPV or ERR need to be populated with robust data. Cost data tend to be more objectively verifiable, although apportionment of costs between discrete pieces of research (as opposed to whole programmes) can sometimes be complex. On the benefits side, assessing the yield effect of genetic research is conventionally straightforward because yield tends to have a market value that can be observed. Provided these market prices are undistorted11, research that gives rise to yield increases can simply be valued by multiplying the yield increment by its domestic or international market price. The yield increment in question will typically occur across some assumed adoption profile. This calculation provides a crude first order estimate of the returns to yield improving research.

In actual fact the analysis can be more complicated. Market prices are in theory determined by the interaction of both supply and demand curves. Significant yield improvements actually mean that the shape and location of these curves can be altered, thereby influencing price. The extent to which price will fall through a supply shift, is determined by the shape of the demand curve for the particular product, or more technically, its slope or elasticity. Surplus analysis12 is normally required to determine the ultimate welfare effect of a price fall resulting from a yield improvement (see Alston et al 1995). The method shows how the effect is determined by the assumptions for both the supply shift and the demand elasticity. Assumptions also have to be made as to whether the economy is closed or open to trade. This form of surplus analysis is common approximation to welfare improvement in agricultural research and development (see Alston et al 1995).

10 http://www.hm-treasury.gov.uk/media/C06/00/Paper_B.pdf11 In reality market prices are nearly always distorted by taxes, subsidies and trade barriers. In this case, the simple route provides misleading signals about the value of an increment of output to society and truer 'shadow' prices need to be derived (see Gittinger 1982). 12 http://www.isnar.cgiar.org/fora/priority/MeSurplus.htm

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The economic surplus approach is highly dependent on these alternative assumptions (Linder and Jarret 1978; Wise 1984). Much of the exiting R&D returns literature includes some form of surplus analysis. We do not attempt such analysis in this study because yields are not the focus of Defra’s interest.

In contrast to conventional productivity, the production of public goods through genetic research presents a problem in terms of valuation. This is basically because many public goods cannot be directly valued using market prices. Correspondingly, there are no market demand and supply curves from which to infer changes in prices and quantities of these outputs. Again market failure can be evoked to explain why, historically, many external environmental costs and benefits have been ignored in project appraisal, thereby, in the case of negative externalities, many projects appear better than they are. Conversely, the omission of non-market benefit works against the accurate appraisal of research and development generating positive external public goods.

If public good externalities are the target of research and development priorities, it obviously becomes important that they are quantified. As it happens, recent advances in environmental economics have provided a good basis for valuing several of the externalities that are relevant to plant and animal genetic developments. These methods can either use market prices, revealed or stated preference to uncover values for environmental change (Hanley and Spash 1993). The precise details of these methods need not detain us here. Suffice to say that the existing body of applications of these has generated a database of values that can be transferred for use in the current analysis. The process of so-called benefits transfer is itself an evolving art form13. But some externality data are considered more robust than others. For example, emissions of carbon dioxide can be valued using the so-called shadow price of carbon, which is a calculation based on the value of damage avoided by the abatement of one extra tonne of carbon into the atmosphere. Relative to this value, the value of other greenhouse gases such as nitrous oxide and methane can be inferred from their equivalent global warming potentials relative to carbon. Such data were central to the calculations of abatement options set out in the recent Stern Review on the economics of climate change and Defra provides basic estimates to use in policy appraisal.14.

The valuation of other outputs or impacts such as water quality and biodiversity may be more complicated, although there is a body of stated preference studies addressing both areas. In other cases indirect measures such as saved animal health expenditures can be used to approximate health and welfare benefits. While these measures are approximations, they cover some of the main categories of benefit that can be expected to be delivered from genetic research.

Returns to agricultural research and development

As previously mentioned, research prioritisation can partly be informed by a backward look at the performance of past spending. In this regard the Defra portfolio shows some success in delivering genetic outcomes with public good dimensions15, though there has been few if any ex-post economic evaluations of genetic research.

Ex-post research appraisal from elsewhere dominates the existing literature on agricultural R&D. With a few exceptions most of these studies are focused on measuring changes in

13 A state of the art review of benefits transfer can be found in the journal Ecological Economics vol. 60: 2 (2006) 14 see also Greenhouse Gas Policy Evaluation and Appraisal in Government Departments April 2006 Department for Environment, Food and Rural Affairshttp://www.defra.gov.uk/environment/climatechange/uk/ukccp/pdf/greengas-policyevaluation.pdf 15 research on the wheat blossom midge is often cited as a good example; http://www2.defra.gov.uk/research/Project_Data/More.asp?I=LK0969&M=KWS&V=midge&SCOPE=0

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productivity or output as a direct benefit of agricultural research expenditures. Some studies do consider wider environmental benefits; e.g. Nagy (2003) considers yield and disease resistance returns to field crop R&D in Alberta.

The main message from the ex-post literature is that most forms of R&D in the sector deliver high rates of return relative to other forms of public spending on research. Accordingly a case is made that agriculture has suffered from systematic under-investment.

Typical of these is Ruttan (1982), highlighting investments in agricultural R&D that are between three and five times higher than those on most alternative investments (i.e. returns on industrial capital). The most recent summary of these studies by Alston et al. (2000) employed a ‘meta-analysis’ of rates of return to agricultural research and extension based on 289 separate studies globally, providing over one thousand rate of return observations. The study found a median return of 58% for extension and a median of 36% for R&D and extension combined.

However, these rates of return are highly variable between regions and programmes. They also do not allow us to identify any meaningful information about the subset of genetic R&D.

These authors do make some interesting observations that are relevant to bear in mind here. The first is that the existing evidence suffers from bias where individual research evaluations have cherry-picked the best studies to consider. For this reason one has to be prudent when using this evidence. The second is that, quality notwithstanding, the current evidence does not suggest that returns to agricultural R&D are on any sort of downward trend. This is an important consideration given the view held by some that the shift in biotechnological productivity may be yet to happen.

No such meta-analysis has been undertaken of UK studies, although Scott et al (2001) show similarly impressive rates in a recent evidence update on the returns to basic publicly funded science research for the Office of Science and Technology.

The existing summaries of agricultural research returns tend to show a narrow focus on examining either yield or productivity changes from UK research at an aggregate level and imputing an internal rate of return for the costs incurred. Table 1.1 outlines most of the studies that have been conducted within the UK and their estimates of the rates of return on agricultural R&D.

Table 1.1: Previous UK Studies into Returns to Agricultural R&D

Author Period Rate of ReturnDoyle and Ridout (1985) 1966-1980 10-30%Wise (1986) 1986 8-15%Thirtle and Bottomley (1988) 1950-1981 70%Khatri and Thirtle (1996) 1953-1990 18%Thirtle and Townsend (1997) 1973-1989 44%Barnes (2002) 1948-1995 22%Thirtle (2004) 1953-2000 32%

Generally these studies show a great deal of variance in rates of return, which reflects both the length of time period studied and improvements in the estimation method employed. Importantly, as emphasised before, these are market orientated and ex-post assessments and are of passing interest to this study which is ex-ante and seeks to examine wider non-market benefits from a specific strand of research funding. UK studies have tended to avoid this area,

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only Barnes (2002) attempted to expand a productivity index to include non-market benefits and then measure the return to research and development. However, due to the nature of data collection, this had to be at the aggregate level and was ex-post. Outside of the UK only a small number of studies have attempted to measure these non-market benefits by applying a variety of techniques. The most detailed seems to be by Wood and Pardey (2000) who examined a set of agro-ecological zones within Australian agriculture. They defined these zones as a geographical area with biophysical variables that can be used to evaluate research with specific objectives. From this, research impacts could be monitored to assess changes in these zones.

Of the ex-ante studies, most have not been conducted at the aggregate level, and are more focused on specific branches of research or at the project level. Araji, Sim, and Gardner, (1978) employed an ex-ante approach by focusing on specific commodities and interviewing researchers and extension specialists concerning the probabilities of adoption and success of the projects. Overall they found internal rates of return from between 33% to 104% but emphasised that these levels would only be achieved with the involvement of advisory services. In other words they lay heavy emphasis on the speculative nature of uptake. Similarly, Alston et al.(2002) studied rootworm resistance in transgenic corn and found large economic benefits along the supply chain for adopters. In addition, non-pecuniary benefits such as increased health and environmental safety were estimated using a telephone survey of adopters. The authors found high levels of benefit from their adoption.

Accordingly, most research employing an ex-ante approach have interviewed either researchers, extension advisors and, in some cases, possible adopters to gain estimates of probabilities of success. These results are then mapped to the population of focus to construct an internal rate of return.

Impact studies related to genetic improvement research

As part of the evidence base about the potential benefits of transgenic crop adoption, some ex-ante studies have considered the likely yield and productivity gains. Some of these studies attribute environmental benefits alongside these gains, but have failed to quantify them fully in terms of a standard rate of return.

Kalter and Tauer (1987) estimated a number of possible improved impacts of soya bean biotechnology on improving productivity within US agriculture. Phillips et al. (2006) examined transgenic enhancement of nutrient cycling for global pork production considering PSP/APPA salivary phytase transgene (EnvirPig) and found P digestion of around double that of non-transgenic pigs, leading to a large reduction in residual P for the environment. Falconi et al. (2001) examined disease resistance in livestock through the use of biotechnological research, finding that benefits ranged from 2 to 9 times their cost. In a study on Bst, Fetrow (1994) states that ‘under typical conditions, the use of Posilac in adequately managed dairy herds returns well over 50% profit over the expenses at typical prices for milk and feed the use of rBST has a positive environmental impact by reducing the amount of manure produced per gallon of milk.’ A paper by Gray and Malla (2001) is interesting in the current context. These papers consider health externalities from crop choices and show that the health care externality effects grossly swamp the conventional measures of research benefits or policy distortions. This factor leads us to speculate that research that deliberately engineers plants and animal for external benefits may actually yield higher rates of return than considering conventional yield returns alone. We return to this in section four.

Overall, evidence from the literature suggests historically high returns to investment in agricultural R&D. Only a limited body of ex-ante studies consider genetic research and

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development, and this is generally focussed on crop yield. We suspect that returns to genetic research in non-market goods may be comparable if considering health effects, but the literature does not allow us to conclude on the returns to research delivering other public goods.

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2. Determining objectives

2.1 Introduction

This section draws on the preceding methodological sections to define the initial stages of the process for conducting an ex-ante appraisal of Defra's plant and animal genetic research priorities. In this section we set out the process of identifying current government objectives, and the initial focus group work that considers how genetic research might match these objectives.

As noted in the previous section, Defra funded research traditionally targets several objectives simultaneously. Sustainability and sector profitability are explicitly mentioned, but changing policy priorities suggests that public good outcomes are now the principal focus of public R&D. Sustainability is summarized in terms of affecting resources use (inputs) and external outputs (good and bad). This broad objective is consistent with other objectives that are discernible in recent Defra statements on underlying resource use efficiency, e.g. sustainable consumption and the polluter pays principle, and more latterly, "one planet living" 16, which appears to be inspired by the concept of ecological foot-printing. The private sector profitability objective is more contested, but it is an acknowledgement of a dependence between public and private roles, and the need for many public goods to be mediated through a productive and therefore financially viable sector.

Government objectives are determined partly by a range of national and international commitments17 and a range of domestically determined aspirations set out in official documentation. These change rapidly, with recent statements even pre empting this evidence base18.

Several policy documents have been identified as guiding current Defra policy. These documents are listed in Annex 2. The government headline strategy for Sustainable development 19 apparently shows 68 policy indicators (chapter 7 – “Ensuring it happens”) spread in terms of responsibility across all government ministries and agencies. Of these, more than 20 could be attributable directly or indirectly to the Defra family and could conceivably be addressed through animal or plant genetic pathways.

More proximate objectives are set out in Defra’s Sustainable Farming and Food Strategy for (SFFS), which is accompanied by a monitoring and evaluation document20 that includes nine strategy objectives set out in Figure 2 (from page 121 of the named document, reproduced in Annex 2 of this report). These objectives are broadly grouped under the headings of economic, environmental and social, which mirror those of the government-wide document. These three categories in turn include three sub-objectives that still remain somewhat broad in terms of their direct links to genetic research. For example, the three economic sub-objectives suggest efficient production at farm/production level, more efficient food chains, reducing the burden on taxpayers. All of these objectives could be serviced by genetics, but there is equally a universe of alternative means for meeting these objectives. This means that methodologically, any cost-effectiveness or cost benefit comparison is essentially intractable. The nine sub-objectives therefore need to be more focused in terms of specific environmental, social and economic endpoints.

16 David Miliband's letter to the Prime Minister - "My priorities for Defra", 11 July 2006 http://www.defra.gov.uk/corporate/ministers/pdf/milibandtopm-letter060711.pdf17 E.g. the Kyoto Protocol and the E.U. Water Framework Directive18 http://www.defra.gov.uk/corporate/ministers/speeches/david-miliband/dm070103.htm19 SDU (2005) Securing the Future - UK Government sustainable development strategy, available at http://www.sustainable-development.gov.uk/publications/uk-strategy/index.htm 20 http://www.defra.gov.uk/farm/sustain/newstrategy/econ/section3.pdf

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Our selection of objectives can be guided by considering the market failure rationale for public intervention in genetic R&D. Left to its own devices, a market will allocate resources to some externalities at a level below the socially optimal desirable level. In consequence, the private sector with its emphasis on market goals will not invest in R&D that supplies public goods, such as environmental or social goals.

Broadly, this leads to an argument where the productivity and efficiency enhancing goals of genetic R&D seem mostly the domain of private investment than the concern of the public sector. Accordingly, there is some justification in dispensing with the economic categories and focusing on the environmental and social categories where market failure is likely to be more prevalent. Environmental outcomes overlap with the stated aims on resource use efficiency, including minimizing inputs and damaging externalities. Significant social impacts range from improvements in human health, to improvement in social welfare within farming communities.

2.1 Initial screening

An initial screening of Defra statements suggested several obvious policy objectives, which were common to both plant and animal genetic research. These are outlined in Table 2.2 below.

Table 2.2: Outline of Defra Policy Objectives

Animals Plants

Climate change: Mitigation and AdaptationLandscape and biodiversity

Efficient Food chainWater quality

Animal health and welfare

Climate change: Mitigation and AdaptationBiodiversity

Resource use (inputs)Plant health

These objectives were subsequently clarified and modified with the input of an initial meeting of a panel of experts with both animal plant genetic expertise. These groups will be described in section 2.3. The main modifications were to distinguish between climate change mitigation and adaptation. Some of the objectives were questionable in terms of their private or public good credentials. For example, it was unclear whether the concept of ‘an efficient food chain’ was something that should be a target of government research; production inefficiency being generally punished by market forces. A counterpoint to this is that efficiency could refer to resource use. Animal scientists suggested that many resource use efficiencies have their root cause in informational failures. This provides a legitimate reason for enabling government intervention to overcome barriers to public good supply through research.

The discussions iterated to the following list, which removed resource use from the plant section, and replaced it with food safety and efficiency. In addition, the plant expert group inserted the category of “rural livelihoods”, as an objective which could be identifiably related to plant genetic developments in biofuels. These are outlined inTable 2.3 below.

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Table 2.3: Identification of Defra Policy Goals from the Expert Groups

Animals Plants

Climate change mitigationClimate Change adaptation Landscape and biodiversity

Efficient Food chain Water quality

Animal health and welfare

Climate change mitigationClimate Change adaptation

Environment and Landscape, incl. biodiversity

Food safety and efficiencyRural Livelihoods

Plant health

These categories stand comparison with research priority lists developed by the stakeholder process conducted by the Defra Research Priorities Group (RPG) which targeted the SFFS.

The RPG report, was published in March 2005,21 and recommends seven priority research themes: Food safety and dietary information Quality and composition of food Disruption of the food supply and understanding sustainability Energy, water and waste Climate change Environmental and landscape research Socio-economics and policy analysis

Accordingly, whilst not identical, some similarity exists between the areas identified by the plant and animal group and the findings of the RPG.

2.2 Matching research with objectives

Having identified relevant objectives, the next stage of the process was to identify plant and animal genetic research capable of addressing the objectives. As a second stage, during June and July 2006, expert panels (approximately 5 scientists for both plant and animal genetics - see Annex 9) met in Edinburgh to consider these policy priorities. The ultimate aim of the following stages was to derive a short list of research themes on which to build an illustrative economic appraisal of the potential of genetics research.

We used this expert "Delphi" format as a means to elicit expert opinion. The process added a participatory element to this review exercise. The participating experts were invited based on their subject area, general knowledge of the field, including Defra experience, and links to private sector research. While the lead scientists moderated the discussions, two economists were on hand to maintain the economic focus of discussions.

The group discussion was focussed on a range of key tasks that were facilitated using scoring matrices for the objectives and for scoring the research priorities that fit underneath each policy objective. This discussion topic guide format required the following information from the experts:

21 http://www.defra.gov.uk/science/documents/RPG/Papers/FinalRPGreport.pdf

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a) Do you agree with the delineation of policy objectives?

As previously noted, the panels were asked whether the policy objectives, delineated above, made sense to them as research targets. Some clarification was necessary. For example, climate change was split into adaptation and mitigation. Other policy statements were deemed unclear or redundant, e.g. there was considerable debate about the differing perspectives on the meanings of ‘efficient food chains’ and ‘sustainable farming systems’. In the latter case, the inclusion of objectives on climate and water quality essentially rendered sustainability (as a separate objective) redundant in environmental tersm.

b) What are the research priorities under each objective?

Here scientists were asked to discuss and list research under each priority. This exercise was done partly during the meetings and partly in their own time. This part of the exercise proved to be the most difficult for the participants, with many anchoring on current research themes. The nature of the discussions also highlighted some significant differences between animal and plant research chains and relationships with the private sector.

Several important issues were clarified: The issue here became one of stressing the need for forward-looking ideas rather than

a focusing on-going research. Emphasis was placed on the need for forward looking research areas that addressed

public good objectives. It was also important to make sure that ideas were formulated as discrete projects

rather than thematic areas, since it was stressed that general thematic areas would not lend themselves to straightforward economic analysis of returns.

Scientists frequently did not see clear counterfactuals to their research approach.

In return, the scientists echoed some of the reservations about what can be missed by the reductionist approach to conventional rate of return appraisal. The most significant discussions involved the difficulty of separating conventional production from more nebulous public good objectives. Most of the scientists held the reasonable view that nothing could be delivered without a financially viable underpinning, and that unless the counterfactual was depicted as the UK without agriculture, and in the absence of compensation schemes or markets for public goods, then profitability of remaining producers had to be safeguarded. Several observed that similar discussions would be unlikely in other countries (e.g. France and U.S.), where productive agriculture is regarded as an engine of rural policy. This discussion in turn, linked to the motives of private breeders and suppliers, and market structure. The differing nature of commercialisation channels between plants and animals also proved important. On the whole, plant scientists were much more sceptical about the channels through which public good outcomes could be realised. This is mainly due to the concentrated nature of the breeding industry. Participants in the plant exercise expressed frustration at the switch in Defra’s focus implied by the exercise. Several pointed to the inherent contradictions in funding streams, which on the one hand encouraged collaboration with private objectives (i.e. LINK), while also asking researchers to focus attention on public good objectives.

Ultimately this was a challenging exercise for scientists involved. Some scientists were clearly uneasy about discussing research ideas and the issue of intellectual property was also raised. Most of the plant scientists agreed that the process would benefit from wider participation and input from others. While not wishing to pre-empt subsequent stages of our research plan22 the organisers were happy for the exercise to be extended to a limited group of 22 A third stage of the research involved a wider survey of stakeholders, preferably those who had not already been party to the outcome of the expert process.

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informants identified by the organiser of the plants panel. Both groups ultimately generated a research list that best exemplify the criteria for meeting the objectives. Inevitably, it has to be accepted that the final list is not ideal, but a reflection of particular perspectives of the group. (The full list of projects are outlined in Annex 3)

c) How would you rate/score both policy objectives and corresponding research priorities?

The research projects and areas initially identified constituted a long list of potential means of addressing policy objectives. This list was then re-circulated to the expert panellists to undertake a structured scoring exercise. This helps us determine a research short list. We used a simplified form of MCA (see section 1.7), which provides a hierarchical weighting system to generate a grand ranking of projects under themes.

Basically, each member was asked to:i. Rank the policy objectives in terms of their importance for research funding

ii. Against each policy objective, to rank a range of criteria that describe the characteristics of that objective and the characteristics of research for its delivery.

iii. For each research project theme organised under each objective, the respondent was asked to rank the criteria that characterised the research appropriateness

These stages are summarised in the tables in Annex 4.

To recap, the scientists ranked the policy themes in terms of importance. They then score the research ideas under each theme. The rankings of each individual against the criteria outlined in Stage (ii) were used to weight the scores given to each project. This allowed a ‘score board’ of projects to be identified against the strategy themes identified in Stage (i). These projects are outlined in Table 2.4 and Table 2.5 below. The final data would be amenable to a grand ranking or a within objective priority ranking. This exercise chose the latter as it offered a representative spread across policy objectives. The exercise was conducted by 8 plant and 8 animal scientists, with the results analysed by the project team.

Evidently, the ranks are partly an artefact of the weighting system used to rank both the policy objective and the project specific criteria. We have tried to obviate any strategic biases, but noted in section 1.7, multi criteria methods23 there is an element of arbitrariness in decisions about who gets to rank the policy themes, and which initial criteria are used to score the projects. With the scoring information, the projects can be sorted into priorities that can then be taken forward to a wider stakeholder exercise.

23 Note that multi criteria methods have been used before in setting national research priorities. While the literature is limited, another example can be found at: ftp://ftp.cgiar.org/isnar/Publicat/PDF/rr-16.pdf

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Table 2.4: Projects Prioritised by the Expert Group (Livestock)Climate ChangeGenetics research to identify new molecules to develop vaccines against new diseases.Modify (farming systems) /livestock breed use / breeding goals to reduce lifecycle emissions

BiodiversityDevelopment of aquaculture as a means to reduce the direct and indirect risk to wild fish stocks e.g. cod – direct; trawling to produce feed for farmed fish.Genetic options to make farmed salmon distinguishable or unfit in the wild, to reduce risk of reducing wild biodiversity

Water QualityNutrient Capture traits in farmed fishModify (farming systems) /livestock breed use / breeding goals to reduce water pollutant emissions

Efficient Food ChainPoultry avian flu./Dairy/Beef cows para TB/TBDevelopment of breeding goals & tools that meet both industry commercial priorities and ‘public good’ e.g. addressing non market value of improving animal welfare/environmental impact.

Animal Health and WelfareReducing zoonotic disease threat via selective breeding (e.g. campylobacter, E. Coli 0157)Breeding for host resistance to disease / improved welfare-related characteristics

Table 2.5: Projects Prioritised by the Expert Group (Crops and Plants)

Multi-purpose genetic researchNovel resources for oilseed rape breedingThe Defra Oilseed Rape Genetic Improvement Centre

Climate Change: MitigationBreeding oilseed rape with a low requirement for nitrogen fertiliser

Climate Change: AdaptationGenetic approaches to maintaining wheat yields in a changing environment.Exploiting association genetics - a case study on drought tolerance and water use efficiency in wheat

Environment and Landscape, incl. BiodiversityGenetic Reduction of Energy use and Emissions of Nitrogen through cereal production: G R E E N grainBreeding oilseed rape with a low requirement for nitrogen fertiliser

Food Safety and Efficiency Identification of genetic markers for lodging resistance in wheat Towards a sustainable whole-farm approach to the control of ergot

Rural LivelihoodsInvestigating Wheat Functionality Through Breeding and End UseMolecular and biochemical characters of post harvest quality in brassicas

Plant HealthControlling soil-borne wheat mosaic virus in the UK by developing resistant wheat cultivars

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3. Web based survey

3.1 Introduction

The expert group exercise derived a set of research priorities to take forward for further validation and analysis. An electronic survey was designed to circulate these priorities to a wider constituency of expertise. This format allowed us to elicit the opinions of other stakeholders who have not been part of the respective panels. Separate plant and animal surveys were designed with input from both the experts and other stakeholders. The questionnaire had three purposes. First, to corroborate the expert views regarding research priorities. Second, to help collect relevant economic data to undertake the illustrative ex ante cost-effectiveness or cost-benefit analysis of research. Third, to gather opinions on the relevant counterfactuals to the specified research for meeting the same objective.

In terms of corroboration, each of the projects short-listed in the previous exercise was presented to respondents to consider. Recall that for an ex ante appraisal, we require best estimates of forecast costs and benefits. Accordingly, a series of questions was designed to approximate these. In terms of both cost-effectiveness and cost-benefit analysis, an obvious data requirement is related to the costs of actually conducting research previously identified. There are two alternative ways to derive costings, either from the awarding bodies or from institute research officers responsible for preparing project budgets. These data are evidently less subjective then asking for estimates from scientists themselves. However, the survey collection would later enable sensitivity analysis of cost estimates. We specify staff costs and ongoing research costs, meaning capital and equipment

Research benefits are more complex. Recall that projects would fall under one of several policy objectives. Accordingly, each project was characterised in terms of the specified benefits that were claimed as key objectives (e.g. climate change, animal health and welfare, plant health etc) by the expert groups. Relevant outputs for these policy areas were then defined; e.g. in the case of plants, in terms of potential reductions in use of fertilisers, which are greenhouse gas pre cursors. In the case of livestock, emissions of methane. Some policy areas were more complex to define in terms of their outputs, but in general most of the short listed projects could be characterised by one or more specific public good outcomes. The respondents to the survey were simply asked a percentage change in the relevant output that could then be qualified with further information on the likelihood of success of the research, the times to realisation, and the adoption rate amongst target users. The latter could simply be derived from farm census data on the UK livestock and arable production.

After considering the costs and benefits of each project, respondents were asked to consider other variables, including the timescale of the research, time to adoption, and their best estimate of the percentage of adopters. Importantly they were asked to state whether they thought there was any feasible counterfactual to a genetic approach to meeting the objectives.

Final survey questions asked for an overall ranking of the various research projects (within a specified budget), and about the background of the respondent. Separate plant and animal surveys were developed and accessed by a web URL address. An abridged version of the survey is included as Annex 524.

Respondent populations were identified for both scientific communities. The animal genetics survey was distributed via the mailing list of the British Society for Animal Science. The plant survey was distributed to a number of key informants for further circulation. These included representatives from the plant improvement networks sponsored by Defra. Both

24 The full plant survey example may be viewed at: http://www.sac.ac.uk/plantgenetics

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groups reached both private and public sector researchers and stakeholders. A total of 55 responses to the plant survey and 59 to the animal survey. Annex 9 lists the institutions that identified themselves among the respondents to the respective surveys.

3.1 Survey results

Summary of survey responses

In this section we present a selective summary of survey responses to questions relating to the importance of Defra’s research priorities, and the overall assessment of the priority of the projects considered in the survey within the Defra genetics research programme.

Further data analysis in relation to specific projects considered in the survey is presented as part of the economic analysis in section four. Full summary statistics for the survey and respondent profiles are present in Annex 6.

Importance of research themes

Respondents to both plant and livestock surveys were asked to rate the importance of six research themes that had been identified from Defra statements and strategies. The themes were rated on a scale of from 1 being least important to 6 being most important. In the case of livestock Figure 3.4 presents the results the mean importance ratings for each of the research objectives. “Animal Health and Welfare” was considered to be the most important theme with a mean rating of 5.1. This was followed by “Efficient Food Chain”, “Biodiversity” and “Climate Change – adaptation”. The least important themes were “Climate Change – mitigation” and “Water Quality”. These results indicate that the most important themes relate primarily to livestock themselves and the food chain, suggesting preferences in favour of aspects of research that is internal to farm enterprises and also private goods. Public good/externality themes relating to farm enterprises were considered as less important. This distinction between the nature of themes may explain why adaptation to climate change was considered to be more important overall than mitigation of climate change.

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2.5

3.2

3.3

2.4

4.7

5.1

Clim ate Change -m itigation

Clim ate Change -adaptation

Biodivers ity

Water Quality

Efficient Food Chain

Anim al Health andWelfare

Figure 3.4: Mean importance ratings of the research themes from the livestock survey.

Figure 3.5 presents the mean importance ratings for the six research themes considered in the plant genetics survey. As with the livestock survey those research themes that are more closely aligned with farm enterprises received the highest ratings (Climate change – adaptation; Plant health). The next group of research themes (Climate change – mitigation; Environment and landscape, including biodiversity; Food safety and food chain efficiency) cover a range of issues that may confer both public and private benefits. “Rural livelihoods” was rated with the lowest importance.

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3.3

4.3

3.1

3.4

2.4

4.5

Clim ate ChangeMitigation

Clim ate ChangeAdaptation

Environm ent andLandscape, including

Biodivers ity

Food Safety and FoodChain Efficiency

Rural Livelihoods

Plant Health

Figure 3.5: Mean importance ratings of the research themes from the plants survey.

Overall assessment of priorities

Following the sections of the web survey in which respondents were asked to rate the resource requirements, beneficiaries and outcomes of each of the research projects, we asked respondents how they would allocate the Defra genetic research budget across each of the projects. This question was intended to determine the projects that respondents felt should be prioritised. The mean percentages are presented in Figure 3.6 for livestock and Figure 3.7 for plants.

The animal health and welfare project “Breeding for host resistance to disease / improved welfare-related characteristics” had the highest mean percentage of funding, a result that corresponds with the importance rating for that research theme. However, the next highest mean percentages were for the climate change mitigation and adaptation projects, rather than the efficient food chain projects. These results indicate some level of disconnect between priorities at the theme level and those at the project level. A similar result can be seen with respect to plants where the highest rated theme (plant health) corresponds with the lowest rated project (Controlling soil-borne wheat mosaic virus in the UK by developing resistant wheat cultivars). However, the assessment of climate change projects does more closely reflect the importance of those themes. These results may in fact reflect the multi-outcome nature of many of these projects in that several projects, although allocated to a particular theme, may deliver benefits over a range of themes.

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19.5

15.6

14.1

13.6

11.7

9.9

9.5

7.6

5.9

4.6

Animal health and w elfare: Breeding for hostresistance to disease / improved w elfare-related

characteristics

Climate change adaptation: Genetics research toidentify new molecules to develop vaccines against

new diseases

Climate change mitigation: Modify (farming systems)/livestock breed use / breeding goals to reduce lifecycle

emissions

Ef f icient food chain: Development of breeding goals &tools that meet both industry commercial priorities and

‘public good’

Water quality: Modify (farming systems) /livestockbreed use / breeding goals to reduce pollution

emissions to w ater

Biodiversity: Development of aquaculture as a means toreduce the direct and indirect risk to w ild f ish stocks

Animal health and w elfare: Reducing zoonotic diseasethreat via selective breeding (e.g. E. Coli 0157)

Ef f icient food chain: Breeding dairy/beef cattle forresistance to paratuberculosis & tuberculosis

Water quality: Nutrient Capture traits in farmed f ish

Biodiversity: Genetic options to reduce the risk offarmed salmon interbreeding w ith w ild salmon (thereby

reducing disease risk to w ild species)

Figure 3.6: Assessment of research priorities for livestock (mean percentage of Defra livestock genetics budget).

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13.9

12.6

12.1

11.1

10.3

9.0

8.2

7.1

6.9

6.2

Climate change mitigation: The Def ra Oilseed RapeGenetic Improvement Centre

Climate change adaptation: Genetic approaches tomaintaining w heat yields in a changing environment

Environment and landscape inc. biodiversity: GeneticReduction of Energy use and Emissions of Nitrogen

through cereal production

Climate change mitigation: Breeding oilseed rape w ith alow requirement for nitrogen fertiliser

Rural livelihoods: Investigating Wheat FunctionalityThrough Breeding and End Use

Climate change adaptation: Exploiting associationgenetics - a case study on drought tolerance and

w ater use ef f iciency in w heat

Rural livelihoods: Molecular and biochemical charactersof post harvest quality in brassicas

Food safety and ef f ic iency: Identif ication of geneticmarkers for lodging resistance in w heat

Food safety and ef f ic iency: Tow ards a sustainablew hole-farm approach to the control of ergot

Plant health: Controlling soil-borne w heat mosaic virusin the UK by developing resistant w heat cultivars

Figure 3.7: Assessment of research priorities for plants (mean percentage of Defra plant genetics budget).

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4. Economic analysis of research priorities

4.1 Introduction

In this section we derive evidence on the economic viability of genetic research based on selected priority project areas and using information derived from the electronic survey and other sources. Recall that the terms of reference stated a requirement for evidence on the rate of return and the cost effectiveness of the short listed projects. Further, this evidence needs to be set in the context of "achieving specific outcomes that will take into account the timescale for delivery and Defra time-bound targets". We therefore need to state which of these targets this research appraisal is aiming at.

4.1 Defining relevant policy targets

As seen in section two, government is committed to a range of environmental and rural policy objectives, some of which are evolving through time. Policy targets are set out in The UK Government Sustainable Development Strategy, as well as more specific targets such as compliance with the Water Framework Directive and the Emissions Ceiling Directive. The wider aspiration for farming to be a positive net contributor to the environment25 leads us to consider how this objective can be benchmarked, and this leads us to the most recent national accounts adjustment for agriculture (see Annex 7). These show us that emissions of greenhouse gases (GHG) are the biggest externality and therefore deduction from the sector gross product followed by water pollution.

Arguably some targets are more binding and clearly defined than others and we identify climate change and water quality objectives as having clear timetables and targets that are relatively clear to measure. Coincidently, these are the also the most prominent outcomes identified by researchers themselves.

Climate change

In terms of climate change, the UK’s binding international target is to reduce a basket of six greenhouse gas emissions by 12.5% below 1990 levels by 2012. This sets a domestic goal of reducing CO2 emissions by 20% by 201026. Further, the Energy White paper goals of setting the UK on a path to reduce CO2 emissions by 60% by 2050 (from 145 Mt C /year to under 90Mt C/ by 2050 (DTI 2003). Agriculture currently contributes 7% of UK greenhouse gas emissions mainly in the form of nitrous oxide and methane and to a lesser extent CO2,27

Nitrous oxide from organic and inorganic fertiliser has a significantly higher global warming potential than methane from livestock. Both arable and livestock sectors are therefore implicated in emissions reductions targets, which are set under commitments to the Kyoto Protocol. Of the greenhouse gases carbon dioxide is less of an issue for agriculture, contributing just 1% of the country’s total emissions. However, nitrous oxide and methane are significant, contributing 66 per cent and 46 per cent of the country’s emissions respectively. Ideally we would like to be able to pro rate a proportional reduction of the 2010 commitment to agricultural and then to sub sectors. However, since there is also a 2050 national target, there is essentially a different trajectory that could be followed. Indeed, the 2050 target leaves more room for genetics to be a relevant solution. In summary the preceding 25 http://www.defra.gov.uk/corporate/ministers/speeches/david-miliband/dm070103.htm26 The UK's base year for the Kyoto target of a 12.5% reduction by 2008-12 is the sum of 1990 emissions for CO2, CH4 and N2O and 1995 emissions for HFC, PFC and SF6.http://www.defra.gov.uk/environment/statistics/globatmos/gaemlimit.htm27 Note that carbon dioxide from combustion of fossil fuels in farm equipment, and from electricity use, is likely to be addressed by placing obligations on the energy sector.

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information gives us a strong basis for using methane and nitrous oxide as policy relevant research targets. This rationale is bolstered by the fact that adjusted agricultural accounts show this to be the biggest external cost of agriculture.

Water Framework Directive

The overall aim of the WFD is to protect water resources in the long term by establishing and enforcing a framework for water management strategies that will ensure sustainable, efficient, and equitable management of European water resources. The Directive sets a stringent timetable for implementation that spells out the main steps to be followed for achieving its objectives (see Table 4.6), from which we can see that 2015 is the first time bound target by which an environmental objective must be met. Unfortunately this target of "good (ecological) status" is contested in terms of its meaning for different water bodies. Because of this, it is unclear whether or not this will be met by countries and what the condition for failure will be. It is clear however, that controlling diffuse pollution from both livestock and arable crops has a role to play. The 2015 target means that genetic solutions that we analyse below will potentially make a significant contribution. However, we suggest that addressing GHG as a priority will deliver water pollution mitigation as an ancillary benefit. Numerous uncertainties prevent us from stating how far genetics will go in helping to reach the 2015 target.

Table 4.6: WFD detailed timetable for implementation

Year Issue Reference2000 Directive entered into force Art. 252003 Transposition in national legislation

Identification of River Basin Districts and Authorities

Art. 23 Art. 3

2004 Characterisation of river basin: pressures, impacts and economic analysis

Art. 5

2006 Establishment of monitoring network Start public consultation (at the latest)

Art. 8 Art. 14

2008 Present draft river basin management plan Art. 132009 Finalise river basin management plan

including programme of measuresArt. 13 & 11

2010 Introduce pricing policies Art. 92012 Make operational programmes of measures Art. 112015 Meet environmental objectives Art. 42021 First management cycle ends Art. 4 & 132027 Second management cycle ends, final

deadline for meeting objectivesArt. 4 & 13

Source: European Commission (http://ec.europa.eu/environment/water/water-framework/timetable.html)

Other policy commitments

While less binding several other targets and aspirations are worth noting, if only because these have ancillary benefits that feed into both air and water targets, and because they offer avenues for genetic R&D that will likely leverage most private investment per unit of public input.

Organic targets

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The first is organic production. The UK Organic Action Plan Group’s objective is to promote the organic farming sector in England by encouraging our producers to supply a greater proportion of the organic primary produce consumed domestically. Currently they supply only around 30% of the market. This Action Plan is intended to help British producers to supply the organic market at least at similar levels to the conventional market, reflecting the varying trends in consumption and UK output. The UK conventional market share of indigenous produce in 2001 was 74.7% and DEFRA supports an objective for the UK organic market share to increase to at least 70%. Market share can vary for a variety of reasons including exchange rates which are outside the scope of this plan, and therefore this objective will be reviewed against the conventional market share figure on an annual basis.

An important point to note is that there is now a buoyant market for organic produce. While it is possible to contest consumer motives for organic purchases, there is some consensus that consumers are willing to pay for public good outcomes through this channel. This demand is also a compelling incentive for private sector involvement that helps deliver public good outcomes. This may be a way round the barriers noted in section 1.5 and might be a potential focus for further genetic funding.

Biofuel targets

The introduction of regulatory obligations has spurred a considerable market potential for renewable fuel sources. Like organic produce this, offers an avenue for public good objectives to be realised through private channels.

4.2 Economic analysis

The preceding discussion has helped to clarify the most urgent policy obligation as climate change and the mitigation of GHG. We suggest that this objective will deliver ancillary water quality objectives though we cannot specify the extent to which a genetic route will contribute to specific WFD targets.

Here, we take the GHG objectives as a basis for calculating returns to genetic R&D investment. We note that many of the priority research ideas offered the potential to deliver on a range of public good outcomes beyond GHG, but targeting this research return simplifies analysis considerably. Figure 4.8 details how the relevant economic case is developed.

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Figure 4.8: Framework for economic analysis.

For given research priority or project targeting a policy objective (i.e. GHG mitigation), we determine whether there is an alternative (counterfactual) method of delivering the output equivalent to that delivered by the project. Assuming for simplicity that there is no alternative method, then we need to derive the costs (C2) and benefit (B2) of conducting the research. Benefits can either be left in other units of account B2' (e.g. tonnes of methane gas) or, if possible, given a monetary value B2''. Comparing B2’’ with C2 and taking account of time profiles is a cost-benefit analysis. Comparing B2' with C2 is cost-effectiveness analysis.

Supposing now that there was an alternative method of delivering the objective targeted by the genetic research. In this case the analysis requires the cost (C1) of conducting that research. Technically for an appropriate cost-effectiveness comparison, the benefit of this method (B1) should be exactly equivalent to that delivered by the genetic approach. Provided these conditions are met it is possible to compare the relative cost effectiveness of options. In most cases, the relative options are unlikely to be delivering on exactly the same benefits and so a CE comparison can be complex. It may be the case that a cost-effectiveness comparison is restricted to one category of important benefits.

Research Priority cctargeting

Counterfactuals; is this the only way?

No

YesAlternative Intervention

Cost (C1) Benefit (B1)Cost (C2) Benefit (B2)

C2-C1 = incremental cost

Monetary B2” Non monetary B2’

B2’/C2 = cost effectiveness

B2’’- C2 = Net benefit (CBA)

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4.3 Incremental cost-effectiveness analysis

The original specification refered to the need to conduct an "incremental cost effectiveness analysis". Specifically to conduct an incremental cost-effectiveness analysis comparing additional research to the best or currently implemented intervention for achieving specific outcomes , that will take into account the timescale for delivery and Defra time-bound targets.

The term "incremental" does not have the standard economic meaning. Normally, the effects of an incremental change refer to the effect of an additional unit of a specific measurement - for example, the effect of an additional pound spent on a greenhouse gase mitigation project. Here we would not be comparing the effects of an incremental change in some intervention, but rather the effect of switching interventions. ICER is a ratio of the differences between the costs and effects of two interventions.

We assume that the delivery of Defra targets relates to environmental change and that other things equal, near-term benefits are preferable. But for demonstration purposes we again consider greenhouse gas (GHG) emissions policy as the policy objective of greatest interest. Defra guidance suggests that we do not discount the emissions of greenhouse gases. We can interpret the requirement to mean the incremental costs, benefits and carbon savings of improved genetics compared to a business as usual (non-genetic) scenario under which may or may not be the technically least cost. We assume however that BAU does not imply a strategy of no emissions mitigation, but instead some alternative mitigation strategy that is focused at farm level abatement

Alternatively we can posit the genetics improvement as the BAU relative to the alternative technical intervention. Either way, we compare the present value additional costs of genetics with the PV cost of in our example methane digesters or feed additives (livestock). We then, divide this PV costs by the lifetime carbon savings from the alternatives. We assume a truncated time horizon to render the comparison:

PVg = Present value of genetics PVc = present value of the counterfactual Qg = quantity of GHG mitigated under genetic option (within time frame)Qc = quantity of GHG mitigated under alternative

Technically then, the ICER can be considered by:

and substituting various options for the counterfactual costs and benefits. The use of this comparison is in terms of comparing the unit cost of incremental benefits delivered by different methods. Some methods may be less expensive than a genetic approach, others may be more expensive. In the latter case, we are only interested in these methods if it delivers a greater quantity of benefit.

If this numerator is negative then there is an incremental saving from pursuing the genetics alternative unless of course, the specified alternative delivers a relatively larger emissions reduction over the specified time period.

We stress that this is something of a false comparison though. That is, the genetics option would normally deliver other benefits alongside the GHG; e.g. enhanced productivity and possibly water quality. We also note the difficulties of pursuing a comprehensive analysis

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here, particularly the notional use of policy instruments which would ordinarily require some wider form of general equilibrium analysis of their true costs.

Finally, returning to a basic advantage of genetics, we conjecture that the permanent and cumulate change affected this way, is likely to be more cost effective than any technical alternative

The following section first presents the rate of return evidence, before considering information on the relative cost-effectiveness of genetic options for meeting the target objectives. It draws on a combination of data drawn from the stakeholder survey and information from published literature.

4.4 Rate of return analysis on selected research projects

Public projects are typically judged on whether they pass a test rate of return on initial investment. This information can be derived from comparing cost outlays with the stream of expected returns to the investment. By characterising our research projects in terms of their projected costs and the monetary environmental benefits, some indication of both the net present value (NPV) or the economic rate of return (ERR) can be derived by a discounting procedure.

The following equation summarizes the procedure:

where NPV is the net present value, B is a measure of monetary benefits (element i at time t), C represents the monetary cost, and r is the discount rate. When all the market and non-market costs and benefits are measured in monetary terms, the aggregation is simple: the discounted value of the total costs over time is subtracted from the total benefits also discounted over time. Positive NPV (i.e. benefits exceed the costs) indicates that the policy is superior to the baseline ‘do nothing’ situation in terms of overall value. If the NPV is negative (i.e. the costs are larger than the benefits) then the policy should not go ahead, unless there are strong non-monetized benefits to consider.

The economic rate of return (ERR) of a given net benefit (i.e. B-C) flow can be derived by considering the discount rate r that would return NPV= 0. In short, we are seeking ERR at least equal to the Treasury test discount rate of 3.5%. A more compelling case can be made with reference to returns on other public sector investments. Alternatively, from the calculation we would be looking for is a positive NPV using a discount rate of 3.5%.

Calculating either of these indicators requires a range of assumptions that are informed by our survey data and which can be subject to sensitivity analysis to test our assumptions. Typical variables that we may wish to find switching values for, relate to our assumptions on adoption and for the natural optimism bias inherent in scientists' views of the potential use of their research. However, the latter may be offset by strategic over estimation of the costs of conducting research.

Spreadsheets underlying the analysis can be obtained from the authors.

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The first section presents a livestock case study using a basic model of trait diffusion for UK livestock breeding developed by Amer et al (2007). We use this model as the vehicle to evaluate the likely returns to some of the livestock projects prioritised by the stakeholder survey. We then go on to consider some results from the plant projects. These are analysed using more rudimentary assumptions about research costs and adoption.

4.5 Livestock

Rather than making new assumptions about the diffusion of genetic improvements, our analysis is based on an existing model set out in Amer et al. (2007), which provides an underlying model of dissemination of genetic progress from industry breeding programs to UK commercial flocks and herds over time and generations. The original model is based on an understanding of UK breeding structures and was originally used to consider the financial returns to breeding, in terms of pure productivity traits. The paper describing this model is attached at Annex 8.

Our focus here goes beyond purely productivity traits to compare the stated research costs for genetic improvement with the monetary value of returns in terms of key improvements from selected projects, for example the value of methane gas emissions.

Accordingly the original model can be augmented with results from the livestock projects survey, which provided data on research costs, research duration, percentage changes in key outcomes (e.g. methane) and adoption rates among key target groups. We combine these with the model assumptions to derive a hypothetical picture of the rate and direction of genetic progress in industry sheep and beef breeding programs. From this, we can quantify the extent of public good benefits likely to accrue to initial research investment, which is itself derived from our survey returns. This allows us to derive an economic rate of return to research on specific outcomes

Method

First, the method of Amer et al. (2007) is used to quantify the benefit of a 1% change in farm productivity, assuming that it takes 10 years of cumulative genetic selection in the various breeding programmes to achieve the 1%. A similar approach is taken to quantify the benefits of a 1% reduction in greenhouse gas emissions, and a 1% reduction in disease burden. These results are then calibrated for four research projects based on surveyed projections of costs. Discounted disseminated benefits are then adjusted for anticipated adoption rates, the time lag until research completion, and the projected cumulative impacts after 10 years are then compared against research project costs using standard cost-benefit methodology.

Survey results on livestock genetics research projects

Table 4.7 presents the mean results from the web-based survey on the likely costs and outcomes for 4 research projects with end applications in livestock genetic improvement. For each project, the duration of the research project, the average total costs of research per year, the adoption rate, and percentage effects on farm productivity, greenhouse gas emissions and disease burden are quantified. An important caveat to this work is that research costs have been considered in terms of beef, sheep and dairy production, but we only have models for the sheep and beef industries. Consequently, research costs are reduced by one third in our calculations, under the assumption that a third of the research budget would be spent on applications in dairy cattle.

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Model of gene flow through the industry

The model of Amer et al. (2007) quantifies the effects of flows of genes from elite performance recorded breeding populations to commercial production animals through the sale of breeding males (rams and bulls), and the inheritance of traits through generations in the commercial flocks and herds (see Annex 1 for an outline of these structures). Thus, the chain of events from research to industry impact can be listed as follows;

i. A period of research of specified durationii. Performance recording breeders adopt new technology and, over time, changes occur

in the rate and direction of the genetic change they achieve. These changes are gradual, but permanent and cumulative

iii. Breeding males (rams and bulls) are sold from the performance recording breeding programs for mating to breeding females (ewes and suckler cows) on commercial farms

iv. A crop of offspring (lambs and calves) are born with improved performance characteristics.

v. Some of these offspring are retained as breeding females (ewes and suckler cows) with improved performance characteristics (excluding progeny of rams and bulls from terminal sire breeding programs).

The breeding females retained pass one half of their superiority to the next generation of calves, and from which a new proportion are kept as replacement breeding females (excluding the offspring of sheep crossing sires, which do not breed their own replacements).

The model of Amer et al. (2007) has been parameterised against current industry conditions, namely: i) penetration rates of sires from performance recorded breeding programs; ii) numbers of breeding females of various breed types in commercial flocks and herds; iii) numbers of sires sold per breeding female; iv) numbers of breeding females mated per male per year; v) survival rates of breeding males; vi) survival rates within commercial female flocks and herds; and vii) breeding female replacement rates.

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Table 4.7: Summary of mean survey results for 4 different research projects with a view to influencing the rate and direction of genetic progress in UK livestock industries Costs are in £000’s.

Project Duration (years)

Research costs(£’000/yr)

Adoption rate (%)

Increase in productivity

(%)

Reduction in GHG emissions

(%)

Reduction in disease burden

(%)Climate change mitigation a 7.10 1587.74 47.50 3.83 -7.00Efficient food chain project b 6.93 1297.90 47.17 2.28 -8.90Animal health and welfare project 1 c? 14.38 2426.88 63.75 10.00 -11.25Animal health and welfare project 2 d? 7.71 1937.78 55.00 4.43 -9.43a Climate change mitigation: Modify (farming systems) /livestock breed use / breeding goals to reduce lifecycle emissionsb Efficient food chain: Development of breeding goals & tools that meet both industry commercial priorities and ‘public good’c Animal health and welfare: Reducing zoonotic disease threat via selective breeding (e.g. E. Coli 0157)d Animal health and welfare: Breeding for host resistance to disease / improved welfare-related characteristics

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Unit economic benefits of farm productivity changes

We calculate the per animal impact of a percent change in farm productivity by first assuming that a 1% increase in farm productivity results in a 1% increase in gross margin, where gross margin is defined as revenues per animal minus variable costs per animal. We have conservatively assumed that gross margins are £100 per cow per year, £100 per calf per lifetime, £20 per breeding ewe per year, and £10 per lamb per lifetime. Thus, the effects of a 1% change in productivity are taken as a 1% change in each of these figures (£1 for cows and calves, £0.20 for ewes and £0.10 for lambs).

Unit economic benefits of greenhouse gas emissions

Using international standards for monitoring country level emissions of 48kg/head per year for beef animals, and 8kg/head/year for sheep, a 1% reduction in greenhouse gas emissions equates to the following:-

0.48kg/head/year for breeding cows,0.96kg/head/lifetime for calves, and 0.08kg/head per year for ewes and lambs.

Taking a conversion factor of 23 carbon equivalent kgs per kg of methane and a long term carbon emission shadow price of £76.00 per tonne, gives the following:-

£0.83 for cows, £1.66 for calves, and£0.14 for ewes and lambs.

We also considered a lower carbon emission shadow price of £45.00. This effectively reduces model projections of methane to 60% of benefits under the high shadow price assumption.

Unit economic benefits of reduced disease burden

Farm gate implications

We assume that the cost of disease burden equates to 30% of the current gross margin for each farming system, with the costs manifesting themselves in terms of:-

preventative treatment, constrained ability to use more profitable farming systems including use of

productive breeds which are currently disease susceptible, and reduced performance and productivity with clinical and sub clinical cases and costs

of treating clinical cases.

For example, the total output value from sheep production in the UK in 2005 was £660 million, of which at least 50% are taken up by variable costs and an estimated disease cost of £160 Million (Nieuwhof and Bishop, 2005). This calculates to 48% (160/(0.5 x 660) = 48%), and so at least for sheep, our assumption of 30% of gross margin for disease costs is conservative. Accordingly, at 30%, a 1%reduction in disease burden would be worth £0.33 for cows and calves £0.63 for ewes and £0.33 for lambs.

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Animal welfare implications

While a reduction in disease burden of farm animals would result in direct benefits to farmers at the farm gate, there would also be benefits to society resulting from the reduction of disease induced discomfort and consequent improved animal welfare. While there are willingness to pay estimates for the social value of welfare, these are somewhat controversial. We assume here that the benefits from reduced animal suffering to society would be twice those of the economic benefits to the farmer from reduced treatment, compromised performance and costs of animal deaths.

Timeframe of impact

We assume that the research leads to changes to the selection capabilities and policies of sheep and cattle breeders who supply breeding males or semen for artificial insemination to currently performance recording breeders after multiplying by the adoption rate specified from the survey for the relevant research project. We assume that the benefits do not start accruing until after the completion of the research. This can be considered conservative, because the research programs are long, and it is more likely that at least some of the research outcomes will be adopted by sheep and cattle breeders prior to the conclusion of the project.

The benefits of livestock breeding technologies are slow but permanent and cumulative. We assume that the ultimate projected improvements, generated from the survey of scientists, would accumulate over a 10 year period, with equal increments achieved per year, and full and permanent improvements captured within the performance recording breeding population by year 10.

Calculations

For each of the four research programs investigated, we computed:- net present values of all benefits discounted at a 3.5% real discount rate; an internal rate of return on all economic impacts up to the farm gate and with direct

consequences for farmers (i.e. methane emissions and animal welfare effects omitted); and

an economic rate of return counting direct benefits to farmers as above, plus benefits from reductions in greenhouse gas emissions and assuming that the animal welfare benefit from reduced disease burden can be quantified as twice the direct economic benefit to farmers.

Results and Discussion

Table 4.8 shows the results from applying the assumptions outlined above for productivity, greenhouse gas emissions and disease burden to the gene flow model of Amer et al. (2007). Results are the net effects of a 1% improvement in each category achieved ultimately after 10 equal increments of 0.1% per year using a 3.5% discount rate to account for the delays from the onset of adoption by breeding programs until the benefits are accrued by commercial farmers. These benefits decline by approximately 10% for every 1% increase in the discount rate.

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Table 4.8: Discounted cumulative benefits (£000’s) from a 1% increase in performance characteristics after 10 years with 100% adoption for 5 sectors of the UK sheep and suckler beef industries using a discount rate of 3.5%.

Productivity by 1%

Disease burdena

reduced by 1 %Emissionsb

reduced by 1%

Penetration rate

Crossing sires 3599.00 1187.67 185.00 0.09Hill sheep sires 11564.00 3816.12 466.00 0.23Terminal sheep sires 5310.00 1752.30 319.00 0.15Dual purpose beef sires 11397.00 3761.01 661.00 1.00Terminal beef sires 1673.00 552.09 120.00 1.00Total (@ current penetration rate) 16852.64 5561.37 21918.41a Direct farmer benefits only. Animal benefits to society were assumed to be twice this sum.b Computed at the higher shadow carbon price of £76.00 per tonne of C02.

Table 4.9 shows the calculated net present values of benefits after research costs, assuming a discount rate of 3.5%. Net Present Values are very high with, an average of one half of the benefits coming from productivity improvements (range 30-73% across the projects), and the other half from either methane emission reductions or reductions in the disease burden. Internal rates of return range between 11% and 18%. The present values of benefits are clearly affected strongly by the discount rate. This is due to the long research durations, and the timeframes for dissemination to commercial farmers through gene flows.

When computing the economic rate of return, it was assumed that society benefits from reductions in greenhouse gas emissions (according to the shadow value assumed for carbon equivalents), and that the value to society of a reduction in disease burden and associated improvements in animal welfare would be two times the value of financial benefits from these savings achieved by farmers.

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Table 4.9: Present values of research costs and benefits, and net present values (£000’s) for 4 projects targeting improvements in the rate and direction of industry genetic progress in sheep and suckler beef breeding programs

Project PV Research

costs

PV Productivity

benefits

PV farm disease burden benefits

PV Emissions benefits

PV Total Farmer benefits

PV Total

Society benefits

NPV Farmer

and Society

% PV benefits to farmer

IRR%a

ERR%b

Climate change mitigation 1 (high shadow price) 6723 23303 0 55345 23303 55345 71925 30 11 18

Climate change mitigation 1 (low shadow price) 6723 23303 0 33207 23303 33207 49787 41 11 16

Efficient food chain project 2 5371 13781 17732 0 31513 35464 61605 47 14 18Animal health and welfare project 1 18298 64127 23807 0 87935 47614 117251 65 11 13

Animal health and welfare project 2 8767 31195 21909 0 53104 43818 88154 55 14 18

Combined (high shadow price) 39159 132407 63448 55345 189178 182240 316798 55a Internal rate of return calculated as the discount rate that results in the PV of total farmer benefits being equivalent to the PV of research costs.b Economic rate of return (ERR) calculated as the discount rate that results in total farmer plus total society benefits (PV Emissions benefits plus 2 times the PV of disease burden) being equivalent to the PV of research costs.

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Conclusions

The combined NPV’s (considering both benefits to farmers and society) of the four projects considered were in excess of £300 Million with a 3.5% discount rate. The PV of research investment to achieve this was approximately £40 Million.

NPVs were sensitive to discount rate because of the long time frames involved. IRR’s ranged between 11 and 14%.

Economic rates of return (ERR’s) were calculated as farmer benefits in addition to the shadow values of methane reductions and assuming that the value of animal welfare improvements was twice the internal value to farmers from a reduction in disease burden. These ranged from 13 to 18%.

4.6 Plants

In this section we present the assumptions and results of the analysis of the rate of return to plant genetic research themes. As with the livestock case, we combine survey data and project specific assumptions to demonstrate the potential economic rates of return to investment in the research areas that match identified policy objectives. Unlike the livestock example, we do not have a formal model to characterize transmission of plant genetic improvements. It is nevertheless possible to characterise the rate of return to specific research themes in terms of the following main assumptions:

i) the stated research costs;

ii) some assumption about the principal category of benefits i.e. productivity, yield or greenhouse gas emissions.

iii) the lifetime of the project, i.e. the period over which the research was to be conducted. This in effect represents a time lag before any benefits can come on line;

iv) the length of impact: this relates to the number of years over which we are assessing the benefit stream. In a sense this is an arbitrary choice, but the affect of long time horizons will be influenced by our discount rate assumptions;

v) adoption rate: this relates to the numbers of producers actually using the technology in question. In essence, this provided us with the basic multiple for the calculation of benefits.

A list of the projects we attempt to appraise and the main variables are outlined in Table 4.10 below.

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Table 4.10: Outline of Major Parameters Adopted within the Modelling Process

Project Main Commodity

Area

Adoption Rate

Length of

Impact

Length of

Project

CostPer

annum

Breeding oilseed rape with a low requirement for nitrogen fertiliser

OSR 0.70 30 7 1,554,560

The Defra Oilseed Rape Genetic Improvement Centre OSR 0.60 30 7 1,144,420

Genetic approaches to maintaining wheat yields in a changing environment.

WHEAT 0.44 30 7 1,128,870

Identification of genetic markers for lodging resistance in wheat.

WHEAT 0.70 30 7 1,056,730

Investigating Wheat Functionality Through Breeding and End Use.

WHEAT 0.50 30 7 964,440

Controlling soil-borne wheat mosaic virus in the UK by developing resistant wheat cultivars

WHEAT 0.70 30 7 509,300

Baseline

A baseline or ‘counterfactual’ was developed for each of the projects from historical growth rates derived from sources such as the agricultural census and the British Survey of Fertiliser Practice (Defra, 2006). Prices were derived for the commodities from the Farm Management Handbook (SAC, 2006) and the Agricultural Pocketbook (Nix, 2006). A flexibility function was employed to provide an indication of the variance in prices that may occur over a 30 year period. Thus all projects were run under a number of scenarios, taking the prices of yield, nitrogen and N2O. These were first halved, to give a low price scenario and then doubled, to give high price scenario. The aim was to give an indication of the robustness of the results.

Costs

Annual running costs for the project were calculated using information provided by the web survey respondents. Essentially the number of institutes estimated to conduct the research were multiplied by the staff cost per institute to give an indication of total staff costs. This was added to annual non-staff running costs multiplied by the number of institutes expected to conduct this research.

Benefits

For each project, respondents were asked to predict the rate of change in a number of benefits for a 30 year period of study. Predominantly for crops, these benefits were; i) growth in yield, and ii) decrease in nitrogen used. The median rates provided by the respondents are outlined in Table 4.11.

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Table 4.11: Median rates of yield increase and nitrogen input reduction resulting from plants genetics projects.

YieldIncrease%

N Reduction

%Breeding oilseed rape with a low requirement for nitrogen fertiliser

5.38 9.23

The Defra Oilseed Rape Genetic Improvement Centre 8.29 5.63Genetic approaches to maintaining wheat yields in a changing environment.

4.64 3.21

Exploiting association genetics - a case study on drought tolerance and water use efficiency in wheat.

5.58 -

Genetic Reduction of Energy use and Emissions of Nitrogen through cereal production.

7.21 3.17

Identification of genetic markers for lodging resistance in wheat.

3.33 1.11

Towards a sustainable whole-farm approach to the control of ergot.

0.44 0.29

Investigating Wheat Functionality Through Breeding and End Use.

1.54 1.35

Molecular and biochemical characters of post harvest quality in brassicas.

5.63 0.83

Controlling soil-borne wheat mosaic virus in the UK by developing resistant wheat cultivars

3.75 0.38

Adoption Profile

The median length for all research projects was around 7 years. Hence, annual costs of the research were calculated over the period 2006 to 2013. After this period a maintenance cost of 10% of total research funding over a 30 year period was employed to account for the cost of transferring the results of the research project into the private sector. It was assumed that 5% of the target population would adopt the technology within the first year and this would slowly accumulate up to the maximum level outlined in Table 4.5 at the end of a 20 year period. Impact would then be sustained for another 10 year period. This is an important assumption, although according to geneticists, in contrast to other research benefits which tends to decline after a peak to be replaced by other technology, a genetic advance is ‘cumulative’, i.e. it is effectively a single step which can, in theory, be sustained for infinity. Consequently, the choice of a 30 year period is rather arbitrary and the results reported have to be read as lower estimates.

A further assumption relates to the role of the supply chain. Survey respondents were asked to identify the major beneficiaries of genetic research, which include breeders as well as farmers etc. However, within the crops model no assumptions across the structure of the industry were employed. This means that the model assumed that benefits are captured within the supply chain, though no indication of the distribution of benefits is given.

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Deriving rates of returnThe economic rate of return (ERR) was calculated for both the private and social benefits of each research project. The economic benefits of yield and N-saving from each research project were calculated as a private rate of return. Then a social rate of return was developed using nitrous oxide as an example. To do this, it was necessary to convert applied nitrogen into its flux gas equivalent and then to derive a shadow price for these emissions.

Emissions factors (EF's) for gas fluxes (N20) from applied nitrogen are controversial and obviously depend on a range of factors such as soil and crop type, rainfall and temperature. However, they do provide a reasonably good indicator of some of the social benefits from genetic research into crops as it is strongly related to the main chemical input within a cropping enterprise, namely nitrogen,. Dobbie and Smith (2003) report EF's ranging between 0.4 and 6.5% of nitrogen applied across a range of crops and conditions in the UK. Alternatively, they report a value of 1.25% +/- 1% as the default value used by IPCC (1997). We employ this factor of 1.25% of applied nitrogen are converted to gas. Based on Dore et al (2003) nitrous oxide has a global warming potential of 310 times that of carbon dioxide. Government Economic Service working paper 'Estimating the Social Cost of Carbon Emissions was published (HM Treasury) suggested £70/tC (within a range of £35 to £140/tC) as an illustrative estimate for the global damage cost of carbon emissions (in 2000 prices). It also suggested that these estimates should be increased by £1/tC per year in real terms to reflect the increasing costs of climate change over time.

Accordingly, if one tonne of CO2 is equivalent to £45 ($85/tCO2)28 then 1 tonne of nitrous oxide, given this factor, is valued at £13,950. The results for both private and social rates of return are presented in Table 4.12 below.

28 Stern Review (2006) http://www.hm-treasury.gov.uk/media/FDE/AD/faq.pdf

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Table 4.12: Rates of Return and price variance (50% lower prices), for Crop Projects, percentages

Rate of ReturnYield and

N save50% Price Reduction

N2O 50% Price Reduction

All 50% Price Reduction

Breeding oilseed rape with a low requirement for nitrogen fertiliser34 25 48 37 53 41

The Defra Oilseed Rape Genetic Improvement Centre29 21 43 32 47 35

Genetic approaches to maintaining wheat yields in a changing environment. 61 35 56 32 72 44

Identification of genetic markers for lodging resistance in wheat.39 29 54 42 59 46

Investigating Wheat Functionality Through Breeding and End Use.37 27 53 40 58 45

Controlling soil-borne wheat mosaic virus in the UK by developing resistant wheat cultivars 46 35 62 48 68 54

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Table 4.12 shows rates of return to all projects are high for both the private (yield and n saving) and social (N2O) benefits. The internal rate of return for the private benefits range from 29% to 61%, for social 43% to 62% and for private and social benefits together, rates of return vary between 47% and 68%.

Even reducing the value of the benefits by 50% leaves a rate of return over the recommended Treasury rate for investment of 3.5%. To put these results into context Alston et al. (2000) applied a meta-analysis to studies on research evaluation and found an average rate of return of 20% for ex-post studies of research benefits. Studies into UK agricultural research have provided similar results.

Accordingly, against these two benchmarks, it seems genetic research in plants is providing returns in excess of this rate, some are providing almost double this, indicating a sound economic investment.

Notably, social returns are higher than private accrued from the research projects, indicating that the social benefit, e.g. a research project’s contribution to climate change, provides a justifiably high rate of return for government investment.

In addition, it should be reiterated that genetic R&D is unique compared to other forms of research, as it provides a step-wise improvement in research that can, in theory, be sustained into infinity. Due to the nature of the modelling process, this study has evaluated the benefits of this research over a 30 year period. However, it is conceivable that these benefits would last longer, indicating a possibly higher rate of return than reported here.

4.7 Discussion

Rate of return evidence provides the most stringent test of economic efficiency taking due account of the consistent monetary value of project outputs and time preference. In this analysis near term environmental benefits will work to improve the viability of a research stream. In relative terms, the return to plant genetic research appears to be greater than the returns to livestock research. However, the assumptions made in both evaluations differ to reflect the structures of plant and animal breeding and dissemination of benefits.

In the case of livestock we are considering 8-14 years of research, and then 10 years to get full adoption, plus further delays for improvements in the breeding programs to disseminate from the breeding programs to commercial animals, particularly for benefits arising from changes in the performance of breeding females. The duration of the research projects is a significant factor in this. ERR’s were 13-18% when breeding programs start to adopt at the conclusion of the research. They increase to 19-24% range if we assume that breeding programs start to adopt half way through the research programmes. The latter is a more realistic reflection of current practice. We do not investigate the rationale or justification for altering discount rates in this analysis.

These rates of return stand comparison with rates from other public investments. For example, while not an ideal comparison, information from the National Audit Office suggests that early PFI contracts were let on the expectation of an internal rate of return (IRR) to investors of 15 to 17 per cent29.

While this analysis has not prioritised investments, the Net Present Value of these assessments can provide some informaton on the magnitude of the projects and the assoicated returns. CEA (see below) does not take into account when carbon reductions are achieved,

29 http://www.nao.org.uk/pn/05-06/05061040.htm

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whereas both cost-benefit analysis and benefit cost ratios produce higher benefit values for earlier carbon reductions.

4.8 Counterfactuals

A range of counterfactuals and policies can be defined for meeting some of the policy objectives stated in Defra documentation (see Table 4.13). Beyond alternative technical solutions, the original invitation suggested that the effectiveness might be compared to policy instruments. Of principal concern are emissions to air30, and for illustrative purposes we consider how climate change objectives might be addressed using alternative approaches.

A range of technical interventions can be advanced to deal with emissions of nitrous oxide and methane31. These innovations amount to good agricultural practice, command and control regulation or use of market based instruments focussed on input use or harmful emissions Thus in the case of nitrous oxide policy instruments may include input taxes or quotas or voluntary codes of practice, that might include crop nutrient planning and manure/slurry uses. Methane research into feed additives anaerobic digestion of animal manures to create biogas. Alternatively, some form of emissions trading could be a joint solution for both gases.

Determining the relative cost-effectiveness of market based instruments is potentially complicated because there will be a range of effects that ideally need to be explored and quantified in a general equilibrium framework that is beyond the scope of this project.

In the following sections we adopt a partial analysis of the costs of implementing alternatives to genetic improvements.

30 A review of instruments for water pollution mitigation is available at http://www.defra.gov.uk/environment/water/quality/diffuse/agri/reports/pdf/dwpa08.pdf31 http://www.defra.gov.uk/farm/environment/climate-change/index.htm

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Table 4.13: Non-genetics approaches to reducing agricultural GHG emissions.Nitrous oxide The main sources of nitrous oxide from agriculture are inorganic nitrogen fertilisers and the storage of manures.

The following measures can be taken to reduce nitrous oxide emissions:

regular fertiliser spreader testing to ensure accuracy of application

operator registration and training

crop nutrient management planning

soil analysis to match application of fertiliser to need

use of technology to target nutrient use, and cut machinery use

integrate manures into fertiliser regimes to reduce inorganic fertiliser use

improve slurry handling

input taxes or quotas

Methane

The majority of methane from agriculture results from the normal digestive processes of livestock, the remainder comes from animal waste. Reductions arise from:

expected reduction in livestock numbers  following CAP reform

ongoing increases in productivity and fertility

research into feed additives

anaerobic digestion of animal manures to create biogas.

emissions trading

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4.9 Reducing greenhouse gas emissions – comparing genetics to alternative approaches

Livestock emissions

The livestock genetics survey identified one project with the potential to reduce greenhouse gas emissions from livestock: “Modify (farming systems)/livestock breed use/breeding goals to reduce lifecycle emissions”. It was estimated that genetic selection could result in a reduction in GHG emissions of 7% and that 47.5% of farmers would benefit from the research, which we will assume equates to the upper range of adoption by farmers. Over its estimated 7 year lifespan this research project was also estimated to cost £11.27 million.

We assume that the research project is applied to reducing enteric methane (CH 4) emissions from dairy cattle, which are estimated at 103.8 kg/head/year a further 25.49 kg/head/year arises from manure (Baggott et al., 2005). We use dairy cattle in this example as they are responsible for higher methane emissions than other livestock types. Table 4.14 presents the estimated cost per tonne reduction in methane over a 20 year period compared to alternative options identified by AEA Technology (1998) and British Biogen (no date).

The effects of the genetics research have been estimated over the UK dairy industry of 20,700 holdings. A linear adoption profile is assumed in which 5% of holdings take up the technology in the first year, with 47.5% of holdings adopting by the twentieth year. However, the 7% decrease in methane production requires a period of cumulative genetic selection, which we assume to occur incrementally and linearly from year 1 to year 10, i.e. 0.7% per year. It is further assumed that each holding will have 100 cattle. The alternative approaches have been assessed on a per holding basis (again assuming 100 cattle) as we have no information on likely adoption rates. These differences do not allow a direct comparison of the aggregate effect of the different technologies on reducing CH4 emissions, however per holding savings and cost per tonne of CH4 avoided are estimated across all alternatives.

Although the genetics project gives the lowest reduction in methane output, except when compared to probiotics, the effect of spreading the research cost over the number of benefiting farms means that it has the lowest cost per tonne of methane avoided by an order of magnitude. An important point is that as the research is publicly funded the costs are not borne privately by the farmer as would be the case with the alternative approaches. However, we have not included the private benefits to the farmer such as improved performance (feed additives) and reduced energy costs (anaerobic digestion).

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Table 4.14: Comparison of genetics research with alternative approaches to methane reduction in dairy cattle.

Genetics project Alternative approachesLivestock breeding goals

to reduce lifetime emissions

Propionate precursors a Probiotics b

Anaerobic digestion of manure c

High outcome/low cost

Low outcome/high cost

Emissions reduction 7% 25% 7.50% 85% 50%Adoption rate 47.50%Number of holdings 20700

Example: Dairy breeding herd: manure CH4/head/year = 25.49kg 0.02549 0.02549 0.02549 0.02549 0.02549

Example: Dairy breeding herd enteric CH4/head/year = 103.8kg 0.1038 0.1038 0.1038 0.1038 0.1038

Assume 100 cattle/holdingTotal CH4 output (tonnes/holding/year) 12.929 12.929 12.929 12.929 12.929

Reduction in CH4 (tonnes/holding/year) 0.7266 2.595 0.7785 2.1667 1.2745Reduction in CH4 over 20 years adjusted for adoption profile and genetic improvement rate (tonnes) 71626.44 51.90 15.57 43.33 25.49

Capital costs 11267800 N/A N/A 60000 70000Annual costs Assumed part of AI costs 5732 3427 7000 10000NPV of annual costs N/A 81465.50 48705.91 99486.82 142124.03

Total NPV (20 years @ 3.5% discount rate) 11267800 81465.50 48705.91 159486.82 212124.03

Cost of CH4 avoided (£/tonne) 157.31 1569.66 3128.19 3680.49 8321.85a Propionate precursors – use of feed additives such as organic acids, malate or fumarate to increase use of hydrogen to produce propionate, hence reducing CH4. b Probiotics – microbial feed additives containing live cells and a growth medium, CH4 emissions reduced due to increased animal productivity.c Anaerobic digestion – production of biogas containing CH4 and CO2 that can be used to produce heat and/or electricity with CH4 converted to CO2.

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Plant emissions

All of the plants genetics projects identified a reduction in nitrogen inputs as an outcome with consequent reduction in nitrous oxide (N2O) emissions. In this section we consider the four projects that were considered to deliver the highest reductions in nitrogen inputs:

Climate change mitigation: Breeding oilseed rape with a low requirement for nitrogen fertiliser

Climate change mitigation: The Defra Oilseed Rape Genetic Improvement Centre Climate change adaptation: Genetic approaches to maintaining wheat yields in a changing

environment Environment and landscape including biodiversity: Genetic Reduction of Energy use and

Emissions of Nitrogen through cereal production

As with the calculations of economic rates of return we assume the same 30 year period for benefits to accrue with uptake taking place linearly over the first 20 years. Baseline nitrogen application levels also followed the same trends as for the ERR calculations. This allowed us to calculate the annual reduction in nitrogen application, and hence N2O emissions, which were then summed over the 30 year period and used to calculate the cost per tonne of avoided N2O arising from each of the four projects.

The range of counterfactual scenarios for nitrogen reduction in crops is more diverse than for the livestock example above as they might include a number of different management approaches that would differ across farms and according to local or climatic conditions. Instead we adopt a simplified aggregate approach across UK outputs for winter oilseed rape and winter wheat. We assume an arbitrary regulatory policy that reduces inputs and by extension imposes a yield cost on producers. The regulation is applied in such a way as to reduce overall nitrogen application by 10% over and above the existing trend in application, and that in turn crop yields are consequently reduced by 0.5% over the long term trend. This reduction in yield is arbitrary, but can easily be scaled upwards or downwards as required.

Table 4.15 summarises the outcomes of the genetics projects and the counterfactual scenarios for winter wheat and winter oilseed rape. Although the genetics projects result in lower levels of N2O reduction, both due to lower potential reductions and lower uptake, those reductions are achieved at a lower unit cost than when compared to the counterfactual scenarios where the cost of N2O reduction is based on lost output. It is important to note that the genetics scenarios do not include the values of increased yields that ranged between 4.6% and 8.3% for the projects under consideration. Furthermore, any additional management or regulatory costs associated with the counterfactual scenarios have not been included. It is also the case that any distributional effects of the different approaches, i.e. whether the costs of reducing the externalities are borne by the public or privately, are not explicitly considered.

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Table 4.15: Comparison of genetics research with alternative approaches to N2O reduction in arable farming.

Genetics Projects CounterfactualsBreeding oilseed rape with a low requirement for

nitrogen fertiliser

The Defra Oilseed Rape Genetic Improvement

Centre

Genetic approaches to

maintaining wheat yields in a changing

environment

Genetic Reduction of Energy use and

Emissions of Nitrogen through cereal production

Oilseed rape 0.5% yield reduction

Wheat 0.5% yield reduction

Nitrogen reduction (%) 9.23 5.63 3.21 3.17 10 10

N2O reduction (tonnes) 3559 1870 2529 3893 5963 18549

Uptake level (%) 66.15 55.91 64.12 56.85 100 100

Lifetime project cost £9,985,085 £6,512,280 £7,111,860 £6,987,889 - -

Value of lost production (NPV @ 3.5%) - - - - £24,671,845 £73,612,964

Cost of N2O reduction (£/tonne) £2,805 £3,483 £2,813 £1,795 £4,138 £3,969

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Incremental cost effectiveness analysis

Recall that this economic analysis aims to show the specific projects are an economically attractive policy approach relative to any alternative means of delivering on the policy goal. Clearly for some forms of outcome there are potentially many counterfactuals to a genetic approach. Accordingly, our comparison could involve comparison between genetic costs of delivery and the costs of several alternative approaches.

Moreover, cost-effectiveness analysis is actually complicated by the fact that most, if not all, projects will be delivering on more than one outcome. There is therefore a considerable degree of incommensurrability between them. To do cost-effectiveness analysis, we have to choose a commensurate benefit and possibly ignore others. Greenhouse gases provide a commensurate outcome in several of the projects considered here. Although these can be given a monetary value, cost effectiveness, in terms of cost per tonne of carbon, (or equivalent) provides a useful way of ranking projects. However, it is not sufficient because it can be misleading when there are net benefits per tonne of carbon which says nothing about the scale of the projects. To maximise benefits or minimise costs to the UK, policies should also be ranked according to total net benefits.

The tables above have presented some evidence of the average cost-effectiveness of each of the genetics and non-genetics outcomes in terms of the cost per tonne of either CH 4 or N2O emissions avoided. On this measure both livestock and plants the genetics research projects compare favourably with non-genetics approaches. However, the estimated costs per tonne of emissions avoided by definition compare each project or counterfactual against a “do nothing” scenario. More useful is a measure of performance of the genetics projects relative to the counterfactuals, this can be achieved by estimating the incremental cost-effectiveness ratio (ICER). The ICER compares the differences in emissions and costs resulting from the genetics project or counterfactual with the lowest reduction in emissions with the alternatives providing higher emission reductions:

,

where, CA low is the PV of the costs of the alternative with the lowest emissions reduction, CAi

is the PV of the costs of alternative i, QA low is the reduction in emissions under the alternative with the lowest emissions reduction, and QA low is the reduction in emissions under alternative i. In essence the ICER estimates the cost of each additional tonne of emissions avoided that would not have occurred under the reference scenario.

Table 4.16 presents the ICERs for the dairy cattle scenarios presented above, in which the genetics project is used as the reference case due to its lowest reduction in CH4 emissions compared to the other counterfactual scenarios. For this example the emissions reductions and costs are presented on a per holding basis, with the genetics research costs equally distributed across the 47.5% of farms that are assumed to take up the technology. The total reduction in emissions has also been distributed evenly across holdings to provide an average reduction. A per holding basis is used for these calculations in order to avoid making assumptions regarding the uptake of the counterfactual approaches across the industry. The estimated ICERs indicate that each additional tonne of CH4 emissions avoided under the counterfactual scenarios range in cost from £1,800 per tonne, for introducing propionate precursors to feed rations, to £11,589 per tonne for anaerobic digestion under the low outcome/high cost scenario. Although the genetics approach offers the smallest emissions reduction relative to the counterfactuals, it does so with the lowest cost. Consequently, the ICER is of more use here in distinguishing between the counterfactual options, if further CH4 emissions are to be sought. In this case the use of propionate precursors would be the most attractive option.

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The ICERs for N2O emission reduction for the plants alternatives are presented in Table 4.17 for oilseed rape and in Table 4.18 for wheat. In contrast to the example for livestock, the calculations here are for the industry in aggregate rather than on a per holding basis. For oilseed rape the lowest reduction in emissions was offered by the “The Defra Oilseed Rape Genetic Improvement Centre” at 1870 tonnes, therefore this option forms the reference case. The alternative genetics project “Breeding oilseed rape with a low requirement for nitrogen fertiliser” has an ICER of £2056 per tonne, whilst the counterfactual scenario has an ICER of £4437 per tonne. This result suggests that the genetics project “Breeding oilseed rape with a low requirement for nitrogen fertiliser” would be the preferred approach when compared to the alternative genetics project and the counterfactual. The difference between the ICERs of this project and the counterfactual, when compared to the difference in emissions reduction, indicate that it would be cost effective to allocate funds to encourage increased uptake of the genetics project’s outputs across the industry in order to increase the overall emissions reduction.

In the case of wheat, the lowest reduction in emissions arises from the genetics project “Genetic approaches to maintaining wheat yields in a changing environment”. The ICER for the alternative genetics project, “Genetic Reduction of Energy use and Emissions of Nitrogen through cereal production”, is -£91. This negative ICER indicates that on the criteria of reducing N2O emissions, this second project is superior in that a greater reduction in emissions can be achieved for a lower cost. The ICER for the counterfactual scenario is £4,151 per tonne. The far greater emissions reduction under the counterfactual would suggest that funding an increased the uptake of the genetics outcomes would not offset the additional costs as was the case with oilseed rape.

The results of the ICER calculations need to be treated with some caution. Firstly, the range of alternatives need not be mutually exclusive, in that the genetics approaches and/or the counterfactuals could be jointly implemented. Secondly, what we are considering here are in fact multi-outcome scenarios, where there may be impacts on yields or other environmental outcomes arising from either the genetics or counterfactual approaches that are not captured within the ICER calculation. These issues may be particularly relevant in the case of livestock where the emissions reductions are relatively modest under the genetics project. Finally, as noted previously, this analysis ignores the distributional aspects of combining approaches with different degrees of public and private costs and benefits.

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Table 4.16: Incremental cost effectiveness ratios for livestock genetics and counterfactual approaches

Genetics project Alternative approachesLivestock breeding

goals to reduce lifetime emissions

(dairy cattle)Propionate precursors Probiotics

Anaerobic digestion of manure

High outcome/low cost Low outcome/high costReduction in CH4 emissions (tonnes/holding) 7.28 51.90 15.57 43.33 25.49Cost per holding (£) 1145.98 81,465.50 48,705.91 15,9486.82 212,124.03

Cost of CH4 avoided (£/tonne/holding) 157.31 1569.66 3128.19 3680.75 8321.85

Difference in CH4 emissions (tonnes) - 44.62 8.29 36.05 18.21Cost difference (£) - 80,319.52 47,559.93 158,340.84 210,978.05

Incremental Cost Effectiveness Ratio (£/tonne) - 1800.27 5740.25 4392.82 11,588.80

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Table 4.17: Incremental cost effectiveness ratios for plant genetics and counterfactual approaches (oilseed rape).

Genetics Projects CounterfactualBreeding oilseed rape with a low requirement for

nitrogen fertiliser

The Defra Oilseed Rape Genetic Improvement

Centre

Oilseed rape: 10% reduction in N

application with 0.5% yield reduction

Reduction in N2O emissions (tonnes) 3559 1870 5963

Cost (£) 9,985,085 6,512,280 24,671,845

Cost of N2O avoided (£/tonne) 2805.59 3482.50 4137.49

Difference in N2O emissions (tonnes) 1689 - 4093

Cost difference (£) 3,472,805 - 18,159,565

Incremental Cost Effectiveness Ratio (£/tonne) 2056.13 - 4436.74

Table 4.18: Incremental cost effectiveness ratios for plant genetics and counterfactual approaches (wheat).

Genetics Projects CounterfactualGenetic approaches

to maintaining wheat yields in a

changing environment

Genetic Reduction of Energy use and

Emissions of Nitrogen through cereal production

Wheat: 10% reduction in N

application with 0.5% yield reduction

Reduction in N2O emissions (tonnes) 2529 3893 18549

Cost (£) 7,111,860 6,987,889 73,612,964

Cost of N2O avoided (£/tonne) 2812.12 1794.99 3968.57

Difference in N2O emissions (tonnes) - 1364 16020

Cost difference (£) - -123,971 66,501,104

Incremental Cost Effectiveness Ratio (£/tonne) - -90.89 4151.13

5. Links between Defra funding and private sector research structures

5.1 Introduction

Re-prioritisation of research objectives and funding may have distributional implications. The project terms of reference required some consideration of the wider impacts of public funding decisions on the private sector. To explore this, this section addresses the last requirement of the terms of reference to consider the relationship between Defra funding and the private sector science base. The review draws on the small literature, and opinion from telephone contacts with industry stakeholders.

In considering the views of different industry stakeholders we have also tried to discern the potential extent of any industry impact of fundamental changes in Defra funding for genetics. We have tried to gather a selection of opinion from industry representatives on their perceptions of Defra funding, and its reach into their business area. The responses were generated by posing questions about their view on changing national priorities, and hypothetical questions on what might happen were Defra funding to be re prioritised, possibly away from genetics entirely. In gauging opinion, we were interested to determine whether any shift in funding might engender significant multiplier (e.g. employment) effects in related industries.

The public private interface offered by Defra funding works in different ways and the synergies depend on a combination of informal and formal links that can be affected by funding changes. Defra recognises that its the role that its funding can play to leverage private sector research and innovation and its willingness to invest in public research offers several advantages to indigenous industries and inward investors.

There is an interesting literature examining the links between publicly funded R&D and private innovation and investment. Indeed, several studies try to substitute problematic rate of return analysis with more partial assessments of the private sector effects of public research; the private sector elasticity of public research investment, measuring the percentage change in private funds allocated in response to a percentage change in public investment. This literature is ably summarised by Scott et al (2001) who in attempting a general overview32 of empirical studies, reach somewhat ambiguous conclusions. Beyond the question of the time frame over which such a measure can be derived, Scott et al conclude that in some sectors, public R&D can just as easily substitute (crowd out) or complement (leverage) private effort, with the net effect being uncertain. Citing a study by Cockburn and Henderson (2000), they nevertheless stress that biotechnology is an exception with considerable growth potential.

Scott et al are on the whole optimistic about the benefits from public research. They suggest that public investment can create strategic value. By creating and maintaining variety (including the supply of skilled graduates), research maintains the diversity of science and technology options vital to a flexible innovation system faced with uncertain future demands and opportunities. These conditions would seem to be a good description of UK agriculture and the changing global environmental in which it operates. This option value is a compelling reason to maintain a foothold in research, especially where there is an element of irreversibility in re creating capacity and an interface, however limited, with the private sector. Indeed, this interface is of value to both

32 i.e. their overview covers a multitude of government R&D (e.g. including defence) spending and not just biotechnology spending

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public and private sectors and something that emerged spontaneously from stakeholder discussions.

In casual phone contacts, there was a considerable level of consensus about the role of Defra funding in bridging the gap between public and private sectors. Some of these views accorded with those of the research community.

Views on the impact of potential changes to Defra funding and priorities were obviously split between those parts of industry that have traditionally been underpinned by public funding, and those industries that have developed a largely private market-led genetics R&D capability. Thus, the distinction between ruminant livestock on the one hand, and pig and poultry research on the other. However remote from Defra funding, no respondents thought it would be advantageous for Defra to withdraw from its current role, though none suggested particularly drastic consequences were this to happen.

Most contacted were aware that Defra were in the process of re focussing its objectives. Some expressed a confusion or conflict in trying to reconcile the emphasis on public goods with productivity goals. It was suggested that promoting productivity could effectively amount to fostering wise resource use i.e. less environmental impact per unit of output. All respondents saw a role for Defra in part funding LINK and Case arrangements. Plant breeders broadly supported the genetic improvements networks, though several offered suggestions of how their function might be improved.

5.1 Livestock science

In livestock science, commercialisation in pigs and poultry is advanced relative to developments in ruminant livestock (beef, dairy and sheep). Unsurprisingly with the former the strength of feeling about Defra funded research was influenced by perceptions of its ability to further commercial objectives and more immediately, a rate of return in terms of productivity. This is the objective to which all forms of R&D funding are subservient.

In the case of ruminant livestock these commercial drivers are less clearly articulated, though ultimately no less important to the larger number of small producers. But the relatively un commercialised state of the sector means that mobilising private budgets for research is more challenging, and consequently, that public funding is more significant in driving research objectives. This structure has ultimately given rise to common pool research endeavours and selection indices were held up as a good example of how public funding can create a common resource that benefits the whole industry, but which no individual producer could afford to create. It is obviously difficult to know what the counterfactual would be, but the view is that Defra input has generated benefits many orders of magnitude its original investment.

It was suggested that the common breeding index model could be used to develop breeding indices for public good traits. But Defra leverage is the key, since the evolution of breeding resources inherent in these indices is a long term collaborative outcome. Moreover such resources are fragile and while they can be reoriented towards other public good objectives, this process takes time and patience to develop the new traits.

There was recognition of the other ways to deliver on public good outcomes than through animal genetics. Some respondents articulated the need for management changes at the farm scale to address some of the pollution problem. But others observed that a one-off genetic intervention

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would be more cost-effective in the long term relative to the regulatory incentives that might otherwise be necessary to change behaviour. The latter would involve on-going incentive payments whereas outcomes through genetics can deliver permanent and cumulate benefits that do not require the recurrent costs of near term technical fixes.

Broadly speaking, the arguments advanced by those involved in ruminant livestock research were reminiscent of economic arguments made to defend other ex situ genetic resource collections. In both cases there are good economic reasons why government would invest to protect this research capital in order to safeguard the conduit through which public goods can be delivered. This is a more obvious lever Defra may have in contrast to pigs poultry and arable crops.

In these sectors earlier commercialisation has meant that no similar collaborative structures have evolved. Rate of return/productivity objectives dominate and companies are global in terms of their production. Defra LINK funding is important as a channel of communication, but in contrast to ruminant livestock, companies appear to be less flexible about accommodating public good objectives. Indeed, were Defra to insist on these then firms would more readily rely on their own resources or turn to levy bodies for their research needs.

5.2 Plant science

As noted in section 2.3, market structure is a general preoccupation of Defra funded plant scientists. The need to construct better links to deliver public good outputs has been observed on several occasions recently33. Private sector plant breeders and recognise the gaps that have opened up between some parts of the science chain (molecular, crop agronomy and plant breeding), and between the aspirations of Defra and their own requirement for commercial rates of return. While this was seen as regrettable, it was not lamented, as much as by the academic and institutional plants research community. Private plant developers suggest that the most significant developments last few decades have been made by the private sector. Nevertheless all those contacted recognised the significance of a public research capacity in plant science. Some suggested that Defra funded research serves their long term horizon scanning function which allows them concentrate on short terms reaction to market demand. Defra money provides research that cans longer term changes - e.g. in relation to EU policy.

Some respondents suggested a desire to have more dialogue as to how money is allocated to research themes. It was unclear how this might come about, although some suggested the genetic improvement networks were a possible means of communication. Other respondents suggested that these were too far removed to have any policy impact. Overall respondents recognised the importance of the various links between themselves and Defra.

5.3 General observations

Overall, apart from ruminant livestock, it is unclear whether the absence of public funding would drive significant change in private sector production in the UK. Arguably, in the case of plant genetics, historical decisions to privatised research have actually weakened government ability to target current policy objectives. While this could not have been foreseen, it serves as a salutary example of why one should be cautious about radical funding decisions. 33 British Wheat Breeders (2006) A proposal for a public-private partnership in wheat crop science genetics May 2006BCPC (2006) Crop genetic improvement for sustainability – the way forward with public private partnerships.

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As things now stand, many plant genetics companies are already operating on a global scale and can potentially derive research insights from elsewhere. In general research funding is not as important a determinant as regulatory burden. However, conversely, there is at least some evidence that the presence of public sector funding can mobilise private sector effort and dialogue. An important question concerns the targeting of this public funding and the ways in which public funding is used to engage with private breeders.

Few sectors suggested that they had not benefited from Defra funding in some indirect way. Even in the private case, the possibilities of LINK and Case arrangements were generally viewed positively. Several respondents noted that LINK was possibly the only conduit between them and Defra and as such needed to be maintained. But it was also suggested that Defra needed to be mindful of why the private sector enters link agreements and that seeking to over-emphasise public good goals was simply not in their immediate interest. Other respondents were keen to point out that Defra funding could be better deployed than at present to build better links between the public and private sectors. One respondent suggested that the on-going review of levy boards offered an opportunity to configure new channels for public-private dialogue.

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6. Conclusions

In this section we return to the rationale for Defra spending on genetic R&D.

Government is increasingly committed to the delivery of public good outcomes from policy, but their attainment is often hampered by market failure. This means delivery on these objectives may not be forthcoming from the private sector. Because of this, public intervention may be necessary to help realize these objectives.

In determining the form of intervention, government has many choices, including further support to targeted research. Investment decisions are normally guided in part by economic efficiency criteria - i.e. rates of return on investment.

The evidence presented here suggests that genetics is both economically efficient and cost-effective relative to alternative plausible intervention options. This is generally a good reason to continue investing in the area as a complement to other policy instruments. Beyond this basic criterion, investment in genetics provides options and flexibility to adapt to changing environments. These options have value.

But continuing current patterns of funding may not be sufficient to overcome some of the hurdles presented by market structure in plant, and to a lesser extent in livestock science. These barriers currently limit the extent to which public good outcomes can be realized. Despite these barriers, we argue that the continued public funding provides a window of opportunity through which change can be enacted. Removal of contacts afforded by research is unlikely to improve the development of mutually beneficial outcomes for both public or private breeders.

Public research funding is complementary to other policy levers, and there are longer term developments that are likely to increase its effectiveness in influencing private sector activity. Most immediately is the evolution of regulation (command and control or market-based), that can be brought to bear on market structure. These pressures are already becoming apparent in response to external policy commitments. For example, the renewable fuels obligations are a development where private profitability objectives can be harnessed to deliver public goods.

Changing consumer demand is also influencing the way in which markets may be used to deliver public good outcomes. Consumer awareness and demand for organic production is a niche where public and private interests converge.

In both cases, there is certainly a case for considering how targeted genetics research can be deployed to foster early stage development of markets to deliver ecosystem goods and services.

This report suggests some caveats to the conclusions arising from the rate of return analysis. Specifically, the complexity of links between public and private innovation mean that the outcomes of publicly funded research are often indirect, unplanned and unpredictable. Measuring the full extent of returns to research is likely to be a very difficult undertaking.

Option value and the strategic value of a diverse research base, are powerful reasons to maintain a domestic presence in genetic research. Public funding maintains a domestic capability and a policy lever that can be difficult to replace if forfeited. Indeed, this is possibly a lesson from recent history of plant genetic research in the UK. These reasons bolster a consensus on the rates

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of return that can be anticipated from further developments in life sciences and the delivery of both environmental and health benefits from investment in animal and genetic research.

We find no compelling evidence of the impacts of withdrawal. Conversely, there is at least some evidence that the presence of public sector funding can mobile private sector effort and dialogue. An important question concerns the targeting of this public funding.

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7. References

AEA Technology (1998). Options to Reduce Methane Emissions (Final Report). Report to DGXI

Alston, J., Hyde, J., Marra, M.C. and Mitchell, P. (2002). An ex-ante Analysis of the Benefits from the Adoption of Corn Rootworm Resistant Transgenic Corn Technology http://www.agbioforum.org/v5n3/v5n3a01-alston.htm AgBioForum, 5(3): 71-84

Alston, J., Pardey, P.and Smith, V. (1999). Paying for Agricultural Productivity. Johns Hopkins University Press

Alston, J.M., Marra, M.C., Pardey, P.G., and Wyatt, T.J., (2000). Research returns redux: a meta-analysis of the returns to agricultural R&D. Australian Journal of Agricultural Economics 44 (2), 185–216

Alston, J.M., Norton, G.W. and Pardey, P.G. (1995). Science under Scarcity: Principles and Practice for Agricultural Research Evaluation and Priority Setting. Ithaca NY: Cornell University Press

Alston, J.M., Pardey, P.G. and Taylor, M.J. eds. (2001). Agricultural Science Policy: Changing Global Agendas. Johns Hopkins University Press.

Alston, J.M., Pardey, P. and Taylor, M. (2001). The Evaluation of the Economic and External Health Benefits from Canola Research. In: Agricultural Science Policy Changing Global Agendas. Johns Hopkins University Press. Baltimore, USA

Amer, P.R., Nieuwhof , G.J., Pollott, G.E., Roughsedge, T., Conington, J and Simm, G. (2007). Industry benefits from recent genetic progress in sheep and beef populations in the UK. Animal (In submission)

Araji, A.A., Sim, R.J. and Gardner, R.L. (1978). Returns to Agricultural Research and Extension Programs: An "Ex-Ante" Approach. American Journal of Agricultural Economics, Vol. 60, No. 5, Proceedings Issue (Dec., 1978), pp. 964-968

Baggott, S.L., Brown, L., Milne, R., Murrells, T.P., Passant, N., Thistlethwaite, G., & Watterson, J.D. (2005). UK Greenhouse Gas Inventory, 1990 to 2003: Annual Report for submission under the Framework Convention on Climate Change. Report to Defra April 2005

Barnes, A.P. (2001). Towards a Framework for Justifying Public Agricultural R&D: The Example of UK Agricultural Research Policy. Research Policy 30, 663-672

Barnes, A.P. (2002). Public Agricultural R&D and ‘Social’ Total Factor Productivity. Agricultural Economics 27 (1), 65-74

Bradford Mills (1998). Agricultural Research Priority Setting: Information Investments for the Improved Use of Research Resources. The Hague, The Netherlands: International Service for National Agricultural Research, 1998, xii, 1-143

75

Braunschweig, T. (2000). Priority setting in Agricultural Biotechnology Research: Supporting public decisions in developing countries with the analytical hierarchy process. ISNAR Research Report 16. http://www.isnar.cgiar.org/priority.htm

Braunschweig, T. (2001) In Gijsbers, G., W. Janssen, H. Hambly Odame and G. Meijerink (eds.) Planning Agricultural Research: A Sourcebook. Wallingford, UK: CABI Publishing. pp 231-241

British Biogen (no date) Anaerobic Digestion Of farm and food Processing residues: Good Practice Guidelines. http://www.r-p-a.org.uk/content/images/articles/adgpg.pdf

Cockburn, I.M., and Henderson, R.M. (2000). Publicly Funded Science and the Productivity of the Pharmaceutical Industry. NBER Conference on Science and Public Policy, Washington, DC

David, P.A., Mowery, D. and Steinmueller, W.E. (1992). Analysing the Economic Payoffs from Basic Research. Economics of Innovation and New Technologies 2(4): 73-90

Defra (2006).  British Survey of Fertiliser Practice.  Defra, London

Defra (2006). First Report of the Sustainable Farming and Food Research Priorities Group. http://www.defra.gov.uk/science/documents/RPG/Papers/FinalRPGreport.pdf

Defra (2006). Greenhouse Gas Policy Evaluation and Appraisal in Government Departments. April 2006 Department for Environment, Food and Rural Affairshttp://www.defra.gov.uk/environment/climatechange/uk/ukccp/pdf/greengas-policyevaluation.pdf

Dobbie, K. and Smith, K. (2003). Nitrous oxide emission factors for agricultural soils in Great Britain: the impact of soil water-filled pore space and other controlling variables Global Change Biology 9 (2), 204–218

Dore, C.J., Goodwin, J.W.L., Watterson, J.D., Murrells, T.P., Passant, N.R., Hobson, M.M., Haigh, K.E., Baggott, S.L., Thistlethwaite, G., Pye, S.T., Coleman, P.J., King, K.R., (2003). National Atmospheric Emissions Inventory, UK Emissions of Air Pollutants 1970-2004 , Defra, UK

Doyle, C.J. and Ridout, M.S. (1985). The Impact of Scientific Research on UK Agricultural Productivity. Research Policy, 14, 2, pp. 109-116

Dreze. J., and Stern, N. (1987). The theory of cost-benefit analysis. In: Handbook of public economics Vol II, ed. A. Auerbach and M. Feldstein, North Holland

Echeverria, R.G. (1990). Assessing the Impact of Agricultural Research. In: Echeverria, R.G. ed. Methods for Diagnosing Research System Constraints and Assessing the Impact of Agricultural Research. Volume 2. The Hague: ISNAR, pp. 1-31

Eftec (2004). Framework for Environmental Accounts for Agriculture. Final Report prepared for Defra, July 2004. Economics for the Environment Consultancy and the Institute for European Environmental Policy

Falconi, C.A, Omamo, S.W., Leteren, G. and Iraqi, F. (2001) An ex-ante economic and policy analysis of research on genetic resistance to livestock disease: Trypanosomosis in Africa. Agricultural Economics 25, 153-163

76

Fetrow, J. (1994). Economics Of Recombinant Bovine Somatotropin On US Dairy Farms. AgBioForum 2(2). http://www.agbioforum.org/v2n2/v2n2a07-fetrow.htm

Georghiou, L. (1998). Issues in the Evaluation of Innovation and Technology Policy, Evaluation 4:1, pp 37-51

Gittinger, J.P. (1982). Economic Analysis of Agricultural Projects. Johns Hopkins University Press, Baltimore, USA

Gray, R. and Malla., S (2001). The Evaluation of the Economic and External Health Benefits from Canola Research. In: J.M. Alston, P.G. Pardey, and M.J. Taylor, eds., Agricultural Science Policy: Changing Global Agendas, Johns Hopkins University Press, 2001

Gray, R. and S. Malla. (1998). A Note on Evaluating Agricultural Policy in the Presence of Health Care Cost Externalities: Dairy Production Quotas and Coronary Heart Disease Costs. Canadian Journal of Agricultural Economics, 46(2) (July 1998): 247-56

Hanley, N. and C. Spash (1993). Cost-benefit analysis and the environment, Edward Elgar

H.M. Treasury (2003). Green Book, Appraisal and evaluation in central government, http://www.hm-treasury.gov.uk./Economic_Data_and_Tools/greenbook/data_greenbook_index.cfm

H.M. Treasury and The Cabinet Office (2006). Stern Review on the Economics of Climate Change, http://www.hmtreasury.gov.uk/independent_reviews/stern_review_economics_climate_change/sternreview_index.cfm

IPCC (1997). Greenhouse Gas Inventory Reporting Instructions, Vol. 1; and Greenhouse Gas Inventory Reference Manual, Vol. 3, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Intergovernmental Panel on Climate Change, Bracknell, U.K

Kalter, R.J. and Tauer, L.W. (1987). Potential Impacts of Agricultural Biotechnology. American Journal of Agricultural Economics, May 1987

Kelley, T, J Ryan and B. Patel (1995). Applied Participatory Priority Setting in International Agricultural Research: making trade-offs transparent and explicit. Agricultural Systems 49, pp 177-216

Khatri, Y.J. and Thirtle, C.G. (1996). Supply and Demand Functions for UK Agriculture: Biases of Technical Change and the Returns to Public R&D. Journal of Agricultural Economics, 47, 3, pp. 338-354

Linder, R.K and Jarret, F.G. (1978). Supply Shifts and the Size of Research Benefits. American Journal of Agricultural Economics, 60, 1, pp. 48-56

Little, I. M. D., and J. A. Mirrlees. (1974). Project Appraisal and Planning in Developing Countries. London, Heinemann

Marra, M., Pardey, P., & Alston, J. (2002). The payoffs of agricultural biotechnology: An assessment of the evidence. Washington, DC: International Food Policy Research Institute

77

Mullen, J.D., Alston, J.M., Sumner, D.A., Kreith, M.T. and Kuminoff, N.V. (2003). Returns to University of California Pest Management Research and Extension: Overview and Case Studies Emphasizing IPM. University of California, ANR Publication 3482, Oakland CA

Nagy, J. (2003) Economic Returns to Feed Barley Yield-increasing and Disease Resistance Research at the Alberta Field Crop Development Centre, Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 51 (3), 281–298

Nieuwhof , G.J and Bishop, S.C. (2005). Costs of the major endemic diseases of sheep in Great Britain and the potential benefits of reduction in disease impact. Animal Science 81: 23-29

Nix, J. (2006) Farm Management Pocketbook, The Andersons Centre, Melton Mowbury

Norton, G.W. and Pardey, P.G. (1987). Priority-Setting Mechanisms for National Agricultural Research Systems: Present Experience and Future Needs. ISNAR Working Paper 7. The Hague: International Service for National Agricultural Research

Norton, G.W., Pardey, P.G. and Alston, J.M. (1992). Economic Issues in Agricultural Research Priority Setting. American Journal of Agricultural Economics 74(5), pp. 1089-94

Pavitt, K. (1991). What makes basic research economically useful? Research Policy 20, pp. 109-119

Phillips, J.P. (2006). Transgenic Enhancement of Nutrient Cycling: Moving Toward An environmentally Sustainable Animal Agriculture. 8th World Congress on Genetics Applied to Livestock Production, August 13-18, Brazil

Rosenberg, N. (1990). Why Do Firms Do Basic Research (with their own money), Research Policy 19, pp. 165-174

Ruttan, V.W. (1982). Agricultural Research Policy. Minneapolis: University of Minnesota Press

SAC (2006). Farm Management Handbook, SAC, Edinburgh

Scott, A. Steyn, G., Geuna, A. Brusoni, S, and Steinmuller, E. (2001). The Economic Returns to Basic Research and the Benefits of University-Industry Relationships: A literature review and update of findings. Report for the UK Office of Science and Technology. Brighton, SPRU, November

Shumway R. (1981). Subjectivity in ex-ante research evaluation, American Journal of Agricultural Economics, 63(1), pp.169-173

Stewart, J. (1995). Models of priority-setting for public sector research. Research Policy 24:115-126

Stiglitz, J (2000). Economics of the Public Sector, 3rd edition. WW Norton, NY

Thirtle, C. and Bottomley, P. (1988). Is Publicly Funded Agricultural Research Excessive? Journal of Agricultural Economics, 39, 1, pp. 99-111

78

Thirtle, C.G. et al (1999). Agricultural R&D Policy in the United Kingdom, Chapter 6 in: Alston et al (1999) Paying for Agricultural Productivity, Johns Hopkins University Press Baltimore, USA

Thirtle, C.G. and Townsend, R. (1997). Efficiency Differences in European Agriculture: Multi-National Productivity Indices and the Returns to R&D with Spillovers. Paper Presented at EAAE Seminar on Innovation for Innovation, Zandvoort, Holland, 21-23 April, 1997

Thornley, J.H.M. and Doyle, C.J. (1984). Management of publicly funded agricultural research and development in the UK: questions of autonomy and accountability. Agricultural Systems 15, 4, 195-208

Wise, W.S. (1984). The Shift of Cost Curves and Agricultural Research Benefits. Journal of Agricultural Economics, 35, 1, pp. 21-30

Wise, W.S. (1986). Calculation of Rates of Return in Agricultural Research from Production Functions. Journal of Agricultural Economics, 37, 2, pp. 151-162

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