Cover

Farm-level Modelling: Techniques, Applications and Policy

Copyright

Contents

Contributors

Foreword

Reference

Preface

Acknowledgements

1 Policy Impact Assessment

1.1 Introduction

1.2 Evolution of EU Agricultural Policies and Parallel Development of Impact Models

1.3 The Widening Role of Farm-level Modelling in Impact Assessment

1.3.1 Interactions between activities

1.3.2 Farm heterogeneity

1.3.3 Agriculture–environment interactions

1.3.4 Dynamics and structural change

1.3.5 Market feedback

1.4 What Models Do We Need to Assess Tomorrow’s Agricultural Policies?

Notes

References

2 Positive Mathematical Programming

2.1 Introduction

2.2 Review of Existing Methods

2.2.1 PMP, calibration and cost estimates

2.2.2 PMP and cost function characteristics

2.2.3 PMP models and latent activities

2.3 PMP Application for Policy Assessments

2.4 Conclusions

Notes

References

3 Modelling Farm-level Adaptations Under External Shocks

3.1 Introduction

3.2 Review of Existing Methodologies

3.3 LP Modelling

3.3.1 Land

3.3.2 Feed

3.3.3 Labour

3.3.4 Herd size

3.3.5 Exogenous adaptation measures

3.3.6 Modelling runs

3.3.7 Input data

3.4 Limitations

3.5 Application

3.5.1 Irish farms under climate change

3.5.2 The case of alternative farrowing housing for sows in the UK

3.6 Summary

References

4 Farm-level Modelling, Risk and Uncertainty

4.1 Introduction

4.2 Review of Existing Models

4.3 The Utility Efficient Programming Model

4.3.1 Gross margin distributions

4.3.2 Calculation of spot price gross margins

4.3.3 Calculation of gross margins using futures markets

4.4 Application

4.4.1 Run 1: coupled area payments

4.4.2 Run 2: the decoupled Single Farm Payment (SFP)

4.4.3 Runs 3 and 4: spot prices and average prices

4.5 Limitations of the Model and Conclusions

Notes

References

5 Modelling Farm-level Biosecurity Management

5.1 Introduction

5.2 Review of Existing Methods

5.3 The Core Model: Adoption Decisions in Biosecurity Management

5.3.1 Multiple correspondence analysis (MCA)

5.3.2 Hierarchical clustering analysis (HCA)

5.3.3 Logistic regression

5.4 Empirical Application

5.4.1 Description of data

5.4.2 Analysis of data by MCA and hierarchical clustering

5.4.3 MCA and interpretation of the dimensions it generates

5.4.4 Additional explanatory variables

5.4.5 Clustering and characterization of the biosecurity practices

5.4.6 Logistic regression

5.5 Limitations and Discussion

Notes

Acknowledgements

References

6: Modelling Farm Efficiency

6.1 Introduction

6.1.1 Policy context

6.1.2 Defining efficiency

Technical efficiency

Cost efficiency

Total factor productivity

6.2 Review of Alternative Methodologiesto Examine Efficiency

6.2.1 Stochastic frontier analysis (SFA)2

6.3 Examining the Efficiency of Irish Dairy Farms

6.3.1 Background to the research question

6.3.2 The theoretical model

6.3.3 Data sources

6.3.4 Model outputs

Technical efficiency of Irish dairy farms: 1979–2012

Model validation

6.4 Conclusions and Relevance of Research Findings to Policy Makers

Notes

References

7: Quantifying Agricultural Greenhouse Gas Emissions and Identifying Cost-effective Mitigation Measures

7.1 Introduction

7.2 Quantifying On-farm GHGE missions

7.2.1 Moving beyond the farm gate: life cycle analysis (LCA)

What is LCA?

Why use LCA?

What are the main steps in LCA?

Guidance for undertaking LCA of food supply chains

7.2.2 Existing studies of GHG emissions in food supply chains

7.2.3 Identifying ways of reducing GHG emissions

7.2.4 Challenges and limitations in the quantification of emissions

Improving the comparability of results

Dealing with interactions between measures

Characterizing variability and uncertainty

Data quality and availability, particularly in developing countries

7.3 Application

7.3.1 The Global Livestock Environmental Assessment Model (GLEAM)

7.3.2 The CAPRI model and the MITERRA-Europe assessment tool

7.3.3 The Livestock Environmental Assessment and Performance (LEAP) partnership

7.4 Concluding Remarks

References

8: Moving Beyond the Farm: Representing Farms in Regional Modelling

8.1 Introduction

8.2 Review of Regional Modelling

8.2.1 Overview of aggregation issues under mathematical programming

8.2.2 Representing farms in farm-based regional modelling: conceptual issues

8.3 The Core Model

8.3.1 Limitations and challenges of the regional model

Loss of farm-level accuracy

Aggregation of farms

Endogeneity of prices in product and input markets

Calibration of the model

Other limitations

8.4 Review of Regional Modelling Applications

8.4.1 Climate effects

8.4.2 Water resources

8.4.3 Environmental loadings from agriculture

8.4.4 Policy analysis

8.5 Application

8.5.1 Edwards Aquifer management

EDSIM model structure

Simulation results

Main findings

8.5.2 Economic and groundwater use implications of climate change in the Ogallala Aquifer region

Structure of the model

Results

Implications

8.6 Summary and Conclusions

References

9: Farm-level Microsimulation Models

9.1 Introduction

9.1.1 Modelling complexity

9.2 Applications of Farm-level Microsimulation Modelling

9.2.1 Hypothetical analyses

9.2.2 Static modelling

9.2.3 Behavioural modelling

9.2.4 Dynamic modelling

9.2.5 Impact of macroeconomic change

9.2.6 Spatial models

9.2.7 Environmental analysis

9.3 Conclusions and Future Directions

Note

References

10: Scaling Up and Out: Agent-based Modelling to Include Farmer Regimes

10.1 Introduction

10.2 Review of Approaches to Scaling

10.3 Core Model

10.4 Application

10.4.1 The Lunan catchment, east Scotland

10.4.2 Evaluation of regimes

10.4.3 Model verification

10.4.4 Results

10.4.5 Limitations

10.5 Summary and Conclusions

Note

References

11: Catchment-level Modelling

11.1 Introduction

11.2 Review of Existing Methodologies

11.2.1 Output-based integration

11.2.2 Scenario-based integration

11.2.3 Dynamic integration

11.3 Core Model

11.3.1 Estimating silt costs to the downstream water users

Direct silt-related costs

Indirect silt-related costs

11.4 Application

11.4.1 Case study of Dwangwa catchmentin central Malawi

11.4.2 Linear optimization model assumptions

Model activities

Household characteristics

Slope of farmland

11.4.3 Results

Payments for watershed services and land under SLM

Silt costs savings and climate variation

Silt costs savings from engaging in PWS in perspective

11.4.4 Limitations

11.5 Conclusion

11.5.1 Policy implications

11.5.2 General conclusions

Notes

References

12: Modelling Food Supply Chains

12.1 Introduction

12.2 Overview of Reasons to Model Supply Chains

12.2.1 Marketing margins models

12.2.2 Price transmission from farmers to consumers

12.2.3 Market structure models

Processors’ oligopoly

Processors’ oligopsony

Farmers’ cooperatives market power

Successive oligopoly and oligopsony models covering the whole supply chain

Bilateral oligopoly between cooperatives and processors and between processors and retailers

12.3 Extension: a Supply Chain Model Considering Inventories

12.3.1 Derivation of the supply of storage equation

12.3.2 Econometric estimates

12.4 Final Remarks: Limitations in Modelling Supply Chains

Notes

References

13: Linkage of a Farm Group Model to a Partial Equilibrium Model*

13.1 Introduction

13.2 Review of Existing Models

13.3 The CAPRI Approach

13.3.1 Farm types in the CAPRI-FT layer

13.3.2 Output market linkage

13.3.3 Land market linkage

13.4 Limitations

13.5 Application

13.5.1 Results

13.6 Summary and Conclusions

Notes

References

14: Conclusions: The State-of-the-art of Farm Modelling and Promising Directions

14.1 A Look Across the State-of-the-art: Why All This Development?

14.2 Fostering Farm-level Modelling

14.2.1 Changes in (agricultural) policy instruments

14.2.2 Changes in relevance and understanding of policy impact indicators

14.2.3 Key biophysical or economics processes do not aggregate linearly

14.2.4 Simultaneous development of databases, computing power and techniques

14.3 Subjective View on Current Limitations and Promising Future Directions

References

Index

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