Chapter
1 Policy Impact Assessment
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
1.3.3 Agriculture–environment interactions
1.3.4 Dynamics and structural change
1.4 What Models Do We Need to Assess Tomorrow’s Agricultural Policies?
2 Positive Mathematical Programming
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
3 Modelling Farm-level Adaptations Under External Shocks
3.2 Review of Existing Methodologies
3.3.5 Exogenous adaptation measures
3.5.1 Irish farms under climate change
3.5.2 The case of alternative farrowing housing for sows in the UK
4 Farm-level Modelling, Risk and Uncertainty
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
4.4.1 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
5 Modelling Farm-level Biosecurity Management
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
6: Modelling Farm Efficiency
6.1.2 Defining 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
Technical efficiency of Irish dairy farms: 1979–2012
6.4 Conclusions and Relevance of Research Findings to Policy Makers
7: Quantifying Agricultural Greenhouse Gas Emissions and Identifying Cost-effective Mitigation Measures
7.2 Quantifying On-farm GHGE missions
7.2.1 Moving beyond the farm gate: life cycle analysis (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
7.3.1 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
8: Moving Beyond the Farm: Representing Farms in Regional Modelling
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
8.3.1 Limitations and challenges of the regional model
Loss of farm-level accuracy
Endogeneity of prices in product and input markets
8.4 Review of Regional Modelling Applications
8.4.3 Environmental loadings from agriculture
8.5.1 Edwards Aquifer management
8.5.2 Economic and groundwater use implications of climate change in the Ogallala Aquifer region
8.6 Summary and Conclusions
9: Farm-level Microsimulation Models
9.1.1 Modelling complexity
9.2 Applications of Farm-level Microsimulation Modelling
9.2.1 Hypothetical analyses
9.2.3 Behavioural modelling
9.2.5 Impact of macroeconomic change
9.2.7 Environmental analysis
9.3 Conclusions and Future Directions
10: Scaling Up and Out: Agent-based Modelling to Include Farmer Regimes
10.2 Review of Approaches to Scaling
10.4.1 The Lunan catchment, east Scotland
10.4.2 Evaluation of regimes
10.4.3 Model verification
10.5 Summary and Conclusions
11: Catchment-level Modelling
11.2 Review of Existing Methodologies
11.2.1 Output-based integration
11.2.2 Scenario-based integration
11.2.3 Dynamic integration
11.3.1 Estimating silt costs to the downstream water users
Direct silt-related costs
Indirect silt-related costs
11.4.1 Case study of Dwangwa catchmentin central Malawi
11.4.2 Linear optimization model assumptions
Household characteristics
Payments for watershed services and land under SLM
Silt costs savings and climate variation
Silt costs savings from engaging in PWS in perspective
11.5.1 Policy implications
11.5.2 General conclusions
12: Modelling Food Supply Chains
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
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
13: Linkage of a Farm Group Model to a Partial Equilibrium Model*
13.2 Review of Existing Models
13.3.1 Farm types in the CAPRI-FT layer
13.3.2 Output market linkage
13.3.3 Land market linkage
13.6 Summary and Conclusions
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