Tradeoff Analysis: From Science to Policy John M. Antle Department of Ag Econ & Econ

1. Tradeoff Analysis: From Science to Policy John M. Antle Department of Ag Econ & Econ Montana State University

2. The Challenge: Policy-Relevant Science • How can we link relevant agricultural, environmental and economic sciences to support informed policy decision making? • E.g., do we know what policies will reduce poverty and encourage adoption of more sustainable practices in the Machakos region? • Ag Scientists: improve crop varieties and management • Environmentalists: need LISA • Economists: need to “get prices right”

3. The Challenge: Policy-Relevant Science • The TOA Approach: Agriculture as a complex system… • interconnected physical, biological and human systems varying over space and time • - the role of heterogeneity in relevant populations • the fallacy of the “representative unit” • - the role of human decision making • - the role of system dynamics and nonlinearities • - relevant scales of analysis to support policy decisions

4. Heterogeneity: Nutrient Depletion and Net Returns in Machakos Variation within and between systems…

5. Human Behavior: Mean versus coefficient of variation of net returns by Montana sub-MLRA, for climate change (CC) and CO2 fertilization scenarios with (A) and without (N) adaptation. (Source: Antle et al., Climatic Change, 2004).

6. Nonlinearities: The effect of differences in the thickness of the fertile A-horizon on the dry matter production of potatoes as simulated with the DSSAT crop model in the northern Andean region of Ecuador.

7. Complexity:The temporal dynamics in carbofuran leaching for 4 different fields as a result of tillage erosion and management changes in the northern Andean region of Ecuador. (Source: Antle and Stoorvogel, Environment and Development Economics, in press).

8. Designing and Implementing Policy-Relevant Science How is it done? Coordinated disciplinary research. How is it implemented: Tradeoff Analysis. • Tradeoff Analysis is a process that can be used to: • set research priorities according to sustainability criteria • support policy decision making • use quantitative analysis tools to assess the sustainability of agricultural production systems.

9. Research priority setting Project design & implementation Communicate to stakeholders Tradeoff analysis process • Public stakeholders • Policy makers • Scientists It’s not a linear process… e.g. NUTMON • Identify sustainability criteria • Formulate hypotheses as potential tradeoffs • Identify disciplines for research project • Identify models and data needs define units of analysis • Collect data and implement disciplinary research

10. TOA is based on an integrated assessment approach to modeling agricultural production systems, using spatially referenced data and coupled disciplinary models.

11. Implementing the TOA Approach: the TOA Software The Tradeoff Analysis model is a tool to model agricultural production systems by integrating spatial data and disciplinary simulation models. It helps scientific teams to quantify and visualize tradeoffs between key indicators under alternative policy, technology and environmental scenarios of interest to policy decision makers and other stakeholders.

12. Example: Assessing Impacts of Policy and Technology Options on the Sustainability of the Machakos Production System Poverty Nutrient Dep Define a tradeoff curve by varying a price (e.g., maize price) for a given technology and policy environment. What is the form of the tradeoff?

13. Factors Affecting Slope of Tradeoff Curve: • Productivity of each system at each site • Nutrient balance of each system at each site • Effects of maize price on farmers’ choice of system at each site (extensive margin) • Effects of maize price on farmers’ choice of management at each site (intensive margin) • Spatial distribution of systems, prices

14. Technology and Policy Scenarios: Manure Management, Fertilizer Prices Poverty Nutrient Dep How do these scenarios shift the tradeoff curve? Do curves differ spatially?

15. Machakos: Base Technology and Prices, Individual Farms

16. Base Technology and Prices, Aggregated by Village

17. Aggregated by Tradeoff Point and Village Base Technology and Prices, Aggregated by Village

18. Aggregated by Tradeoff Point

19. Aggregated by Tradeoff Point with Alternative Policy and Technology Scenarios

20. Conclusions: • TOA is a tool that can integrate data and modeling tools to support informed policy decision making • The challenges: • Make the tools available to clients. • Create a demand for better information. • Improve the tools: • lower cost of adoption and use • expand applicability

22. Process for Transfer of TOA Tools to Users: • Informing potential clients (web sites, etc) • Training (workshops, on-line course) • Collaborative agreements with clients • Use by client staff with TOA support • Follow-up to assess strengths and weaknesses

23. Key Issue: High adoption (training) and implementation costs (data) • Data • Soils and climate • Economic: farm surveys • Model complexity (training) • DSSAT models • Economic models • Environmental models

24. Solutions • Data • Soils and climate: down-scaling techniques • Economic: minimum data approach • Linkages to existing data: NUTMON • Model complexity • Bio-physical: landscape-scale empirical models • Economic: minimum data approach

25. Experience • Downscaling & linkages: Peru, Senegal, Kenya • soil & climate data • adaptation of existing farm survey data • Kenya: complex model implemented in 3 months with NUTMON data, but model complexity remains • Minimum data: Panama • simple model implemented with 1 week training, 1 month data collection & model development • but limited applicability

26. Implications • Optimal strategy for institutionalization • utilize minimum data approach for training and initial applications • develop more detailed applications if needed as clients acquire capability, data

27. Conclusions • TOA successfully implemented as an operational tool applied to various policy problems • environmental & human health impacts of pesticide use (Ecuador) • terracing and related conservation investments (Peru, Senegal) • soil carbon sequestration (USA, Peru, Senegal, Kenya) • nutrient depletion (Senegal, Kenya)

28. Conclusions (cont.) • Adoption by national and international institutions is in progress • Development of downscaling & minimum data methods will lower adoption costs • Further experience needed to fully assess impact • But…note methodological issues to be confronted in assessing impact of policy research (see Pardey and Smith, IFPRI, 2004)