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# Valdivia toa md-modeling_workshopamsterdam_2012-04-23

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Presentation from the CCAFS Farm-household Modeling workshop - Amsterdam, 23-35 April 2012

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### Valdivia toa md-modeling_workshopamsterdam_2012-04-23

1. 1. TOA-MD: Tradeoffs Analysis forMultidimensional Impact Assessment Roberto O.Valdivia and John M. Antle CCAFS Modeling Workshop Amsterdam, The Netherlands April, 2012
2. 2. What is the TOA-MD Model?The TOA-MD Model is a unique simulation tool for multi-dimensional impactassessment that uses a statistical description of a heterogeneous farm populationto simulate the adoption and impacts of a new technology or a change inenvironmental conditions.TOA-MD is designed to simulate what would be observed if it were possible toconduct a controlled experiment. In this experiment, a population of farms isoffered the choice of continuing to use the current or “base” production system(System 1), or choosing to adopt a new system (System 2).In fact it is never possible to carry out such ideal experiments, so TOA-MD isdesigned to utilize the available data to attain the best possible approximation,given the available time and other resources available to conduct the analysis.Additionally, TOA-MD is designed to facilitate analysis of the inevitableuncertainties associated with impact assessment.
3. 3. TOA-MD approach: modeling systems used by heterogeneous populations A system is defined in terms of household, crop, livestock and aquaculture sub-systems Systems are being used in heterogeneous populations
4. 4. (ω)Opportunity cost, system choice and adoption Opportunity cost = v1 – v2 follows distribution ( ) v1 = returns to system 1 V2 = returns to system 2 System 2: < 0 System 1: > 0 (adopters) (non-adopters) 0 opportunity cost Map of a heterogeneous region
5. 5. A useful adaptation shifts thedistribution of opportunity cost and the adoption curve, increasing gains and reducing The difference between the losses, to give a net gain from curves is the gain from adaptation when all farms adaptation use the adapted technology ( ) r(2) 100 Adoption rate
6. 6. Adoption, Outcome Distributions and ImpactIndicators  Outcome distributions are associated with system choice ◦ Farms select themselves into “non-adopter” and “adopter” sub- populations, generating corresponding outcome distributions for these sub-populations  Impact indicators are based on system choice and outcome distributions ◦ TOA-MD produces mean indicators and threshold-based indicators  Analysis shows that impacts depend on the correlations between adoption (opportunity cost) and outcomes ◦ Many impact assessments ignore correlations ◦ Yet these correlations are often important for accurate impact assessment!
7. 7. Adoption and outcome distributions (z|1) System 1 before adoption: 25% > threshold r(1,a)% non- Outcome z adopters r(2,a)% adopters(z|1,a) (z|2,a)System 1: 20% > (z|a) System 2: 90% > Entire Population with adoption: 55% >
8. 8. Components of the ModelDesign Population (Strata) System characterization Impact indicator designDataOpportunity cost distribution Outcome distributionsSimulation Indicators and Adoption rate Tradeoffs
9. 9. Types of applicationTECHNOLOGY ADOPTION AND IMPACT ASSESSMENTThe TOA-MD allows users to simulate technology adoption (i.e. adoption rate)under a variety of conditions defined by the user. The TOA-MD has thecapability of simulate impacts of technology adoption using statisticalrelationships between technology adoption and environmental, economic andsocial outcomes. Impacts are defined as population means or as the proportionof the population above or below a threshold (e.g. poverty line). Examples oftechnology adoption applications are:• Introduction of new crop varieties• Crop and livestock management• Soil conservation & agroforestry• Integrated agriculture – aquaculture
10. 10. Types of application, cont.ECOSYSTEM SERVICES SUPPLY AND PAYMENTSThe TOA-MD can simulate supply curves for ecosystem services associatedwith agricultural systems and payments schemes. Examples of theseapplications are:Soil carbon sequestration and GWPWater quality and quantityBiodiversityENVIRONMENTAL CHANGEThe TOA-MD allows users to assess impacts of any exogenousenvironmental change such as climate change on population of farms.Examples of these applications are:Simulate impacts of and adaption to climate changeChanges in water quantity and quality
11. 11. Application Impacts Economic (e.g. income based Technology Adoption poverty rate, farm income, other poverty indicators) cv Ecosystemcv services Social (e.g. food security indicators, , health) Environmental change Environmental (e.g. soil depletion, water quality)Recent applications- Preliminary Economic, Environmental and Social Impact Assessment of the EADD Project in Kenya using Minimum-Data Tradeoff Analysis. Gates Foundation, ILRI- Integrated Agriculture-Aquaculture in Malawi. –USAID/AQCRSP- IFAD Projects: Ghana, Bangladesh, Malawi - World Fish Center- Climate change and adaptation : AgMIP- Livelihood Strategies and Adoption of Endemic Ruminant Livestock Breeds, ILRI- Climate change: Kenya (Claessens et al, 2012), CIP-ICRISAT
12. 12. Final remarksThe TOA-MD can: Simulate technology adoption (estimate an adoption rate) under avariety of conditions defined by the user Assess economic, environmental and social impacts of technologyadoption, using population mean and threshold indicators Simulate supply curves for ecosystem services associated withagricultural systems Assess impacts of environmental change, such as climate change,with or without adaptationTraining in use of the model, and the model software are availablefrom the TOA Team.
13. 13. Key PublicationsClaessens, L., J.M. Antle, J.J. Stoorvogel, R.O. Valdivia, P.K. Thornton, and M. Herrero. 2012. “A minimum-data approach for agriculturalsystem level assessment of climate change adaptation strategies in resource-poor countries.” Agricultural Systems, Forthcoming.Antle, J.M. 2011. “Parsimonious Multi-Dimensional Impact Assessment.” American Journal of Agricultural Economics.Antle J.M. and R.O. Valdivia. "Methods for Assessing Economic, Environmental and Social Impacts of Aquaculture Technology: IntegratedAgriculture-Aquaculture in Malawi.” 9th Annual Fisheries and Aquaculture Forum, Shanghai Ocean University, April 22 2011Antle, J.M., B. Diagana, J.J. Stoorvogel and R.O. Valdivia. 2010. “Minimum-Data Analysis of Ecosystem Service Supply in Semi-SubsistenceAgricultural Systems: Evidence from Kenya and Senegal.” Australian Journal of Agricultural and Resource Economics 54:601-617.Claessens, L., J.J. Stoorvogel, and J.M. Antle. 2009. “Economic viability of adopting dual-purpose sweetpotato in Vihiga district, WesternKenya: a minimum data approach. ” Agricultural Systems 99:13-22.Nalukenge, I., J.M. Antle, and J.J. Stoorvogel. (2009). “Assessing the Feasibility of Wetlands Conservation Using Payments for EcosystemServices in Pallisa, Uganda.” In Payments for Environmental Services in Agricultural Landscapes . Ed. L. Lipper, T. Sakuyama, R. Stringer and D.Zilberman. Springer Publishing.Smart, F. 2009. Minimum-Data Analysis of Ecosystem Service Supply with Risk Averse Decision Makers. Ms. Thesis, Montana State University –Bozeman.Immerzeel, W., J. Stoorvogel and J. Antle. 2007. "Can Payments for Ecosystem Services Secure the Water Tower of Tibet?" AgriculturalSystems 96:52-63.Antle, J.M. and J.J. Stoorvogel. 2006. "Predicting the Supply of Ecosystem Services from Agriculture." American Journal of AgriculturalEconomics 88(5):1174-1180.Antle, J.M., Valdivia, R. 2006. “Modelling the supply of ecosystem services agriculture: a minimum-data approach.” Australian Journal ofAgricultural and Resource Economics 50: 1–15.