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Minimum-Data Analysis of Technology Adoption and Adaptation to Climate Change John M. Antle

Minimum-Data Analysis of Technology Adoption and Adaptation to Climate Change John M. Antle Department of Ag Econ & Econ Montana State University. Workshop on Adaptation to Climate Change Nairobi Sept 2008. Motivation

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Minimum-Data Analysis of Technology Adoption and Adaptation to Climate Change John M. Antle

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  1. Minimum-Data Analysis of Technology Adoption and Adaptation to Climate Change John M. Antle Department of Ag Econ & Econ Montana State University Workshop on Adaptation to Climate Change Nairobi Sept 2008

  2. Motivation • Understanding technology adoption: what farms adopt where, and why? What changes or incentives are required to achieve a target adoption rate? • E.g., orange flesh sweet potato; practices that reduce off-farm externalities • Adaptation to climate change: what are impacts of climate change with present practices? What are benefits of adaptation? Will new technologies facilitate adaptation? • E.g., drought resistant varieties, more resilient crops such as sweet potato

  3. Methodological issues • Need timely, feasible analysis to inform policy decisions • Representative farm vs heterogeneous population • Technical vs economic potential • Data availability

  4. Machakos: heterogeneity leads to different activities, adoption rates, vulnerabilities

  5. Modeling Adoption Rates in Heterogeneous Populations • Farmers choose practices to max expected returns • v (p, s, z) ($/ha) • p = output & input prices, s = location, z = system 1,2 • Farmers earn v (p, s, 1) for current system • Farmers can adopt system 2 and earn • v (p, s, 2)– TC – A • where TC = transaction cost, A = other adoption costs

  6. The farmer will choose system 2 if • v (p, s, 1) < v (p, s, 2) – TC – A • The opportunity cost of switching from 1 to 2 is •  = v (p, s, 1) – v (p, s, 2) + TC + A •  adopt system 2 if < 0. • Suppose Government or NGO wants to encourage adoption by providing incentive payment PAY (e.g., to reduce negative externalities of syst 1, or encourage positive externalities of syst 2) •  adopt system 2 if < PAY. • Opportunity cost varies spatially, so at some sites farms adopt system 1 and at other sites adopt system 2

  7. Construct spatial distribution of opportunity cost

  8. PAY Derivation of adoption rate from spatial distribution of opportunity cost Case3 PAY0 Case 2 () Rate 100 Case 1

  9. PAY s = 0 A f w) ( Rate 0 Effect of the Changing the Variance of the Opportunity Cost: “representative farm” is limiting case with zero variance in opportunity cost, adoption curve is a step function

  10. Analysis of Adaptation to Climate Change • Impacts of climate change: Productivity of vulnerable crops declines more than resilient crops, e.g., maize vs sorghum, tomato vs sweet potato • PAY is amount needed to compensate for loss • Adaptation is adoption of practices that are relatively less vulnerable under the changed climate • Reduces loss due to climate change, or increases gains

  11. PES Effect of climate change: impact and adaptation Without adaptation () Rate 100 With adaptation

  12. Minimum Data Methods to Simulate Adoption Rates • (Antle and Valdivia, AJARE 2006) • How to estimate the spatial distribution of opp cost of changing practices? • Use “complete” data to estimate site-specific inprods and simulate site-specific land management decisions to construct spatial distribution of returns • MD approach: estimate mean, variance, covariance of net returns distributions using available data • Need to know mean and variance of •  = v (p, s, 1) – v (p, s, 2) + TC + A

  13. MD approach: use available data to estimate mean and variance of  • Mean: E () = E (v1) – E (v2) + TC + A • Suppose system 1 has one activity, then: • E (v1) = p11 y11 – C11 is usually observed • E (v2) = p21 y21 – C21 is estimated using Inprods* and cost data: • y21 = y11 {1+ (INP21 – INP11)/INP11} • * Inprod = inherent productivity = expected yield at a site with “typical” management • C21 is estimated using C11 and other information on changes in practices • TC and A are estimated using available data, if relevant

  14. Variance of returns: • Observation: cost of production cy where  is a constant and y is yield • Then v = py – c  (p - ) y and CV of v is equal to CV of y • Recall:  = v (p, s, 1) – v (p, s, 2) + TC + A so we know 2= 12 + 22 - 212 • Usually observe 12, can assume 1222 • 12 difficult to observe. Can assume correlation is positive and high in most cases. If 1222 = 2 then • 2 22 - 212  2 = 22(1 – 12)

  15. Most systems involve multiple activities (crops, livestock). 12 and 22 depend on variances and covariances of returns to each activity. In the MD model, we assume all correlations between activities within system 1 are equal (1), and make the same assumption for system 2 (2). • In general, incentive payments are calculated as • PAY = PES * ES • Where PES = $/unit of ES, ES = services / ha • For adoption analysis, set ES = 1, then • PAY = PES ($/ha)

  16. Conclusion: to implement MD approach we need: • Mean yields for system 1 • Either mean yields for system 2, or Inprods for each activity in each system • Output prices and cost of production for each activity • Variances (or CVs) of returns (yields) for each system • Correlation of returns to activities within each system (1 and 2) • Correlation of returns between systems 1 and 2 (12)

  17. Implementation: Data files and programs (Excel, SAS, R) Inprods and ES rate Data for system 1 Data for system 2 REGIONS TEMPL1 TEMPL2 Tradeoff file TRDMD Calculate mean and variance of opportunity cost Sample distribution of opportunity cost Adoption decision by field Aggregate to get regional adoption curve or ES supply curve

  18. Validation: Compare Detailed Models to MD Model • 3 carbon sequestration studies • Montana • Kenya • Senegal

  19. Comparison of EP and MD Models: carbon supply for Montana wheat system

  20. Comparison of EP and MD models: Carbon contract participation in Machakos, Kenya Case Study (Full model = 700 parms, MD = 75)

  21. Comparison of EP and MD models: Carbon Contract Participation in Senegal Peanut Basin

  22. Where do I get minimum data? • Regional (AEZ) data • Soils & climate • Observed yields, yield trials • Budgets for costs of production, observed prices • Farm survey data • Yields & yield variability • Mean costs of production & prices

  23. MD Examples • Adoption of new variety for an existing crop • Inprods, CV, price, cost of production, correlation • Adoption of new crop • Yield in TEMPL1, weights • Partial adoption of new variety: add new variety as new crop, set weights • Ecosystem services • ES rate, Inprods

  24. MD Examples (cont.) • Climate change • Modify inprods, adjust weights? • Adaptation • Modify inprods for base, for adapted system with climate change

  25. Climate impact & adaptation • REGIONSCC: climate only • REGIONSCM: climate with improved maize • REGIONSCP: climate with sweet potato

  26. Country Team Work Plans • System & scenario identification • Data acquisition • Team composition • Collaboration plan • Follow-up workshop • Publication

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