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Regional Impact Assessment AgMIP SSA Kickoff Workshop John Antle AgMIP Regional Econ Team Leader 1 Accra, Ghana Sept 10-14 2012.

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Presentation on theme: "Regional Impact Assessment AgMIP SSA Kickoff Workshop John Antle AgMIP Regional Econ Team Leader 1 Accra, Ghana Sept 10-14 2012."— Presentation transcript:

1 Regional Impact Assessment AgMIP SSA Kickoff Workshop John Antle AgMIP Regional Econ Team Leader 1 Accra, Ghana Sept

2 2 AgMIP Economics Protocols

3 3 Crop Models Aggregation Global Econ Models Climate Models RCPs & SSPs Aggregate Outputs Equilibrium Prices Regional and Global Model Intercomparisons and Impact Assessments RAPs Regional Econ Models AgMIP IA Framework

4 RCPs, SSPs and RAPs Representative Ag Pathways economic & social development storylines agricultural technology trends prices and costs of production ag, conservation, other policy

5 TOA-MD: multi-dimensional assessment of CC impacts & adaptation Systems are being used in heterogeneous populations A system is defined in terms of household, crop, livestock and pond sub-systems Economic, environmental and social indicators Why use TOA? What about other regional models?

6 There are two parts of TOA-MD simulations: First, the model simulates the proportion of farms that would adopt a new system (system 2), and the proportion that would continue to use the “base” system (system 1) In CC impact assessment, “adopters” are those who gain from CC, “non-adopters” as those who lose from CC Second, based on the adoption rate of the system 2, the TOA-MD model simulates selected economic, environmental and social impact indicators for adopters, non-adopters and the entire population. Farm income; poverty; soil nutrients and SOM; food security; nutrition; health.

7 This is a complex challenge! (Climate) x (crops + livestock) x (socio-econ factors) To make IA manageable, we carry out different types of simulation experiments – reference scenarios for model evaluation, validation, intercomparison – sensitivity analysis, varying some parameters while holding others constant – “pathway” analysis to explore the range of possible future states of the world IA simulations involve multiple dimensions: – climate (base, future) – production system (current systems, adapted systems) – policy (mitigation, other) – socio-economic conditions (prices, costs of production, farm size, nutrition, etc.) Let us now fix policy and socio-economic parameters, and consider IA accounting for climate and production system changes – then we can re-introduce those dimensions, i.e., replicate the analysis with those factors changed The Climate Impact Assessment Challenge

8 We can simulate various “experiments” for climate impact assessment, depending on the type of modeling approach and objectives of the analysis: Climate change impact without adaptation – System 1 = base climate, base technology – System 2 = changed climate, base technology Climate change impact with adaptation (“standard” analysis) – System 1 = base climate, base technology – System 2 = changed climate, adapted technology Adoption of adapted technology with climate change: – System 1 = changed climate, base technology – System 2 = changed climate, adapted technology Simulation Experiments for Impact Assessment

9 Consider the case of CC without adaptation: – system 1 = base climate, base technology – system 2 = changed climate, base technology  = v 1 – v 2 measures the difference in income with the base and changed climates –  > 0  CC causes a loss –  < 0  CC causes a gain So we need to know the spatial distribution of  :   =  1 -  2   2 =    1  2  12 We observe  1 and  1 2, but not  2,  2 2 or  12, so we use climate data + crop models or statistical models to estimate them Example: Using TOA-MD to Quantify Economic Impacts of Climate Change

10 Define: Y 1 has mean  1 and variance  1 2 Assume: Y 2 = b Y 1, b = Y 2 /Y 1 =  b +  b ,   i.i.d.(0,1) (true?) Two cases:matched vs un-matched data (observed & simulated) Matched: Y 2 = b Y 1 Un-matched:mean of Y 2 is  2 =  b  1  2 2 =  b 2   b 2 (   1 2 )  12 =  b  1 /  2 A Random Proportional Yield Model to Construct System 2

11 Goal: use observed data from system 1 plus crop simulations to project yield distributions for system 2 A = actual crop yield, B = simulated crop yield with current climate, C = simulated crop yield with changed climate, R = C/B,  R = mean of R etc. Sources of variation: soils, weather management – how to incorporate management variation? Mean bias:  A >  Y – biases in R = C/B causes a bias in estimate of  R – note  R   C /  B Variance bias:  B and  C   A causes bias in  R (var of a ratio) Role of Crop Models in CC Impact Assessment

12 We will use the TOA-MD model setup from Claessens et al. (2012 Ag Systems) to simulate impacts of CC without adaptation on the Machakos farming system: maize: using the Crop Model Team estimates of climate impacts on yields beans, mixed subsistence, dairy: 20% average reduction in productivity, no change in variance of net returns irrigated vegetables: no change in mean or variance Example: Maize Yields in Machakos, Kenya

13 Results: Observed and DSSAT Crop Yields Claessens et al use data for Machakos DSSAT simulations from Thornton et al Ag Systems which predicted R = 0.74.

14 Machakos production activities and system characterization under climate change (Claessens et al Ag Systems) CC without adaptation

15 Sensitivity Analysis: Mean Relative Maize Yield

16 Sensitivity Analysis: Between-System Correlation

17 Impacts by Strata and Aggregated Note: mean relative maize yield = 0.79, between system correlation = 0.9 Stratum 1 = subsistence farms, no dairy or irrigation Stratum 2 = mixed crop-livestock with dairy Stratum 3 = irrigated veges and mixed crop-livestock

18 CC Impacts for Socio-Economic Scenarios (RAPs) with Low (1) and High (2) Challenges to Adaptation RAP1 = low challenges to adaptation; more commercially-oriented farms with 50% more land allocated to maize, mean relative maize yield = 1, net returns SD reduced 20%, higher maize and dairy prices, 20% increase in farm size, 50% increase in off-farm income RAP2 = high challenges to adaptation; farms maintain subsistence orientation with minimal adaptation to CC, higher maize and dairy prices, 20% increase in production cost, 20% reduction in farm size

19 Goals for the Workshop – Identify & describe regional systems – Identify regional data & issues – Implement climate-crop-TOA-MD applications for Machakos case study – Economists: review TOA-MD BLM, prepare regional case studies – Plan for RAPs workshops/design Implement IA for regional case studies Prepare strategy for full regional implementation


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