New Methods to Assess Climate Change Impacts and Adaptation for Poor Agricultural Households John M. Antle Roberto Valdivia Agricultural and Resource Economics Oregon State University Seminar presented at the UK Department for International Development, London, May

Motivation Research -- and common sense! -- suggest that poor agricultural households are among the most vulnerable to climate change and face some of the greatest adaptation challenges Rural households and agricultural systems are heterogeneous, implying CC impacts – and value of adaptation strategies -- will vary within and between these populations Farmers’ choice among adaptation options involves self-selection that must be taken into account for accurate representation of adaptation options Impacts of climate change and adaptation depend critically on future socio-economic conditions

Modeling impact and adaptation to climate change: policy analysis of the “third kind” The challenge: model the “counterfactual” of future technology, climate and socio- economic conditions. Much more challenging than ex post impact assessment or extrapolation! System 2 System 1 time Indicator

4 Example: Impact of CC on Subsistence, Dairy and Irrigated Farms in Vihiga and Machakos Districts, Kenya Claessens, Antle, Stoorvogel, Valdivia, Thornton & Herrero A method for evaluating climate change adaptation strategies for small- scale farmers using survey, experimental and modeled data. Agricultural Systems (in press). RAP1 = positive development pathway, low challenges to adaptation RAP2 = adverse development pathway, high challenges to adaptation

Towards improved methods… The methods used to assess CC impact and adaptation to date are not well suited to assess CC impacts and adaptation potential ◦ Averaged (aggregated) climate, technical and socio-economic data -- and corresponding “representative farm” or aggregate models -- fail to represent heterogeneity and technological detail essential to analysis of adaptation ◦ Analysis of impacts of future climate done with current socio-economic system and technology ◦ Limited measures of economic impact (land values, gross returns), lack of distributional impacts. Tradeoff Analysis for Multi-Dimensional Impact Assessment (TOA-MD): a micro-simulation approach to multi-dimensional impact assessment ◦ A parsimonious, generic framework to analyze impacts of CC, adaptation in heterogeneous populations of farm households Representative Agricultural Pathways (RAPs): A systematic approach to scenario design (under development via AgMIP) ◦ Ag-specific scenarios building on and linked to RCPs and SSPs

What is the TOA-MD Model? TOA-MD is a unique simulation tool for multi-dimensional impact assessment ◦ based on a statistical description of a heterogeneous farm population ◦ simulates impacts of changes in:  technology and socio-economic conditions  environmental conditions such as climate  policy interventions such as Payments for Ecosystem Services Global registered users

What is the TOA-MD Model? TOA-MD is designed to simulate experiments for a population of farms using a “base” production system (System 1), and an alternative System 2 TOA-MD is designed to utilize the available data to attain the best possible approximation, given the available time and other resources available to conduct the analysis ◦ can be used for ex post and ex ante analysis ◦ an alternative to econometric models that require large panel datasets TOA-MD is designed to facilitate analysis of the inevitable uncertainties associated with impact assessment through sensitivity analysis. ◦ Can use preliminary or “minimum data,” provide guidance for efficient collection of additional data when needed Software with documentation in SAS and Excel, available to registered users at tradeoffs.oregonstate.edu ◦ Self-guided course and training workshops

Using TOA-MD to Assess Climate Impacts and Adaptation Step 1: Design RAPs and scenarios ◦ technical, economic, social, policy pathways linked to global SSPs Step 2: Identify and characterize base system, adapted system(s) Step 3: Quantify impacts of CC on base and adapted system(s) Step 4: Simulate impacts without adaptation ◦ impacts on farm net returns (“losers” and “gainers” from climate change) ◦ impacts on other economic (e.g., poverty) or non-economic (e.g., health, environment) indicators Step 5: Simulate impacts with adaptation ◦ gains from adaptation ◦ economic and non-economic indicators

Steps 1-3: RAPs, climate and systems: Vihiga District, Kenya RAPs storylines provide a framework in which qualitative information can be translated into model parameters ◦ how to make this process more systematic and transparent? Climate data & models simulate productivity impacts of climate change Farmers & scientists evaluate adaptation options

Step 4: Using TOA-MD to Simulate CC Impacts without adaptation TOA-MD can simulate various “experiments” for climate impact assessment. To evaluate adaptation investments we consider: Costs of CC: impacts of climate change without adaptation ◦ System 1 = base climate, base technology ◦ System 2 = changed climate, base technology Benefits of Adaptation: adapted technology with climate change ◦ System 1 = changed climate, base technology ◦ System 2 = changed climate, adapted technology These can be done for alternative RAPs First we consider impacts without adaptation, then adaptation

CC without adaptation case: ◦ 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 There is a distribution of  in the farm population ◦ “Every farm has its  ” ◦  =  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 Using TOA-MD to Quantify Economic Impacts of CC

TOA-MD approach: modeling systems used by heterogeneous populations Systems are being used in heterogeneous populations A system is defined in terms of household, crop, livestock and aquaculture sub-systems

(ω)(ω) 0 Map of a heterogeneous region Distribution of gains and losses due to CC  = v 1 – v 2 = losses from CC v 1 = present income v 2 = future income Losses  > 0 Gains  < 0  = losses

()()  100 The areas under the adoption curve measure economic gains and losses from climate change r(2) Gains Losses Gains  < 0 Losses  > 0 % gainers % losers

Define: 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,  R = std dev of R CC = climate perturbed yields = R x A Assume:R =  R +  R ,   i.i.d.(0,1) Then:  2 =  R  1  2 2 =  R 2   R 2 (   1 2 )  12 =  R  1 /  2 A “Proportional Relative Yield” Model to Link Crop Model Simulations to TOA-MD Note: most econ models just use the mean, not the variance or correlation!

Representing heterogeneous productivity impacts of CC Relative yield concept: R = future yield/present yield Future yield = R x current expected yield DSSAT maize yield simulations for 45 farms in Machakos, present climate vs 2030s, current management

Climate change impacts in Vihiga and Machakos Districts, Kenya

Step 5: Impacts with Adaptation Improved maize Dual-purpose sweet potato

(ω)(ω) 0 Map of a heterogeneous region Adoption of adapted technology  = v 1 – v 2 = opportunity cost v 1 = base tech, future climate v 2 = adapted tech, future climate Non-adopters  > 0 Adopters  < 0 

()()  100 Derivation of adoption rate from spatial distribution of opportunity cost with adoption threshold a = 0 ω < 0 Predicted adoption rate r(2) ω > 0

Adoption rates of adapted technologies

CC no adaptation Current climate CC impacts with adaptation: improved maize and dual-purpose sweet potato, net returns Improved maize Dual purpose sweet potato

Gains from adoption of improved maize CC no adaptation Current climate Adoption rate of adapted technology Adopters Non-adopters

Gains from adoption of dpsp CC no adaptation Current climate Adoption rate of adapted technology Adopters Non-adopters

CC no adaptation CC impacts with adaptation: improved maize and dual-purpose sweet potato, poverty Current climate Improved maize DPSP

Conclusions TOA-MD is a unique simulation tool for multi-dimensional impact assessment of agricultural systems designed which incorporates: ◦ heterogeneity of agricultural systems ◦ effects of self-selection on impacts ◦ socio-economic scenarios (qualitative pathway and quantitative scenarios) TOA-MD is designed for ◦ climate impact assessment and adaptation analysis ◦ technology adoption and impact assessment ◦ payments for ecosystem services and other policy interventions TOA-MD provides a generic modeling framework designed to be used by multi-disciplinary research teams ◦ software, documetation and training available from the TOA Team

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