A New Trans-Disciplinary Approach to Regional Integrated Assessment of Climate Impact and Adaptation in Agricultural Systems John Antle & Roberto Valdivia Dept of Applied Economics Oregon State University 1 AAEA International Track Session, Minneapolis, MN July

AgMIP’s RIA approach: user-driven and forward looking Key questions: How can we do meaningful analysis of CC impact, adaptation and mitigation? (look forward, not backward?) How can we communicate to decision makers? What is meaningful? Stakeholders want to know impacts they care about => poverty, food security, health…its not just about yield or aggregate production) => What to do? What will it cost? Stakeholders = farmers, crop breeders, farm advisers, research managers, policy makers…

AgMIP’s RIA approach: Key features A protocol-based approach: rigorously documented so results can be replicated and inter-compared, and methods improved Participatory: identification of impact indicators, choice of key systems, adaptations, design of future pathways and scenarios used A trans-disciplinary, systems-based approach: must include key features of current and possible future systems, including multiple crops, inter-crops, livestock, and non-agricultural sources of income. Heterogeneity: must account for the diversity of systems, and the widely varying bio-physical and socio-economic conditions Vulnerability: must be possible to characterize the impacts on those farm households that are adversely impacted by climate change, as well as those that benefit from climate change. Key uncertainties in climate, production system and economic dimensions of the analysis must be assessed and reported so that decision makers can understand them and use them to interpret the results of the analysis.

Systems and Scales Adaptation Packages

AgMIP’s Trans-disciplinary Approach to CCIA 5

Linking Crop, Livestock and Economic Models: Importance of Heterogeneity Hypothesis: High degree of bio-physical & socio-economic heterogeneity plays a key role in assessing CC impact, vulnerability & adaptation in ag systems Most analysis averages data at level of political units such as counties, districts etc or larger E.g., in US & Kenya crop-based systems: > 80% of variance in net returns/ha in farm hh populations is WITHIN such units Effect of adaptation on reduced vulnerability to loss Can have small average gain or loss but substantial vulnerability!

Modeling Heterogeneous Impacts: the TOA-MD Model (tradeoffs.oregonstate.edu) A “parsimonious” model designed for ex ante impact assessment via simulation experiments using observational, experimental and scenario data simulates heterogeneous ag systems using the “Roy model” logic of the micro-econometrics literature Can address the “three questions” of CCIA: Q1: what is climate sensitivity of current systems? Q2: what are future climate impacts w/o adaptation? Q3: how useful are prospective adaptations in the future? what is the economic potential for adoption of alternative systems, what are their economic, environmental and social impacts? Antle, J.M., J.J. Stoorvogel and R. Valdivia New Parsimonious Simulation Methods and Tools to Assess Future Food and Environmental Security of Farm Populations. Philosophical Transactions of the Royal Society B 369:

The Three Questions of CCIA Negative impacts Positive impacts Key question for impact and adaptation: what is the counterfactual?

Representative Ag Pathways & Scenarios – Many regional economic impact assessments impose future climate and assess adaptation under current socio-economic conditions. – Key finding of earlier global studies & AgMIP regional studies is importance of future scenarios to impact & vulnerability assessments

Linking Crop, Livestock and Economic Models: Modeling Heterogeneity Random relative yield model: Yst = time-averaged farm-level yield for system s = current, future, adapted climate t = current, future Xst = simulated value of Yst For Q1: R1 = Xcf/Xcc For Q2: R2 = Xff/Xcc For Q3: R3 = Xaf/Xcc Assume: For Q1: Ycf = R1 x Ycc For Q2: Yff = R2 x Ycc For Q3: Yaf = R3 x Ycc Use site-specific soils, climate and management data with statistical or process-based crop or livestock models to estimate Xst & generate spatial distributions of projected yields for Q1, Q2 Q3.

Example: Relative wheat yield distribution and gains and losses from CC 30% gainers, 70% losers (vulnerable) TOA-MD model simulated gains and losses Heterogeneous region

AgMIP Regional RIAs: Net Economic Impact vs Vulnerability Each point = RCP 8.5 climate x crop model x region

AgMIP Regional RIAs: Importance of Future Scenarios (RAPS) Each point = RCP 8.5 climate x crop model x region

How to Communicate? => Stakeholders: need narratives (interpretation) backed up by credible analysis & data

Some Key Challenges Local-regional-global linkages across inter-related processes (disaggregation, counterfactuals, adaptation) Climate (or weather?) variability and extremes (we can model impacts, but are climate data good enough?) Weeds, pests and diseases (new AgMIP activity…) Future socio-economic pathways: technology, demographics, institutions, policy … (we need serious investment in this) Uncertainty & dimensionality: climate x bio-physical x economic

AgMIP Regional RIAs: Impacts of Climate Change in Mid-Century VariableDescription % losersPercent of losers from CC (%) Net Impact Net Impacts as a percent of farm net returns (%) Chg_FNRChange in farm net returns (%) Chg_PCIChange in per capita income (%) Chg_PovChange in poverty rate (%) Q1 Q2