Integrated Regional Assessment of Agricultural Systems: Lessons from AgMIP and REACCH John M. Antle Professor of Applied Economics Oregon State University AgMIP Co-PI and Regional Economics Leader 1 Presented at Coupling Economic Models with Agronomic, Hydrologic, and Bioenergy Models for Sustainable Food, Energy, and Water Systems, Iowa State University, Oct

2 Agricultural Model Inter-comparison and Improvement Project (AgMIP.org) A new global community of science: climate, water, soils, crops & livestock, economics, pests & diseases ◦More than 700 participating scientists ◦ Collaborating & supporting institutions include: ◦USDA Agricultural Research Service ◦UKAID (DFID) ◦NASA ◦USAID ◦Bill and Melinda Gates Foundation ◦National and international agricultural research centers and programs

3 AgMIP Regional Climate Change Impact Assessment Teams 5-year project, DFID funded 8 regional teams, 18 countries, ≈ 200 scientists Data, models, scenarios designed & implemented by multi-disciplinary teams & stakeholders Small-scale, mixed crop and crop-livestock systems; principal crops vary by region (maize, millet/peanut, rice, wheat) typical of “semi- subsistence agriculture”

4 REACCH - Regional Approaches to Climate Change in Pacific Northwest Agriculture 5-year project funded by USDA-NIFA University of Idaho Oregon State University Washington State University USDA-ARS scientists & students Large-scale wheat-fallow and annual cropped systems typical of “industrial commodity agriculture”

Our stakeholders: we know the climate is changing, so what can we do? Is our modeling useful? Eastern Uganda Northwest USA

6 Integrated Assessment: one scale, multiple disciplines

7 Global-Regional Integrated Assessment: multiple scales and disciplines – high complexity Valdivia, R.O., J.M. Antle, C. Rosenzweig, A.C. Ruane, J. Vervoort, M. Ashfaq, I. Hathie, S. Homann-Kee Tui, R. Mulwa, C. Nhemachena, P. Ponnusamy, H. Rasnayaka and H. Singh. (2015). Representative Agricultural Pathways and Scenarios for Regional Integrated Assessment of Climate Change Impact, Vulnerability and Adaptation. C. Rosenzweig and D. Hillel, eds. Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project Integrated Crop and Economic Assessments, Part 1. London: Imperial College Press.

8 Impact, Adaptation & Vulnerability of Ag Systems: AgMIP Regional IA Methods ( integrated-assessments-handbook/#) Antle, J. M., R.O. Valdivia, K.J. Boote, S. Janssen, J.W. Jones, C.H. Porter, C. Rosenzweig, A.C. Ruane, and P.J. Thorburn. (2015). AgMIP’s Trans-disciplinary Agricultural Systems Approach to Regional Integrated Assessment of Climate Impact, Vulnerability and Adaptation. C. Rosenzweig and D. Hillel, eds. Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project Integrated Crop and Economic Assessments, Part 1. London: Imperial College Press.

9 Lessons: 1  Need models of HETEROGENEOUS SYSTEMS, not only LU or AGGREGATE COMMODITY market models  E.g., in US ag census data approximately 80% of variation is within-county, 20% is between-county  Methods to link ag systems to market models

10 Relative yield distributions: heterogeneity and uncertainty… Source: Author and collaborators, REACCH-PNA Project

11 Lessons: 2  Need models and data capable of estimating well-defined counterfactuals and treatment effects of environmental change, policy, and other drivers of change  Impact indicator: V[technology, climate, state of world]  H = historical conditions, F = future conditions, Antle, J.M. and C.O. Stöckle Perspectives on climate impacts on crops from agronomic-economic analysis. Paper prepared for the symposium on impacts of climate change on agriculture in the Review of Environmental Economics and Policy.

12  Treatment effects relevant to science & policy stakeholders o Distinguish climate impact versus “adaptation” in historical, future conditions o Reduced-form econometric, LU models only represent climate impact + adaptation in current (historical) world o “Hybrid structural models” that satisfy “Marshak’s Maxim” can estimate all relevant treatment effects o Need methods for future scenarios (beyond “Shared socio-economic pathways” to ag & region-specific) Antle, J.M. and C.O. Stöckle Perspectives on climate impacts on crops from agronomic-economic analysis. Paper prepared for the symposium on impacts of climate change on agriculture in the Review of Environmental Economics and Policy.

13 Example: estimating the counterfactual for “out-of-sample” test of a hybrid model: adoption of annual cropping in the wheat-fallow region (CropSyst and TOA-MD models) Antle and Stockle, 2015 REEP (in review) Predicted adoption of annual cropping in wheat-fallow area = 20% actual adoption rate = 23%

14 Lessons: 3  AgMIP: need PROTOCOL-BASED approach  for model inter-comparison & improvement  for transparency, reproducibility in integrated assessment  addressing SCENARIO and MODEL UNCERTAINTY  to evaluate out-of-sample predictions  multiple models and model ensembles: ONE BIG MODEL IS NOT THE ANSWER (see CMIP … )  But …. how do you do multi-disciplinary, multi-scale ensembles?

15 AgMIP modeling teams: model inter-comparison to understand & reduce uncertainty in crop models Asseng, S. et al. Uncertainty in Simulating Wheat Yields Under Climate Change. Nature Climate Change 2013.

16 AgMIP global modeling team: economic model and scenario uncertainty (what about regional economic models?) Projected Changes in Commodity Prices in 2050 without Climate Change WHT = wheat, CGR = coarse grains, RIC = rice, OSD = oil seeds, RUM = ruminant animal products Projections from 9 models, multiple scenarios, no CO2 effects on crops (Nelson et al. PNAS 2014).

17 Lessons: 4  To evaluate well-being we need to model FOOD SYSTEMS  IPCC AR5, forthcoming USDA assessment report on CC & global food security  beyond food security to health, nutrition Source: IPCC AR-5, WGII, Ch 7.

18 Lessons: 5  Need NextGen data, models and knowledge products AgMIP NextGen study groundwork-for-the-next-generation-of-agricultural-system-models/  user access to model products: e.g., dashboards, visualization  open source, modular, inter-operable model components  new ICT tools & data systems Antle, Capalbo and Houston, “Tapping Big Data…”Choices Sept 2015

19 Conclusion: lots to do! Thanks for your attention… Antle, Capalbo and Houston, “Tapping Big Data…”Choices Sept 2015