Application of the geo-spatial bio-economic modeling framework, ICAE 2015

1. Global Futures and Strategic Foresight Analysis at CGIAR: Application of the geo-spatial bio-economic modeling framework to inform decision making S Nedumaran, G Sika, D Enahoro, Keith Wiebe and Cynthia Bantilan CGIAR Research Program on Policies, Institutions and Markets On behalf of the CG centers working on GFSF Bernardo Creamer, Ulrich Kleinwechter, Guy Hareau, Daniel Mason-D'Croz, Nelgen Signe, Roberto Telleria ICAE Conference 9-14 August Milan, Italy

2. Outline  Global Futures and Strategic Foresight (GFSF)  Why foresight analysis?  Framework developed in GFSF  Case studies undertaken by CG centres  ICRISAT – Evaluation of Groundnut Promising technologies  CIP – Evaluation of Potato promising technologies  CIMMYT - Impact of climate change on production and food security of maize systems in SSA  ILRI - Quantification of global livestock futures  Summary and way forward

3. CG centers partners in GFSF

4. Why Foresight Analysis? Global food economy is in a state of FLUX Growing population Rising incomes Changing diets Restrictive trade policies Climate change Natural Resource degradation Food crops used for bio-fuel Higher and more volatile food prices and increasing food and nutritional insecurity Drivers of Change

5. 0 500 1000 1500 2000 2500 3000 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 US$perMetricton Groundnuts Oil Soybeans Maize Sorghum Rice, Thai 5% Prices for Agricultural Commodities, 1971-2013 Stable and low Source: World Bank (2014) Note: Price are in real 2010 US$.

6. Foresight Analysis Reveals! 0 20000 40000 60000 80000 100000 120000 2010 2015 2020 2025 2030 2035 2040 2045 2050 '000metricton Supply Demand Demand and supply of grain legumes in Low Income Food Deficit Countries (LIFDC)* Source: IMPACT model projection Note: Grain legumes - groundnuts, chickpea, pigeonpea and soybeans; *There are 62 countries classified under LIFDC by FAO Projected population(Millions) under poverty in 2050

7. Goal of Global Futures and Strategic Foresight Increasing the yield by developing crop varieties with promising traits Increased production, reduces the prices, increase the consumption Reduce malnutrition and Poverty  To support increases in agricultural productivity and environmental sustainability by evaluating promising technologies, investments, and policy reforms  Evaluation of selected promising technologies under development in CG centers Source: Nelson et al., PNAS (2014) Modeling climate impacts on agriculture: Incorporating economic effects

8. Strategic Foresight@ICRISAT • GFP activities integrated in CRP-PIM • Multidisciplinary team created and institutionalized (14 member team) • Promising technologies were identified and prioritized for evaluation • Collaboration with other CRPs and Global Projects like AgMIP (data sharing, model enhancement, capacity building) Multi-disciplinary team @ ICRISAT •Cynthia Bantilan, Nedumaran, Kai Mausch, N Jupiter Economists •Ashok Kumar, SK Gupta, CT Hash, PM Gaur, P Janila, Ganga Rao, Jana Kholova Breeders and Physiologists •P Singh •Dakshina Murthy •Gumma Murali Krishna Crop Modelers/GIS/RS ImpactAssessment

9. Integrated Model Framework Hydrology model-WSM Climate model Source: Rosegrant et al. (2012) Crop model

10. Evaluation of Promising Technologies: Virtual Cultivars  Target of the crop improvement scientists – develop promising technologies with higher yield PotentialYield(Kg/ha) 2014 2020 Incorporating the traits in elite cultivars better root system Extractmorewater

11. Crop Model Calibration and Development of Virtual Promising Cultivars  DSSAT Crop Model  Baseline Cultivars selected - JL 24, M 335 and 55-437  Location  Anantapur and Junagadh sites in India  Samanko (Mali) and Sadore (Niger) sites in West Africa  Calibrate and validate baseline cultivars  Manipulated the genetic co –efficient of baseline cultivars and developed the virtual promising cultivars for each locations  Drought Tolerant  Heat Tolerant  Drought + Heat + yield Potential  Estimate the yield change for each technology compare to baseline cultivars in each location  Data source: Breeders yield trial data; NARS trial data  National Bureau of Soil Survey and Land Use Planning, Nagpur India; WISE soil database  India Meteorological Department (IMD); NASA website (http://power.larc.nasa.gov/)

12. 1171 1225 1270 1477 1100 1150 1200 1250 1300 1350 1400 1450 1500 JL 24 Drought Tolerance Heat Tolerance Drought + Heat tolerance + Yield potential Kg/ha 1228 1271 1246 1451 1100 1150 1200 1250 1300 1350 1400 1450 1500 JL 24 Drought Tolerance Heat Tolerance Drought + Heat tolerance + Yield potential Kg/ha Step 1: Simulated Yield of Promising Technologies of Groundnuts Source: Singh, P., Nedumaran, S., Ntare, B.R., Boote, K.J., Singh, N.P., Srinivas, K., and Bantilan, M.C.S. 2013. Potential benefits of drought and heat tolerance in groundnut for adaptation to climate change in India and West Africa. Mitigation and Adaptation Strategies for Global Change. 18% Yield under Current Climate 26% Yield under Climate change 2050 (HADCM3, A1B scenario) Location: Anantapur, India; Baseline Cultivars: JL 24

13. Step 2: Spatial Change in Groundnut Yield Baseline cultivar (Current Vs Future Climate) Promising Technology – Drought Tolerant

14. Step 3: Technology Development and Adoption Pathway Framework 2012 2018 2035 India60% Nigeria40% 20372020 Research lag Adoption lag Promising Technologies of Groundnut development Technology development Technology dissemination and adoption Outcomes and Impacts • Change in Production • Change in prices • Change in consumption • Poverty level Nedumaran et al. (2013) Target countries Target countries Production share (%) Burkina Faso 1.2 Ghana 1.62 India 12.99 Malawi 0.7 Mali 1.01 Myanmar 3.84 Niger 0.51 Nigeria 10.72 Tanzania 1.73 Uganda 0.76 Vietnam 1.84 Total 36.92

15. Potential Welfare Benefits and IRR (M US$) Technologies Net Benefits (M US$) IRR (%) Heat Tolerant 302.39 30 Drought Tolerant 784.08 38 Heat + Drought + Yield Potential 1519.76 42 Nedumaran et al. (2013) 0 50 100 150 200 250 300 350 Malawi Tanzania Uganda Burkina Faso Ghana Mali Nigeria Niger India Myanmar Vietnam ESA WCA SSEA MUS$ Heat Tolerance Drought Tolerance Heat+Drought+yield potential

16. Evaluation of improved potato varieties for SSA • Key traits • Higher yield potential • Late-blight and virus resistance • Heat tolerance • Processing quality • 30% higher yields • Nine target countries • Total investment: 9.8m US$ (4.29m NPV, 2000 constant prices) • Project duration: 12 years Source: Theisen and Thiele (2008). EthiopiaUganda Rwanda Burundi DR Congo Kenya Tanzania Mozambique Malawi

17. Welfare Benefits and IRR 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 Net welfare changes (M $) Low adoption Medium adoption High adoption 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 IRR Low adoption Medium adoption High adoption  Positive production impacts in target countries  Positive net welfare effects and high ROI in target countries  Comparable with findings from previous impact evaluations of improved varieties  Investment in improved potato varieties justified from economic point of view

18. Strategic Foresight @ CIMMYT: Impact of Climate change on Maize Production in SSA

19. GCM monthly gridded data Regional/global crop productivity under various climate models and technologies Evaluated DSSAT model DSSAT Crop Model Site/farm level simulation Site soil Daily site climate Crop Crop management Model calibration Model evaluation 27 FAO soil groups daily pixel climate Crop management Weather generator Crop per MME Evaluated DSSAT model Evaluated IMPACT model GIS GIS Projections on population and income growth Trade-offs (elasticities) on inputs, production and consumption patterns Projections on trade barriers Projected world and domestic prices Projected demand, supply and net trade Nutrition results DSSAT Spatial DSSAT IMPACT Bio-economic modelling framework

20. Changes in yield and area of maize under low N level in SSA by 2050 (a & b) and 2080 (c & d) relative to the baseline (2000) using climate projection from CSIRO and MIROC global circulation models under the A1B emission scenario Impact of Alternate Climate Scenarios on Maize

21. SSA Eastern SSA Southern SSA Central SSA Western SSA 0 10 20 30 40 CSI-A1 vs. Base2050 MIR-A1 vs. Base2050 Changein#ofpeople atriskofhunger(mil.) Caloric intake in 2050 under MIR-A1 scenario 1500 2000 2500 3000 3500 4000 Changeinpeopleatriskofhunger(mil.) -2 0 2 4 6 8 10 BUR DJIERI ETH KEN MAD MLW MOZ RWA SOM TAN UGA ZAM ZIM ANG CAM CAR CHA CON DRC EQG GAB BEN BUF GAM GHA GUIGUBIVC LIB MAL MAU Niger Nigeria SEN SLETOG Effect of Climate Change on Food Security in SSA

22. Summary Maize production in SSA  Reduction of up to 12% and 20% by 2050 and 2080, respectively  Sahel and southern Africa: reduction in maize yields due to increasing temperatures and decreasing rainfall  Highlands in eastern Africa: increase in maize production due to small changes in rainfall and increasing temperature  Food security in SSA: hardest-hit is eastern Africa; DRC in central Africa and Nigeria in western Africa Tesfaye et al., 2015

23. Strategic Foresight @ ILRI Global Livestock Futures

24. Global Livestock Futures Objective: Improve representation of livestock sector in IMPACT model to: Better account for (agro-ecological and management system) barriers to sector growth Better assess potential for sector expansion Improve capacity to simulate response, growth and recovery to shocks - including climate change Enhance model usefulness as policy assessment tool for livestock sector development

25. Original Specification Suggested Updates Supply response is relatively homogenous within countries Livestock supply disaggregated by system types (intensive/extensive) Livestock feed basket composed only of internationally-traded feeds (mostly coarse grains and meals) Pasture grasses, crop residues and occasional feeds added to livestock feeding possibilities Yield is exogenously determined, and does not respond to quantity or quality of fed rations Meat and milk yield response functions are endogenous, responding to changes in feed quantities and nutritive values Total herd size includes milk-producing and slaughtered meat animals only Total herd count includes replacement and/or follower herds in dairy and meat production Animal productivity only indirectly affected but not affected by feed availability through price effects Explicit feed-availability constraints imposed on animal productivity Source: Msangi et al., 2014 Suggested Enhancements to Livestock Sector representation in IMPACT

26. Source: Msangi et al., 2014 Baseline Projections of Meat Production to 2030 for Key Countries Baseline Projections of Milk Production to 2030 for Key Countries Baseline Results

27. Summary  More dynamic growth for meat and lamb production in China, milk in India; Brazil meat production to surpass US by 2030  Supply-side response to growing demand for livestock products is more constrained in the enhanced model  Growth in feed demand and pressure on land resources more apparent, with important implications for the more extensive production systems

28. Way forward  Evaluate the additional promising technologies (biotic stress tolerant and management options) with current GF/PIM Strategic foresight tool  Provide evidence to better targeting of technology and inform priority setting for CG centres and CRPs  Identify and collaborate with pest and diseases modelling team  Consider linking results from global models with household data for ex ante impact assessment at lower scales  Gender lens in foresight analysis and technology evaluation  Development of ‘stand alone’ module in IMPACT with enhanced representation of livestock  Test current and alternative (technology and policy) strategies for livestock sector development under a range of plausible future scenarios - including global climate change

29. Thank you!

Application of the geo-spatial bio-economic modeling framework, ICAE 2015

Global Future & Strategic Foresight Program (GFSF)

Application of the geo-spatial bio-economic modeling framework, ICAE 2015