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CCAFS Science Meeting A.2 Jerry Nelson - AgMIP
 

CCAFS Science Meeting A.2 Jerry Nelson - AgMIP

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CCAFS Science Meeting presentation by Gerald Nelson (Senior Research Fellow , IFPRI) - "What is AgMIP?"

CCAFS Science Meeting presentation by Gerald Nelson (Senior Research Fellow , IFPRI) - "What is AgMIP?"

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    CCAFS Science Meeting A.2 Jerry Nelson - AgMIP CCAFS Science Meeting A.2 Jerry Nelson - AgMIP Presentation Transcript

    • What is AgMIP?CCAFS Science Meeting May 2, 2011 1
    • Why AgMIP?• Agricultural risks growing, including climate change• Consistent approach needed to enable agricultural sector analysis across relevant scales and disciplines• Long-term process lacking for rigorous agricultural model testing, improvement, and assessment 2
    • AgMIP Objectives• Improve scientific and adaptive capacity of major agricultural regions in developing and developed world• Collaborate with regional experts in agronomy, economics, and climate to build strong basis for applied simulations addressing key regional questions• Develop framework to identify and prioritize regional adaptation strategies• Incorporate crop and agricultural trade model improvements in coordinated regional and global assessments of future climate conditions• Include multiple models, scenarios, locations, crops and participants to explore uncertainty and the impact of methodological choices• Link to key on-going efforts – CCAFS, Global Futures, MOSAICC, National Adaptation Plans 3
    • AgMIP Two-Track Science Approach Data at Sentinel Sites Platinum Gold Silver Track 1: Model Improvement and Intercomparison Track 2: Climate Change Multi-Model AssessmentCross-Cutting Themes: Uncertainty, Aggregation Across Scales*, Representative Agricultural Pathways
    • AgMIP Regions 45˚ 0˚ -45˚ -90˚ 0˚ 90˚Benefits include:- Improved capacity for climate, crop, and economic modeling to identify and prioritize adaptation strategies- Consistent protocols and scenarios- Improved regional assessments of climate impacts- Facilitated transdisciplinary collaboration and active partnerships- Contributions to National Adaptation Plans
    • Crop Model Pilot Activities in AgMIIP Crop Modeling Coordinators K. J. Boote, Univ. of Florida Peter Thorburn, CSIRO, Australia
    • Crop Modeling Team Goal• To evaluate different crop models – for accuracy of response to climatic, CO2, and other growth and management factors – so there is confidence in the ability of models to predict global change effects and make consistent scenario-based projections of future crop production for economic analysis. Learn from intercomparisons and improve the crop models. 2nd I in AgMIP is “Improvement”.
    • Crop Modeling Team Activities• Activity 1 – Inter-compare crop models for methods and accuracy of predicting response to variety of drivers• Activity 2 – Conduct uncertainty pilot analyses across an ensemble of models• Want standardized protocols across crops. – Wheat “uncertainty” (Asseng, Ewert)* – Maize “uncertainty” (Bassu, Durand, Lizaso, Boote)* – Sugarcane “uncertainty” (Thorburn, Marin, Singels)* – Rice “uncertainty” (Bouman, Tao, Hasegawa, Zhu, Singh, Yin)* – New teams (sorghum (Rao), peanut (Singh), potato (Quiroz)) *Already at work
    • Accomplishments Crop Modeling Team AgMIP-South America Workshop• Calibrated for two Brazilian sites – three maize models (CERES-Maize, APSIM, & STICS) – two rice models (APSIM-ORZYA, and CERES-Rice)• accounting for soils, cultivar, & management• Used time-series and end-of-season data
    • Accomplishments Crop Modeling Team AgMIP-South America Workshop• Conducted climate change uncertainty analyses with three maize and two rice calibrated crop models – Mean temperature (Tmax & Tmin), (-3, 0, +3, + 6, +9 C). – CO2 levels (360, 450, 540, 630, & 720 ppm) – Rainfall (-30, 0, +30%) – N fertilizer (0, 25, 50, 100, 150% of reference N)• Simulated baseline 30 years and one future scenario!• Compare how crop biomass, LAI, grain yield, grain number, N accumulation, seasonal T and E respond to these factors across the different crop models.
    • Sensitivity analyses examples from AgMIP Workshop, Campinas, Brazil, August 2011 Grain Yield and Biomass Response of DSSAT, APSIM, & STIC maize models to temperature CERES STICS APSIM 11
    • Sensitivity analyses examples from AgMIP Workshop, Campinas, Brazil, August 2011Days to maturity and ET of DSSAT, APSIM, & STIC maize models in response to temperature. ET affected by life cycle. 12
    • Sensitivity analyses Yield Yieldexamples from 1.5 1.5AgMIP Workshop a) b)Campinas, Brazil Relative Yield Relative Yield 1.0 1.0August 2011 0.5 0.5 APSIM APSIM Upland Rice Upland Rice 0.0 0.0 DSSAT DSSAT -2 0 2 4 6 8 400 500 600 700Yield Response of Temperatura CO2 levelAPSIM-ORYZAand CERES-Riceto temperature, Yield YieldCO2, rainfall, and 1.5 1.5 c) d)N fertilization Relative Yield Relative YieldAlex Heinemann, 1.0 1.0Brazil, Aug 2011 0.5 0.5APSIM APSIM Upland Rice APSIM Upland Rice 0.0 0.0 DSSAT DSSATCERES -30 -20 -10 0 10 20 30 0 50 100 150 Precipitation Variation N Levels
    • DOYMaturity DOYMaturity 1.5 1.5Maturity Response r) s)of APSIM-ORYZA Relative DOYMaturity Relative DOYMaturityand CERES-Rice 1.0 1.0to temperature,CO2, rainfall, and 0.5 0.5N fertilizationAlex Heinemann, APSIM Upland Rice APSIM Upland Rice 0.0 0.0 DSSAT DSSATBrazil, Aug 2011 -2 0 2 4 6 8 400 500 600 700 Temperatura CO2 levelAPSIMCERES DOYMaturity DOYMaturity 1.5 1.5 t) u) Relative DOYMaturity Relative DOYMaturity 1.0 1.0 0.5 0.5 APSIM APSIM Upland Rice Upland Rice 0.0 0.0 DSSAT DSSAT -30 -20 -10 0 10 20 30 0 50 100 150 Precipitation Variation N Levels
    • BIOMASS BIOMASS 1.5 1.5Biomass Response i) j)of APSIM-ORYZA Relative BIOMASS Relative BIOMASSand CERES-Rice to 1.0 1.0temperature, CO2,rainfall, and N 0.5 0.5fertilizationAlex Heinemann, APSIM Upland Rice APSIM Upland Rice 0.0 0.0 DSSAT DSSATBrazil, Aug 2011 -2 0 2 4 6 8 400 500 600 700 Temperatura CO2 levelAPSIMCERES BIOMASS BIOMASS 1.5 1.5 k) l) Relative BIOMASS Relative BIOMASS 1.0 1.0 0.5 0.5 APSIM APSIM Upland Rice Upland Rice 0.0 0.0 DSSAT DSSAT -30 -20 -10 0 10 20 30 0 50 100 150 Precipitation Variation N Levels
    • LAI LAI 1.5 1.5LAI Response of e) f)APSIM-ORYZAand CERES-Rice 1.0 1.0 Relative LAI Relative LAIto temperature,CO2, rainfall, and 0.5 0.5N fertilizationAlex Heinemann, APSIM Upland Rice APSIM Upland Rice 0.0 0.0 DSSAT DSSATBrazil, Aug 2011 -2 0 2 4 6 8 400 500 600 700 Temperatura CO2 levelAPSIMCERES LAI LAI 1.5 1.5 APSIM g) h) DSSAT 1.0 1.0 Relative LAI Relative LAI 0.5 0.5 APSIM Upland Rice Upland Rice 0.0 0.0 DSSAT -30 -20 -10 0 10 20 30 0 50 100 150 Precipitation Variation N Levels
    • Maize Crop Pilot – Preliminary Results Simona Bassu, Jean Louis Durand, Jon Lizaso, Ken Boote Baron Christian, Basso Bruno, Boogard Hendrik, Cassman Ken, Delphine Deryng, De Sanctis Giacomo, Izaurralde Cesar, Jongschaap Raymond,Kemaniam Armen, Kersebaum Christian, Kumar Naresh, Mueller Christoph, Nendel Claas, Priesack Eckart, Sau Federico, Tao Fulu, Timlin Dennis, Jerry Hatfield, Marc Corbeels
    • Model Behaviour: Maize Crop Pilot Preliminary Sensitivity Analysis Low input information ….Response to Temperature (6 models) Morogoro (Tanzania) Ames (Us) 1,8 1,8 1,6 1,6 1,4 1,4 1,2 1,2 Yield ratioyield ratio 1 1 0,8 0,8 0,6 0,6 0,4 0,4 0,2 0,2 0 0 -5 0 5 10 -5 0 5 10 Temperature increase (°C) T increase
    • Models Behaviour: Maize Crop Pilot Preliminary Sensitivity Analysis Low input information ….Response to CO2 (6 models) Morogoro (Tanzania) Ames (US) 1,5 1,5 1,4 1,4 1,3 1,3 yield ratioYield ratio 1,2 1,2 1,1 1,1 1 1 0,9 0,9 300 400 500 600 700 800 300 400 500 600 700 800 [CO2] ppm [CO2] ppm
    • AgMIP Initiatives – Track 1 Experimenters & Crop Modelers Workshops Model ImprovementTrack 1Track 2 − Test against observed data on response to CO2, Temperature, including Interactions with Water, and Nitrogen Availability 20
    • Calibration of CERES and APSIM maize models against 4 seasons at Wa, Ghana 5000 Simulated versus observed maize yield at Wa, Ghana over 4 years, using CERES-Maize (data courtesy, Jesse Naab) 4000Simulated Grain Yield, kg/ha y = 0.833 x + 361 3000 R2 = 0.925 2000 1000 0 0 1000 2000 3000 4000 5000 Observed Grain Yield, kg/ha
    • Tested CROPGRO-Peanut model response to temperature.Crop grown at 350 ppm CO2. Model mimics observed pattern ofbiomass & pod mass vs. temperature with pod failure at 39C. 12000 AgMIP, test accuracy of multiple crop models Crop or Pod, kg / ha 10000 against data like this. Arrow is Southern 8000 US crop cycle temp. 6000 4000 Sim - Pod Obs - Pod Sim - Crop 2000 Obs - Crop 0 25 30 35 40 45 Mean Temperature, °C Genetic Impr. Heat tolerance
    • 4000 Predicted - 700 Seed Yield, kg / ha Observed - 700Simulated Seed 3000Yield of Dry BeanMontcalm vs. 2000Temperature 1000No change neededin temp effect on 0podset or sd growth 20 25 30 35 40 Mean Temperature, °C 6000Final Biomass of Mod Sim Crop or Pod, kg / ha Obs - CropDry Bean Montcalm Default Sim 4000vs. TemperatureMade leaf Ps lesssensitive to high 2000temperature 0 20 25 30 35 40 Mean Temperature, °C
    • REGIONAL ECONOMIC MODELING 24
    • Regional Modeling: 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 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 technologies and socio-economic conditions• Goal of AgMIP regional modeling is to advance CC impact and adaptation research through the development of Protocols for systematic implementation of impact and adaptation analysis, inter- comparison and improvement.
    • Regional Modeling Activities• Regional SSA and SA Teams – All teams use at least one standard modeling approach (TOA-MD and others according to region, team composition and interests) – All teams develop RAPs, adaptation scenarios for their regions, consistent with global RCPs, SSPs and RAPs – Further refine RAPs concepts and protocols• Linking regional models to national/global models – Methods for coupling global model prices, other variables to regional analysis – Inter-comparison of global and regional model outputs?• Linking climate data, crop & livestock models to regional economic models – Developing improved methods for systematic use of climate data, soils and other biological data with crop & livestock models to characterize spatial and temporal distributions of productivity for use with economic models• Methods to assess uncertainty in parameters, model structure – Parameter estimation methods based on survey, experimental, modeled and expert data; functional form and distributional assumptions – Within and between individual model levels (climate, crop, econ) 26
    • Example: New Methods for Linking Crop and Regional Economic Models• Question: how to quantify the future productivity of ag systems for impact assessment and adaptation analysis, accounting for spatial heterogeneity?• Answer: use crop models to simulate relative yield distributions: – y2 = (1+ /y1) y1 = r y1 giving r = (1+ /y1) where r = r + r , (0,1) – Using this model, with observations on one system and plausible bounds on r & r we can approximate mean, variance and between-system correlations for the other system – data for r & r can come from crop model simulations Example: maize relative yield distribution in Machakos, Kenya R = future yield/present yield
    • Sensitivity analysis of alternative methods of estimating relative yield distribution with matched and unmatched site- specific data and averaged data (simulated CC gains and losses, using TOA-MD model for Machakos, Kenya) 100000 Analysis shows critical role that 80000 estimation of spatial variance 60000 (heterogeneity) plays in estimation of distributional impacts. 40000 20000Losses 0 0 10 20 30 40 50 60 70 80 90 100 -20000 1a = time-averaged, matched bio-phys & econ data by site -40000 1b = matched bio-phys & econ data by site (not time averaged) 2a = time-averaged, unmatched bio-phys & econ data by site -60000 2b = unmatched bio-phys & econ data by site (not time averaged) 3a = site-specific bio-phys data, spatially averaged econ data with -80000 approximated spatial variance 5a = averaged bio-phys and econ data -100000 5b = averaged bio-phys and econ data, approximated variance of bio- phys data only Percent of Farms 1a 1b 2a 2b 3a 5a 5b 28
    • Example: Using TOA-MD and RAPs to simulate distributional impacts of CC and adaptation strategies using dual-purpose sweet potato, Vihiga and Machakos Districts, Kenya (note effect of RAPs on base and estimated impacts) Vihiga Machakos Poverty Rate (% of farm population living on <$1 per day) Scenario No Dairy Dairy Total No Dairy Dairy Irrigated Total base 85 38 62 85 43 54 73 CC 89 49 69 89 51 57 78 imz 87 42 65 85 44 50 73 dpsplw 88 42 66 85 44 50 73 dpsp 85 41 63 83 43 50 71 dpsp1 85 36 60 83 41 49 71 dpsp12 85 30 58 83 38 48 70 RAP1 base 65 17 41 72 30 46 60 RAP1 CC 71 18 44 77 33 47 64 RAP1 imz 66 15 41 70 27 40 58 RAP1 dpsp 65 15 40 69 27 40 57Source: Claessens et al. Agricultural Systems in press 2012 29
    • GLOBAL ECONOMIC MODELINTERCOMPARISONINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 30
    • Why bother? We all have lots to do! It matters • Policy makers care if we tell them  Agricultural land use will expand dramatically  Agricultural prices will increase by 100% between now and 2050  Climate change will increase the number of malnourished children by 25%  Increased agricultural research expenditures can cut both of those numbers in half Policy makers want 1 handed economists
    • WHAT DO THE MODELS SAYABOUT AGRICULTURAL PRICES?INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
    • IMPACT: Economy, demography and climate changes increase prices(price increase (%), 2010 – 2050, Baseline economy and demography) Minimum and maximum effect from four climate scenarios Page 33
    • Alternate Perspectives on PriceScenarios (perfect mitigation), 2004- 2050 IMPACT has substantially greater price increases Page 34
    • Alternate perspectives on agricultural area changes, 2004-2050IMPACT has IMPACT has land use increases innegative net land some countries and decreasesuse change elsewhere Page 35
    • Activities Phase 1, Single reference scenario • Single set of common drivers – income, population, agricultural productivity without climate change • What do models say about key outputs? • Why do they differ? Phase 2, Explore relevant scenario spaces • E.g., RAPs as drivers • Linkages to crop and regional economic models
    • REFERENCE SCENARIO:A ‘TASTE’ OF THE INITIAL RESULTSINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
    • World wheat prices, perfect mitigation
    • World coarse grains price , perfect mitigation
    • World agricultural land, perfect mitigation