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6 CIMMYT- Investing in data for improved modeling

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A one-day Strategic Foresight Conference took place at IFPRI Headquarters in Washington DC on November 7, 2014. Participants from leading global modeling groups, collaborating CGIAR centers and research programs, and other partners reviewed new long-term projections for global agriculture from IFPRI and other leading institutions, examined the potential impacts of climate change and other key challenges, and discussed the role of foresight work in identifying and supporting promising solutions.
Topics included:
Long-term outlook and challenges for food & agriculture
Addressing the challenges
Foresight in the CGIAR
Webcast video of morning sessions available on Global Futures program website here: http://globalfutures.cgiar.org/2014/11/03/global-futures-strategic-foresight-conference/

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6 CIMMYT- Investing in data for improved modeling

  1. 1. Investing in data for improved modeling Sika Gbegbelegbe Strategic Foresight Conference IFPRI, Washington DC, 7 November 2014
  2. 2. Outline • Improving wheat and maize crop models: process • Linking crop and socio-economic models • Integration with work of others in CIMMYT: awareness
  3. 3. IMPROVING WHEAT CROP MODEL
  4. 4. Wheat – Context • Objective: develop baseline crop models for impact assessment studies (global and regional scales); June 2011 • Data requirements: – List of representative crop varieties – Measured field trial data for crops: planting date; anthesis and maturity dates; grain yield at maturity; kernel weight; etc.
  5. 5. • Determined parameters of 1 representative cultivar for each of 17 Mega- Environments (MEs) (data, expert knowledge and genetic info) Calibration & validation (IWIS) Stat. analysis & validation (IWIS) Validation (IWIS) ME Repr. cultivars ME1 Seri M 82 ME1 PBW 343 (Attila) ME2A Kubsa (Attila) ME2B Tajan ME 3 Alondra ME 4A Bacanora (Kauz) ME 4B Don Ernesto INTA ME 4C HI 617 (Sujata) ME 5A Kanchan ME 5B Debeira ME 6 Saratovskaya ME 7 Pehlivan ME 8A Halcon SNA ME 8B Katya ME 9 Bacanora (kauz) ME 10 Bezostaya ME 11 Brigadier ME 12 Gerek 79
  6. 6. • Measured field trial data for Seri M 82 and Kauz from 1991 to 1995: grain yield; days to anthesis; days to maturity; kernel weight ; aboveground biomass; harvest index; kernel number per m2 ME Repr. Cult. P1V P1D P5 G1 G2 G3 Phint ME1 Seri M 82 20 94 564 22 39 1.0 120 ME 4A Bacanora (Kauz) 20 94 564 24 37 1.0 120 ME2A Kubsa (Attila) 20 94 564 22 40 1.0 120
  7. 7. Calibration: simulated vs. measured parameters for Seri M 82 and Kauz in Obregon, Mexico (ME 1) 0 2000 4000 6000 8000 10000 Yield(kg/ha) Yield-M Yield-S 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Yield(kg/ha) Yield-M Yield-S
  8. 8. 0 2 4 6 8 10 12 0 4 8 12 Simulatedgrainyield(tonnes/ha) Measured grain yield (tonnes/ha) 1:1 line Seri-Ludh (ME 1) Seri-Sud (ME 5) Kauz-Ludh (ME 1) Kauz-Sud (ME 5) Kauz-Bang (ME 5) Attl-Obre (ME 1) Attl-Ludh (ME 1) Attl-Sud (ME 5) Attl-Chile (ME 2) Evaluation I: Seri M 82, Kauz and Attila
  9. 9. 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Simulatedgrainyield(tonnes/ha) Measured grain yield (tonnes/ha) 1:1 line Alondra-Brazil Sujata-India Kanchan-India Kanchan-Bang Debeira-Sudan Debeira-Brazil Katia-Iran Katia-Turkey Bezostaya-Turkey Bezostaya-Iran Evaluation II: other benchmark varieties
  10. 10. From site-specific to global simulations • Global input data: adjustments – Climate: measured data between 1980 and 2000; rainfall; temperature; elevation – Soil: initial conditions; adjustments – Management: • Varieties: done • Fertilizer application: adjustments • Irrigation application • Planting months: adjustments – Overlapping of MEs (with low average yields)
  11. 11. FAO annual yield data - average 1999-2001 (kg/ha) 0 2000 4000 6000 8000 Simulatedannualyield(kg/ha) 0 2000 4000 6000 8000 GER FRA CHN RUS USA IRAN MOR Large producers Medium producers Small producers 1:1 Line • Paper submitted to ‘Agricultural Systems’: authors include modelers from CIMMYT; breeders and physiologists from CIMMYT and ICARDA; modelers from IFPRI
  12. 12. IMPROVING MAIZE CROP MODEL
  13. 13. Parameterization: benchmark maize Environment Bench. cult. Highland BH660 Wet upper mid-alt. WH403 Wet lower/up. mid-alt. SC403 Dry mid-altitude SC513 Wet lowland POI 30F32 Wet lower mid-altitude ZM521 USA and Canada Garst 8808 South America DKB 333B Southern Europe A632 x W117 Europe (other) DEA Chn, JPN, NKR, SKR CF1505 Middle east (cold) POI 31R88 Middle East + Egypt ZM521 South East Asia Suwan 3851 Australia and NWZL DeKalb XL82 Calibration & validation Literature (DSSAT) • Challenge: measured data (issues) • Investment in data collection: weather data; field trial data; work started in 2012
  14. 14. Baseline global maize production • Efforts underway to develop global baseline maize simulations: different crop management (e.g., split application of fertilizer for rainfed and irrigated maize) • Simulation against FAOSTAT data 14 y = 0.52x + 1378.8 R² = 0.67 0 2000 4000 6000 8000 10000 12000 -3,000 2,000 7,000 12,000 SimulatedYield(kg/ha) Yield from FAO (kg/ha) for major producers 1:1
  15. 15. LINKING CROP AND SOCIO- ECONOMIC MODELS
  16. 16. Linking crop and economic models • Impact of 2012 weather extreme on maize production in the USA and related effects on global food security • Bio-economic impact of climate change on maize-based systems in Africa • DT wheat in CWANA: do adoption pathways matter?
  17. 17. Awareness of Foresight Modeling in CIMMYT • Increased awareness among Foresight modeling team: economic; crop and spatial modelers • Awareness within CIMMYT (Wheat and Maize CRPs) – June 2011: one wheat breeder (with statistical background) out of 3 was able to support (input data) – January 2012: Global Futures meeting in Kenya (all centers) • 2 wheat breeders (CIMMYT and ICARDA) involved • Increased awareness for maize breeders and physiologists – August 2013: CIMMYT-wide meeting on foresight modeling (modelers from UF) • Presentation of preliminary results: results from bio-economic modeling • Demands from breeders and pathologists: recommendation domains for testing DT wheat (wheat); foresight on disease incidence (maize)

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