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Crop yield predictions using seasonal climate forecasts

Crop yield predictions using seasonal climate forecasts. SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE simone.sievert@cptec.inpe.br Second EUROBRISA Workshop July 2009 – Dartmoor, Devon- UK. Aim. Investigate the potential of using seasonal climate forecasts for producing maize yield predictions.

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Crop yield predictions using seasonal climate forecasts

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  1. Crop yield predictions using seasonal climate forecasts SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE simone.sievert@cptec.inpe.br Second EUROBRISA Workshop July 2009 – Dartmoor, Devon- UK

  2. Aim Investigate the potential of using seasonal climate forecasts for producing maize yield predictions Crop yield model Climate Forecast National Statistics Maize Grain Yield Source: IBGE

  3. The Study Area 27.2°- 29.8°S/51.2°- 56.0°W Rio Grande Do Sul State (RS) “Long River of South”

  4. About Maize in RS… After USA and China, Brazil is the main maize producer in the entire world, and RS is the second greatest producer in Brazil (IBGE, 2006). Sowing Date: Sep/Oct Harvest: Feb/March Crop cycle ~130 days

  5. Crop model: General Large Area ModelChallinor et al., 2003 Daily data required: Yield is a time varying fraction of Biomass YIELD BIOMASS Solar Radiation Min. Temperature Max. Temperature Rainfall Transpiration Efficiency ROOT SYSTEM TRANSPIRATION Development Stage LEAF CANOPY SOIL WATER Yield Gap Parameter Water Stress Outputs Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading)

  6. Muller et al., 2005 Calibration GLAM was based on observational data (soil and crop phenology). UFRGS, Eldorado do Sul Site, Brazil.

  7. Maize yield predictions ECMWF System 3: Anderson et al. (2007) ECMWF Tech. Memo, 503, pp 56 Meteorological stations: Daily data Rain, T, S (climat) B.corr. Rain T, S (climat.) • GLAM model • Challinor et al. 2003 Fcst. crop Sim. crop Morse et al. (2005) Tellus, 57A(3), 464-475 Hindcast period: 1989-2005 0 to 5 month lead predictions; 11 ensemble members

  8. Seasonal weather data into crop model: Monthly Mean Rainfall ECMWF (bias corrected) forecasts, 11 ensemble members initialized in Sept. (for Sept, Oct, Nov, Dec, Jan, Feb) Radiation & Temperature Daily mean observed climatology for wet and dry days (1989 – 2005).

  9. Correlation Between ECMWF monthly Forecasts and Obs. Rainfall Anomalies (1981-2005), Issue Sep. oct. nov. sept. dec. jan. feb.

  10. Time disaggregation: Monthly mean to daily rainfall using a weather generator • Stochastic weather generator (first order Markov chain) based on gamma rainfall PDF (Moron, 2005) • Input data: daily rainfall observations and monthly mean rainfall predictions • Output data: daily rainfall sequences

  11. Daily Rainfall Histogram for a county - Sept-Feb (1989-2005) Observation WG (ECMWF) for 2 members

  12. Daily Rainfall Sequence for All Januaries (1989-2005) 1989 1994 1999 2004 Obs. 1989 1994 1999 2004 WG (ECMWF-Mb. 5)

  13. Daily Rainfall Sequence for All Octobers (1989-2005) 1989 1994 1999 2004 Obs. 1989 1994 1999 2004 WG (ECMWF- Mb. 5)

  14. fcst obs Grain yield RS state produced six months in advance Grid Point 1 Grid Point 2

  15. 3 fcst obs Grain yield prediction for indiv. Countyproduced six months in advance 5 7 Grid Point 1

  16. 3 fcst obs Grain yield predictionfor indiv. Countyproduced six months in advance 5 7 Grid Point 2

  17. Summary • Stochastic weather generator: powerful tool for making use of monthly mean rainfall forecasts from coupled seasonal forecast models for producing crop yield predictions • Preliminary results show promising usefulness of monthly mean rainfall forecasts produced by ECMWF coupled model for producing maize yield predictions for RS six months in advance

  18. Future Directions • Use monthly mean rainfall forecasts from other coupled models (e.g. CPTEC, UK Met Office and Meteo-France) into weather generator for use in maize yield crop model • Compared skill of different crop yield forecasts produced using different coupled model monthly mean rainfall forecasts • Further investigate potential of using seasonal climate forecasts for producing maize yield predictions for other locations (e.g. Uruguay)

  19. Thanks: • Caio Coelho (CPTEC- Brazil) • Homero Bergamaschi (UFRGS, Brazil) • Andrew Challinor (The University of Leeds-UK)

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