Figure 1 - RS state map showing the main maize producer region and the 11 municipalities.

1. 3 5 oct. nov. sept. 7 dec. jan. feb. Monthly Mean Rainfall (ECMWF) Daily rainfall Weather Generator (IRI) Crop Model (GLAM) Maize grain Yield Fcst. Obs. This study aims to investigate the potential of using monthly mean climate forecasts from the European Centre for Medium-range Weather Forecast (ECMWF) model for producing maize yield predictions in RS in 5 months in advance. Crop yield predictions using seasonal climate forecastsSimone M. S. Costa and Caio A. S. CoelhoInstituto Nacional de Pesquisas Espaciais – INPE, São Paulo - SP SEASONAL WEATHER DATA INTO CROP MODEL STUDY AREA Brazil is the 3rd main maize producer in the entire world after USA and China, and RS State is the 2nd greatest producer in Brazil (IBGE, 2006). Maize yields interseasonal variability is high due to irregular rainfall during the cropping seasons. A stochastic weather generator was used to disaggregate the 11 ensemble members of monthly mean rainfall into daily rainfall. Then the disaggregated daily rainfall was used as input data to a process-based crop model to predict maize crop yield. Weather generator estimates the rainfall occurrence based on a 1st-order Markov chain and the amount on a gamma distribution fit to 11-yrs of daily observed rainfall. Crop model - General Large Area Model, GLAM is a process-based model for annual crops and requires daily data of solar radiation, temperature and rainfall. Data - Rainfall - ECMWF forecasts (bias corrected). Radiation &Temp. - observed climatology (INMET, FEPAGRO). Figure 1 - RS state map showing the main maize producer region and the 11 municipalities. RESULTS ECMWF couple seasonal forecast model does show skill on monthly mean rainfall forecasts during the maize crop cycle (Fig.2), suggesting the potential use this information for crop prediction. There is a generally good agreement between the simulated and the predicted yield, particularly for the last 10yrs. For most years the obs. yield is within 95% prediction interval, indicating good reliability of yield predictions. A reasonable agreement is noticed between the observed and disaggregated histograms (Fig.3), indicating that the used weather generator can reproduce the observed daily rainfall distribution accordingly. Grain yield prediction for indiv. Municipality produced six months in advance for 16 years Correlation Between ECMWF monthly Forecasts and Observed Rainfall Anomalies (1981-2005), Issue Sep. Daily rainfall histogram for Santa Rosa county Figure 4 - Grain yield prediction produced 5 months in advance for 3 municipalites (3, 5 and 7, Fig1). Black line is the ensemble mean grain yield (i.e. mean of the 11 dots). Dashed lines indicate the 95% prediction interval. Figure 2 – High positive correlation is noticed over nearly all South America in September, indicating good association between observed and forecast anomalies. Figure 3 – Daily rainfall histogram for Sept. to Feb. (1989 – 2005) based on observed rainfall and disaggregated rainfall for two of the 11 ECMWF ensemble members. Preliminary results show promising usefulness of monthly mean rainfall forecasts produced by ECMWF model for predict maize yield for RS in 5 months in advance. This work was supported by the EUROBRISA network project (F/00 144/AT) kindly funded by the Leverhulme Trust. The dynamical ensemble forecast data were kindly provided by ECMWF as part of the EUROSIP project. Three forecasting centres are the partners in EUROSIP (ECMWF, the UK Met Office and Meteo-France). The authors thank Vicent Moron for making the weather generator software available at IRI website. CASC thanks the FAPESP (process 2005/05210-7 and 2006/02497-6) for funding part of the EUROBRISA project.