Estimation of Coffee Yield from Gridded Weather Data

Abstract

Agrometeorological models have been applied to crop yield estimations, providing information for management. A limiting factor of these models is the input data, which come mostly from surface meteorological stations (SMS). We propose the use of gridded weather data provided by the European Center for Medium-Range Weather Forecasts (ECMWF) and the NASA as a viable and innovative alternative for coffee (Coffea arabica L.) yield estimation in Brazil. We made modifications in the coefficients of the model proposed by Santos and Camargo, regarding the penalties for extreme temperatures and those related to different gridded data sources. The accuracy, measured by the mean absolute percentage error (MAPE), was 23.76, 24.61, and 22% for the calibrations with ECMWF, NASA, and SMS data, respectively. These high levels of MAPE were the result of the high biennality of the crop. The average tendency, measured by the systematic root mean square error, was an overestimation (or underestimation) of +/- 465 kg ha(-1) by the ECMWF, +/- 411 kg ha(-1) by NASA, and +/- 653 by the SMS models. The mean precision, measured by the nonsystematic root mean square error, was 186, 190, and 280 kg ha(-1) for the ECMWF, NASA, and SMS models, respectively. These results indicate that coffee yield for Sao Paulo and Minas Gerais can be calibrated and estimated with the ECMWF and NASA gridded data.