CROP MODELING WITH LESS DATA: THE FAO MODEL FOR SOYBEAN YIELD ESTIMATION

AUTOR(ES)

Richetti, Jonathan; Johann, Jerry Adriani; Uribe-Opazo, Miguel Angel

FONTE

Eng. Agríc.

DATA DE PUBLICAÇÃO

2021-04

RESUMO

ABSTRACT Crop growth simulation models such as WOFOST and DSSAT are useful, but require several inputs that sometimes are not available, especially in developing areas. In addition, measured data is usually time and labor-intensive. In search of faster and easier methods for soybean estimates, this study presents a lower input requiring methodology for yield estimation. This study combines the FAO-33 yield model with the agro-ecological zone approach for soybean yield estimations using mostly indirect data. Sowing and harvest dates and yield were collected from 74 soybean commercial farms. Agrometeorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. Fifty farms (66%) were used to calibrate the model and 24 farm areas (33%) were used for evaluation purposes. Two methodologies (FAO-56 and Thornthwaite and Mather) for water balance and actual evapotranspiration (ETa) estimations were used. The comparison of yield estimations and observations showed that the use of low data input to obtain reasonable accuracy, with a mean error of −310 kg ha−1 and a mean absolute percentage error of 23.3%.

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