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Intercomparison and Performance of Maize Crop Models and Their Ensemble for Yield Simulations in Brazil

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Abstract

Maize yield prediction is of extreme importance for both identifying those locations with high potential for this crop and determining the yield gaps of the crop where it is currently produced. The most feasible way to estimate crop yields is with the use of crop simulation models, since well calibrated and evaluated. Even though, these estimations have uncertainties once the crop models are not complete. Recent studies have shown that crop models´ uncertainties can be reduced when several models are used together, in an ensemble. Considering that, this study aimed to calibrate and evaluate three crop simulation models (AEZ-FAO; DSSAT-CERES-Maize and APSIM-Maize) to estimate maize potential and attainable yields and to assess the performance of different ensemble strategies to reduce their uncertainties for maize yield prediction. Weather, soil and maize yield data from 79 experimental sites in Brazil were used for calibrating and evaluating these models. After that, the models showed only a good performance, with mean absolute errors (MAE) between 727 and 1376 kg ha−1, R2 between 0.49 and 0.79, d index between 0.78 and 0.94, and C index from 0.54 to 0.84. When the ensemble was applied, using the combination of two models (DSSAT-CERES-Maize and APSIM-Maize), the results showed a better performance than each single model or even the average of them, with MAE = 799 kg ha−1, R2 = 0.79, d = 0.94 and C = 0.84, allowing us to conclude that the ensemble of simulated maize yields is a good strategy to reduce uncertainties on simulations.

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  1. Department of Biosystems Engineering, Agricultural College Luiz de Queiroz (ESALQ), University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil

    Yury C. N. Duarte & Paulo C. Sentelhas

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Duarte, Y.C.N., Sentelhas, P.C. Intercomparison and Performance of Maize Crop Models and Their Ensemble for Yield Simulations in Brazil. Int. J. Plant Prod. 14, 127–139 (2020). https://doi.org/10.1007/s42106-019-00073-5

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