The values of the physical-water attributes of soils for use in agricultural simulation models are usually obtained using difficult and time-consuming methods. The objective of this study was to analyze the performance of the AquaCrop model to estimate soybean and maize crop productivity in the region of Campos Gerais (Brazil), with the option of including soil physical-water attributes in the model. Real crop productivities and input data (soil, climate, crop and soil management) were obtained from experimental stations of the ABC Foundation for the crop years 2006 to 2014. Sixty-four yield simulations were performed for soybean (four municipalities) and 42 for maize (three municipalities), evaluating input soil data scenarios of AquaCrop as follows: i) all soil physical-water attributes were measured (standard) and ii) the attributes were measured only using textural classification of the area (alternative). Real and simulated yields were verified by simple linear regression analyses and statistical indices (r, d, c). The standard scenario yielded performances between very good and excellent (0.75<c≤1.0) for soybean and between bad and excellent (0.40<c≤1.0) for maize. The alternative scenario was more variable, with performances between terrible and excellent (0.0<c≤1.0) for soybean and terrible and medium (0.0<c≤0.65) for maize. Using only the soil texture classification in AquaCrop indicated an easier way to estimate crop yields, but low performances may restrict estimates of soybean and maize yields in Campos Gerais.
The computational models that simulate yield of agricultural crops are important to planning activities. The objective of this study was to verify the performance of AquaCrop model to simulate soybean and maize yield in Campos Gerais region, in different soil types. The AquaCrop was used to estimate yield, requiring climate, soil, crop and soil management input data. In the analysis were used data from 21 and 32 experiments with maize and soybeans, respectively, carried out in the ABC Foundation, from years harvest between 2006 and 2014. For soybean crop, the highest absolute and relative errors of productivity simulations occurred in less productive crops, due to the lack of rain during sowing, water deficit in the harvest or high temperatures in the first weeks after the plants emergence. The highest absolute and relative errors verified in the simulations with maize crop experiments did not allow defined pattern identification. The AquaCrop achieved “very good” and “excellent” performances in the simulations of soybean and maize yield it the analyzed locations. The soil type affected the results from the analyzes of the two crops, and the Latossolos provided better performance and higher correlation compared to other soil types.
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