Strategy of Soybean Management (Glycine max L.) to Cope with Extreme Climate Using CropSyst© Model

Aminah Aminah, Ambo Ala, Yunus Musa, Rusnadi Padjung, Kaimuddin Kaimuddin


This research was carried out to verify the CropSyst© plant model from experimental data in a soybean field and to predict planting time along with its potential yield. The researches were divided into two stages. First stage was a calibration for model on field from June to September 2015. Second stage was the application of the model. The required data models included climatic, soil and crop’s genetic data. There were relationship between the obtained data in field and the simulation from CropSyst© model which was indicated by 0.679 of Efficiency Index (EF) value. This meant that the CropSyst© model was well used. In case of Relative Root Mean Square Error (RRMSE), it was shown at 2.68 %. RRMSE value described that there was a 2.68 % error prediction between simulation and actual production. In conclusion, CropSyst© can be used to predict the suitable planting time for soybean and as the result, the suitable planting time for soybean on the dry land is the end of rainy season (2 June 2015). Tanggamus variety is the most resistant variety based on slow planting time, because the decreased percentage of production was lower (8.3 %) than Wilis (26.3 %) and Anjasmoro (43.0 %).


calibration; CropSyst©; Efficiency Index; RRMSE; soybean

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