Evaluation and application of the CROPGRO-soybean model for determining optimum sowing windows of soybean in the Nigeria savannas

Abstract

Soybean production is limited by poor soil fertility and unstable rainfall due to climate variability in the Nigeria savannas. There is a decline in the amount and duration of rainfall as one moves from the south to north of the savanna zones. The use of adapted soybean varieties and optimum sowing windows are avenues to increase productivity in the face of climate variability. Crop simulation models can be used as tools for the evaluation of alternative management options for a particular location, including fertilizer application rates, plant density, sowing dates and land use. In this study, we evaluated the performance of the Cropping System Model (CSM)-CROPGRO-Soybean to determine optimum sowing windows for three contrasting soybean varieties (TGX1835-10E, TGX1904-6F and TGX1951-3F) cultivated in the Nigeria savannas. The model was calibrated using data from ten field experiments conducted under optimal conditions at two sites (BUK and Dambatta) in Kano in the Sudan savanna (SS) agro-ecology over four growing seasons. Data for model evaluation were obtained from independent experiment for phosphorus (P) response trials conducted under rainfed conditions in two locations (Zaria and Doguwa) in the northern Guinea savanna (NGS) zone. The model calibration and evaluation results indicated good agreement between the simulated and observed values for the measured parameters. This suggests that the CROPGRO-Soybean model was able to accurately predict the performance of soybean in the Nigeria savannas. Results from long-term seasonal analysis showed significant differences among the agro-ecologies, sowing windows and the soybean varieties for grain yield. Higher yields are simulated among the soybean varieties in Zaria in the NGS than in Kano the SS and Jagiri in the southern Guinea savanna (SGS) agro-ecological zones. Sowing from June 1 to July 5 produced optimal yield of TGX1951-3F and TGX1835-10E beyond which yield declined in Kano. In Zaria and Jagiri the simulated results show that, sowing from June 1 to July 12 are appropriate for all the varieties. The variety TGX1951-3F performed better than TGX1904-6F and TGX1835-10E in all the agro-ecologies. The TGX1951-3F is, therefore, recommended for optimum grain yield in the savannas of northern Nigeria. However, the late maturing variety TGX1904-6F is not recommended for the SS due to the short growing season in this zone.

Simulating potential yield of rainfed soybean in northeast Nigeria

“…The results of model calibration showed that the genetic coefficients for the two soybean varieties resulted in simulated growth parameters and yield that were in good agreement with their corresponding observed values, consistent with the findings of Bebeley et al (2022) . In all the locations, there was a good fit in the model prediction of days to 50% flowering, days to physiological maturity and grain yield with low RMSE and high d -index values.…”

“…The soybean model was calibrated by determining the cultivar coefficients of the two varieties (TGX 1951–3 F and TGX 1448–2E) which are widely disseminated in northeast Nigeria. Except for variety TGX 1448–2E, the calibration results for TGX 1951–3 F has been reported by Bebeley et al (2022) . The cultivar parameters calibrated and used in DSSAT are shown in Table 1a.…”

“…Data collected included days to 50% flowering, days to physiological maturity, grain and biomass yield. The detailed climate, field observations, soil, and crop management practices used for model evaluation were previously published by Bebeley et al (2022) . The response of the model was evaluated using three different statistical indicators, including root mean square error (RMSE), normalized root mean square error (RMSE n ) and index of agreement ( d -value).…”

“…Production constraints, such as pests and diseases, drought, poor soil fertility, high pod shattering, and poor agronomic practices, contribute to low soybean yields ( Kamara et al, 2014 , Khojely et al, 2018 ). Among the climatic limitations, the occurrence of drought and high temperatures during flowering and grain-filling stages particularly negatively affects the yield of soybean ( Bebeley et al, 2022 ). In the Nigeria savannas, delays in the onset of the rainy season are becoming common ( Tofa et al, 2020 ).…”

“…The CROPGRO-Soybean has been used to accurately simulate the growth and yield of soybean for a range of crop management practices in several locations. These include the investigation of variety and sowing time technologies in Brazil ( Sampaio et al, 2021 ), Nigeria ( Bebeley et al, 2022 ), and Thailand ( Banterng et al, 2010 ) and varietal performance of soybean in diverse ecologies in Kenya ( Nyambane et al, 2012 ). There is a need to determine the performance in northeast Nigeria of soybean varieties released for use in other parts of the Nigeria savannas.…”