Simulated Optimum Sowing Date for Forage Pearl Millet Cultivars in Multilocation Trials in Brazilian Semi-Arid Region

Abstract

Forage production is primarily limited by weather conditions under dryland production systems in Brazilian semi-arid regions, therefore sowing at the appropriate time is critical. The objectives of this study were to evaluate the CSM-CERES-Pearl Millet model from the DSSAT software suite for its ability to simulate growth, development, and forage accumulation of pearl millet [Pennisetum glaucum (L.) R.] at three Brazilian semi-arid locations, and to use the model to study the impact of different sowing dates on pearl millet performance for forage. Four pearl millet cultivars were grown during the 2011 rainy season in field experiments conducted at three Brazilian semi-arid locations, under rainfed conditions. The genetic coefficients of the four pearl millet cultivars were calibrated for the model, and the model performance was evaluated with experimental data. The model was run for 14 sowing dates using long-term historical weather data from three locations, to determine the optimum sowing window. Results showed that performance of the model was satisfactory as indicated by accurate simulation of crop phenology and forage accumulation against measured data. The optimum sowing window varied among locations depending on rainfall patterns, although showing the same trend for cultivars within the site. The best sowing windows were from 15 April to 15 May for the Bom Conselho location; 12 April to 02 May for Nossa Senhora da Gloria; and 17 April to 25 May for Sao Bento do Una. The model can be used as a tool to evaluate the effect of sowing date on forage pearl millet performance in Brazilian semi-arid conditions.

Simulation of the Optimum Planting Windows for Early and Intermediate-Maturing Maize Varieties in the Nigerian Savannas Using the APSIM Model

“…Planting during this period carries the risk of intermittent drought and poor crop establishment in the SS zone (Kamara et al, 2009). Decision about date of planting is important to minimize low crop establishment and ultimately avoid the extra cost of seed and labor required for replanting in the semi-arid regions (Santos et al, 2017). More importantly, the response of maize varieties to other inputs such as fertilizer is dependent upon planting time (Iken and Amusa, 2014).…”

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

“…The average value of MAPE is 4.28%, which is much lower than the prediction error in the current literature, which ranges from 8% to 22% [18]. For the normalized-RMSE values, all of them are less than 10%, which implies that the established model has excellent performance in wind electric power prediction [70]. This proves that the GOA-LSSVM can effectively improve the forecasting accuracy.…”

“…Through multiplying these three terms, the final wind electric power values are obtained. The values of three error criteria are applied to discuss the prediction performance, which are the Root Mean Square Error (RMSE, computed in accordance with Equation (29)), the Normalized-RMSE calculated based on [69,70], and the Mean Absolute Percentage Error (MAPE) calculated as below:…”

Adjusting sowing date improves the photosynthetic capacity and grain yield by optimizing temperature condition around flowering of summer maize in the North China Plain

“…Adjusting the sowing date is an effective strategy to mitigate the adverse effects of climatic factors by optimizing the climate conditions during the crop growth period ( Santos et al, 2017 ; Coelho et al, 2021 ; Deng et al, 2022 ; Li et al, 2022 ). Our previous study has demonstrated that sowing date-associated variation in temperature was the primary factor that influenced maize grain yield in the NCP ( Zhou et al, 2016 , 2017 ).…”