Increasing domestic rapeseed production is an important national goal in China. Researchers often use tools such as crop models to determine optimum management practices for new varieties to ...increased production. The CROPGRO-Canola model has not been used to simulate rapeseed in China. The overall goal of this work was to identify key inputs to the CROPGRO-Canola model for calibration with limited datasets in the Yangtze River basin. First, we conducted a global sensitivity analysis to identify key genetic and soil inputs that have a large effect on simulated days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index. The extended Fourier amplitude test method (EFAST) sensitivity analysis was performed for a single year at 8 locations in the Yangtze River basin (spatial analysis) and for seven years at a location in Wuhan, China (temporal analysis). The EFAST software was run for 4520 combinations of input parameters for each site and year, resulting in a sensitivity index for each input parameter. Parameters were ranked using the top-down concordance method to determine relative sensitivity. Results indicated that the model outputs of days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index were most sensitive to parameters that affect the duration of critical growth periods, such as emergence to flowering, and temperature response to these stages, as well as parameters that affect total biomass at harvest. The five model outputs were also sensitive to several soil parameters, including drained upper and lower limit (SDUL and SLLL) and drainage rate (SLDR). The sensitivity of parameters was generally spatially and temporally stable. The results of the sensitivity analysis were used to calibrate and evaluate the model for a single rapeseed experiment in Wuhan, China. The model was calibrated using two seasons and evaluated using three seasons of data. Excellent nRMSE values were obtained for days to flowering (≤1.71%), days to maturity (≤ 1.48%), yield (≤ 9.96%), and above-ground biomass (≤ 9.63%). The results of this work can be used to guide researchers on model calibration and evaluation across the Yangtze River basin in China.
Rapeseed (Brassica napus L.) is an important oilseed crop grown worldwide with a planting area of 6.57 million ha in China, which accounts for about 20% of the world’s total rapeseed planting area. ...However, in recent years, the planting area in China has decreased by approximately 12.2% due to the low yield and economic benefits. Thus, to ensure oil security, it is necessary to develop high-efficiency cultivation for rapeseed production. Crop growth models are powerful tools to analyze and optimize the yield composition of crops under certain environmental and management conditions. In this study, the CROPGRO-Canola model was first calibrated and evaluated using the rapeseed planting data of four growing seasons in Wuhan with nine nitrogen fertilizer levels (from 120 to 360 kg ha−1) and five planting densities (from 15 to 75 plants m−2). The results indicated that the CROPGRO-Canola model simulated rapeseed growth well under different nitrogen rates and planting densities in China, with a simulation error of 0–3 days for the anthesis and maturity dates and a normalized root mean square error lower than 7.48% for the yield. Furthermore, we optimized the management of rapeseed by calculating the marginal net return under 10 nitrogen rates (from 0 to 360 kg ha−1 at an increasing rate of 40 kg ha−1) and 6 planting densities (from 15 to 90 plant m−2 at an increasing rate of 15 plant m−2) from 1989 to 2019. The results indicated that the long-term optimal nitrogen rate was 120–160 kg N ha−1, and the optimal planting density was 45–75 plants m−2 under normal fertilizer prices. The optimal nitrogen rate decreased with increasing fertilizer price within a reasonable range. In conclusion, long-term rapeseed management can be optimized based on rapeseed and nitrogen cost using long-term weather records and local soil information.
Increasing domestic rapeseed production is an important national goal in China. Researchers often use tools such as crop models to determine optimum management practices for new varieties to ...increased production. The CROPGRO-Canola model has not been used to simulate rapeseed in China. The overall goal of this work was to identify key inputs to the CROPGRO-Canola model for calibration with limited datasets in the Yangtze River basin. First, we conducted a global sensitivity analysis to identify key genetic and soil inputs that have a large effect on simulated days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index. The extended Fourier amplitude test method (EFAST) sensitivity analysis was performed for a single year at 8 locations in the Yangtze River basin (spatial analysis) and for seven years at a location in Wuhan, China (temporal analysis). The EFAST software was run for 4520 combinations of input parameters for each site and year, resulting in a sensitivity index for each input parameter. Parameters were ranked using the top-down concordance method to determine relative sensitivity. Results indicated that the model outputs of days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index were most sensitive to parameters that affect the duration of critical growth periods, such as emergence to flowering, and temperature response to these stages, as well as parameters that affect total biomass at harvest. The five model outputs were also sensitive to several soil parameters, including drained upper and lower limit (SDUL and SLLL) and drainage rate (SLDR). The sensitivity of parameters was generally spatially and temporally stable. The results of the sensitivity analysis were used to calibrate and evaluate the model for a single rapeseed experiment in Wuhan, China. The model was calibrated using two seasons and evaluated using three seasons of data. Excellent nRMSE values were obtained for days to flowering (≤1.71%), days to maturity (≤ 1.48%), yield (≤ 9.96%), and above-ground biomass (≤ 9.63%). The results of this work can be used to guide researchers on model calibration and evaluation across the Yangtze River basin in China.
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