Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, P.R. China.
Inspection and Quarantine Technology Communication Department, Shanghai Customs College, Shanghai, P.R. China.
PLoS One. 2021 Nov 18;16(11):e0259929. doi: 10.1371/journal.pone.0259929. eCollection 2021.
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.
提高国内油菜籽产量是中国的一个重要国家目标。研究人员通常使用作物模型等工具来确定新品种的最佳管理实践,以提高产量。CROPGRO-油菜模型尚未在中国用于模拟油菜。这项工作的总体目标是确定 CROPGRO-油菜模型的关键输入,以便在长江流域有限的数据集上进行校准。首先,我们进行了全局敏感性分析,以确定对模拟开花天数、成熟天数、产量、地上生物量和最大叶面积指数有较大影响的关键遗传和土壤输入。在长江流域的 8 个地点(空间分析)和中国武汉的一个地点(时间分析)进行了单一年份的扩展傅立叶幅度测试法(EFAST)敏感性分析。EFAST 软件为每个地点和年份的 4520 个输入参数组合运行,为每个输入参数生成一个敏感性指数。使用自上而下的一致性方法对参数进行排名,以确定相对敏感性。结果表明,开花天数、成熟天数、产量、地上生物量和最大叶面积指数的模型输出对影响关键生长阶段持续时间的参数最为敏感,例如出苗到开花和这些阶段的温度响应,以及影响收获时总生物量的参数。五个模型输出对几个土壤参数也很敏感,包括排干上限和下限(SDUL 和 SLLL)和排水率(SLDR)。参数的敏感性通常在空间和时间上是稳定的。敏感性分析的结果用于在中国武汉的一个油菜单实验中对模型进行校准和评估。该模型使用两个季节进行校准,并使用三个季节的数据进行评估。开花天数(≤1.71%)、成熟天数(≤1.48%)、产量(≤9.96%)和地上生物量(≤9.63%)的 nRMSE 值均获得了优异的结果。这项工作的结果可以指导研究人员在中国长江流域进行模型校准和评估。