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Abstract：
Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.
摘要：
用于估计作物模型参数的观测数据集的适当组合可以在确保准确性的同时降低计算成本。本研究旨在探讨观察到的物候阶段的不同组合对品种特异性参数（CPSs）估算的定量影响。我们使用CROPGRO-大豆物候模型（CSPM）作为案例研究，并结合了广义似然不确定性估计（GLUE）方法。四个观测物候阶段的不同组合，包括最初的开花，初始pod,初始颗粒,和来自Exp的五个大豆品种的初始成熟阶段。1和Exp。表2中描述的图3分别用于校准CSP。CSPM,由优化的CSP驱动，然后针对来自Exp的两个独立的物候数据集进行评估。2和Exp。4在表2中描述。均方根误差(RMSE)(平均绝对误差(MAE)，决定系数(R2)，纳什·萨特克利夫模型效率(NSE))为15.50(14.63、0.96、0.42)，4.76(3.92,0.97,0.95),4.69(3.72,0.98,0.95),3.91（3.40、0.99、0.96）和12.54（11.67、0.95、0.60），5.07(4.61,0.98,0.93),4.97(4.28,0.97,0.94),4.58(4.02,0.98,0.95)使用一个,两个,三,在CSP估计中观察到四个物候阶段。评价结果表明，RMSE和MAE下降，R2和NSE随着用于参数校准的观测物候阶段数的增加而增加。然而,RMSE(MAE，NSE)使用两个，三,和四个观察阶段。通过使用至少两个观察到的物候阶段平衡校准效果和计算成本来获得用于CSMP的相对可靠的优化CSP。这些发现为作物模型的参数估计提供了新的见解。
