Zhang Ying, Ai Pengrui, Ma Yingjie, Fu Qiuping, Ma Xiaopeng
College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Xinjiang Water Conservancy Project Safety and Water Disaster Prevention Key Laboratory, Urumqi 830052, China.
Plants (Basel). 2025 Aug 22;14(17):2608. doi: 10.3390/plants14172608.
The APSIM (Agricultural Production Systems Simulator)-Wheat model has been widely used to simulate wheat growth, but the sensitivity characteristics of the model parameters at different soil moisture levels in arid regions are unknown. Based on 20232025 winter wheat field data from the Changji Experimental Site, Xinjiang, China, this study conducted a global sensitivity analysis of the APSIM-Wheat model using Morris and EFAST methods. Twenty-one selected parameters were perturbed at ±50% of their baseline values to quantify the sensitivity of the aboveground total dry matter (WAGT) and yield to parameter variations. Parameters exhibiting significant effects on yield were identified. The calibrated APSIM model performance was evaluated against field observations. The results indicated that the order of influential parameters varied slightly across different soil moisture levels. However, the WAGT output was notably sensitive to accumulated temperature from seedling to jointing stage (T1), accumulated temperature from the jointing to the flowering period (T2), accumulated temperature from grain filling to maturity (T4), and crop water demand (E1). Meanwhile, yield output showed greater sensitivity to number of grains per stem (G1), accumulated temperature from flowering to grain filling (T3), potential daily grain filling rate during the grain filling period (P1), extinction coefficient (K), T1, T2, T4, and E1. The sensitivity indices of different soil moisture levels under Morris and EFAST methods showed highly significant consistency. After optimization, the coefficient of determination (R) was 0.8770.974, the index of agreement (d-index) was 0.9410.995, the root mean square error (RMSE) was 319.45642.69 kg·ha, the mean absolute error (MAE) was 314.69473.21 kg·ha, the residual standard deviation ratio (RSR) was 0.680.93, and the Nash-Sutcliffe efficiency (NSE) was 0.26~0.57, thereby enhancing the performance of the model. This study highlights the need for more careful calibration of these influential parameters to reduce the uncertainty associated with the model.
APSIM（农业生产系统模拟器）-小麦模型已被广泛用于模拟小麦生长，但该模型参数在干旱地区不同土壤湿度水平下的敏感性特征尚不清楚。基于中国新疆昌吉试验站2023 - 2025年冬小麦田间数据，本研究采用Morris和EFAST方法对APSIM -小麦模型进行了全局敏感性分析。选取的21个参数在其基线值的±50%范围内扰动，以量化地上部总干物质（WAGT）和产量对参数变化的敏感性。识别出对产量有显著影响的参数。根据田间观测评估校准后的APSIM模型性能。结果表明，不同土壤湿度水平下影响参数的排序略有不同。然而，WAGT输出对从幼苗到拔节期的积温（T1）、从拔节到开花期的积温（T2）、从灌浆到成熟期的积温（T4）以及作物需水量（E1）显著敏感。同时，产量输出对每茎粒数（G1）、从开花到灌浆的积温（T3）、灌浆期潜在日灌浆速率（P1）、消光系数（K）、T1、T2、T4和E1更为敏感。Morris和EFAST方法下不同土壤湿度水平的敏感性指数显示出高度显著的一致性。优化后，决定系数（R）为0.8770.974，一致性指数（d指数）为0.9410.995，均方根误差（RMSE）为319.45642.69 kg·ha，平均绝对误差（MAE）为314.69473.21 kg·ha，残差标准差比（RSR）为0.680.93，纳什-萨特克利夫效率（NSE）为0.260.57，从而提高了模型性能。本研究强调需要更仔细地校准这些有影响的参数，以减少与模型相关的不确定性。
