Sensitivity analysis (SA) is a fundamental practice for analyzing model behavior under different conditions of application. A number of SA techniques were proposed, ranging from simple screening methods to computationally expensive variance-based ones. In this study, we compared the Morris and E-FAST methods by applying them to three widely used generic crop models largely differing for complexity and for the approaches used to formalize knowledge on crop physiology, i.e., STICS, CropSyst and WOFOST. SA experiments were carried out at sub-model level on rice crops grown under different environmental conditions. Results highlighted the lack of linearity between the total-order sensitivity estimates provided by E-FAST and Morris, although the concordance (TDCC) between the parameter rankings obtained with the two methods was always significant at the 0.05 level for parameters involved with crop growth and for those involved with phenological development for STICS, whereas it was significant at the 0.10 level for the phenology parameters of CropSyst and WOFOST. Given Morris required less than 3% of the model executions needed by E-FAST, our results allow considering Morris as a suitable alternative to more demanding SA methods when ranking parameters or discriminating between influential and non-influential model factors are the SA goals, especially in computationally expensive SA studies.

敏感性分析 (SA) 是分析不同应用条件下模型行为的基本实践。提出了许多 SA 技术，从简单的筛选方法到计算成本高昂的基于方差的方法。在这项研究中，我们通过将 Morris 和 E-FAST 方法应用于三种广泛使用的通用作物模型进行比较，这些模型的复杂性和用于形式化作物生理学知识的方法有很大不同，即 STICS、CropSyst 和 WOFOST。SA 实验是在不同环境条件下种植的水稻作物的子模型水平上进行的。结果强调了 E-FAST 和 Morris 提供的全阶灵敏度估计之间缺乏线性，尽管使用两种方法获得的参数排名之间的一致性（TDCC）对于涉及作物生长的参数和涉及 STICS 物候发育的参数始终在 0.05 水平上显着，而对于物候参数在 0.10 水平上显着CropSyst 和 WOFOST。鉴于 Morris 只需要 E-F​​AST 所需的不到 3% 的模型执行，我们的结果允许将 Morris 作为对更苛刻的 SA 方法的合适替代，当排序参数或区分有影响和无影响的模型因素是 SA 目标时，尤其是在计算昂贵的 SA 研究中。CropSyst 和 WOFOST 的物候参数为 10 级。鉴于 Morris 只需要 E-F​​AST 所需的不到 3% 的模型执行，我们的结果允许将 Morris 作为对更苛刻的 SA 方法的合适替代，当排序参数或区分有影响和无影响的模型因素是 SA 目标时，尤其是在计算昂贵的 SA 研究中。CropSyst 和 WOFOST 的物候参数为 10 级。鉴于 Morris 只需要 E-F​​AST 所需的不到 3% 的模型执行，我们的结果允许将 Morris 作为对更苛刻的 SA 方法的合适替代，当排序参数或区分有影响和无影响的模型因素是 SA 目标时，尤其是在计算昂贵的 SA 研究中。