Silvestro Paolo Cosmo, Pignatti Stefano, Yang Hao, Yang Guijun, Pascucci Simone, Castaldi Fabio, Casa Raffaele
Department of Agriculture Forestry and Nature (DAFNE), University of Tuscia, Viterbo, Italy.
Institute of Methodologies for Environmental Analysis (IMAA), Consiglio Nazionale delle Ricerche (CNR), Tito Scalo, Potenza, Italy.
PLoS One. 2017 Nov 6;12(11):e0187485. doi: 10.1371/journal.pone.0187485. eCollection 2017.
Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations.
基于过程的模型可有效地用于评估干旱对田间和区域尺度作物产量的影响。然而,在许多情况下,尤其是在区域尺度使用这些模型时,有必要确定对输出影响最大的参数和输入变量,并评估当气候和环境条件变化时它们的影响如何变化。在这项工作中,比较了两种能够表示产量对水分响应的不同作物模型Aquacrop和SAFYE,目的是通过全局敏感性分析(GSA),使用Morris和EFAST(扩展傅里叶幅度敏感性测试)技术,对中度到强度水分受限的气候情景量化它们的复杂性和可塑性。尽管敏感性指数的排名受所用情景的影响,但通过自上而下相关系数(TDCC)评估,SAFYE的排名之间的相关性高于Aquacrop,揭示了明显的模式。发现与物候和水分胁迫生理过程相关的参数和输入变量对Aquacrop影响最大。对于SAFYE,发现水分胁迫可以从原始SAFY模型中描述的调节叶片生长的过程中间接推断出来。SAFYE的复杂性和可塑性低于Aquacrop,这使得它更适合于对数据要求较低的区域尺度应用,前提是唯一的目标是评估作物产量,并且不寻求关于影响其限制的胁迫因素机制的详细信息。