Abstract
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.
Highlights
Drought is one of the phenomena which most influences agricultural production worldwide, causing significant and occasionally dramatic harvest losses [1]
The time trends of simulated above ground biomass and leaf area index (LAI) were in agreement with measured data for most of the sowing dates, the time of simulated grain maturity was slightly delayed as compared to the actual harvest dates (S1 Fig)
The results reported in this study provide key elements to the knowledge on the behavior, in water limited climatic scenarios, of two models, Aquacrop and Simple Algorithm For Yield (SAFY), which have an increasing user community and are extremely interesting in the context of regional scale studies, and for remote sensing data assimilation [7, 13, 22, 24, 56]
Summary
Drought is one of the phenomena which most influences agricultural production worldwide, causing significant and occasionally dramatic harvest losses [1]. As recently reviewed by [2], considerable efforts have been made to analyze the complex phenomenon of drought and assess its severity and impact [3,4,5] Available approaches, in this context, include the prediction of yield losses in the presence of water shortages, both at the field and at the regional scale, e.g. using remote sensing data [6] coupled to crop models [7]. Many models are available for this purpose, with varying degree of complexity and predictive performance, such as WOFOST [5, 8], the CERES DSSAT models [9], STICS [10] or CROPSYST [11], among the most widely used These process-based crop models were originally conceived for field scale applications, they are increasingly employed for spatialized regional scale studies [12,13] and remote sensing data assimilation [13,14,15,16]. Crop models are usually quite demanding in terms of data requirements and, when applied to large areas, they can be affected by many sources of uncertainty, due to the poor quality of input data (e.g. weather, soil), to the lack of information on management (e.g., sowing dates, fertilization practices, cultivars grown), which can be variable in space and time, as well as to the model structure [14, 17], to the experience of the model users [18] and to the uncertainty in the data used for their calibration [19,20]
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