Selecting crop models for decision making in wheat insurance
Selecting crop models for decision making in wheat insurance
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DOI：
10.1016/j.eja.2015.04.008
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发表时间：
2015-08-01
影响因子：
5.2
通讯作者：
Minguez, M. I.
中科院分区：
文献类型：
--
作者：
Castaneda-Vera, A.;Leffelaar, P. A.;Minguez, M. I.
在农作物保险中，保险公司量化实际风险的准确性在很大程度上取决于实际产量数据的可用性。在没有历史记录的情况下，作物模型可能是生成预期产量数据以进行风险评估的宝贵工具。然而，为特定的目标、地点和实施规模选择作物模型是一项困难的任务。深入了解不同的作物和土壤模块，了解如何获得产出，可能有助于模型的选择。本文的目的是（一）作物模型的有用性进行评估，用于作物保险分析和设计，（二）选择最合适的作物模型在西班牙半干旱地区的干旱风险评估。为此目的，首先，预选作物模型模拟小麦产量在田间规模的种植条件下，其次，四个选定的模型（Aquacrop，CERES小麦，CropSyst和WOFOST）进行了比较的建模方法，过程描述和模型输出。在非限水条件下，四种模型对冬小麦生长的模拟结果基本一致，但在限水条件下，四种模型对产量的模拟结果差异较大。这些差异主要与不同的模拟土壤水分的有效性和假设的联系与干物质的形成。我们的结论是，在这样的半干旱条件下，在田间尺度上的冬小麦生长的模拟，CERES小麦和CropSyst是首选。当土壤详细数据有限时，WOFOST是数据可用性和复杂性之间令人满意的折衷。Aquacrop将生理过程整合到一些代表性参数中，从而减少了输入参数的数量，这在观测数据稀缺时被视为一个优势。然而，该模型对低水可用性的高度敏感性限制了其在所考虑区域的使用。与使用作物模型的集合相反，我们赞成集中精力选择或重建一个模型，其中包括更好地描述它们将被应用的地区的农艺条件的方法。使用诸如作物模型等复杂的方法与许多不确定性来源有关，尽管这些模型是了解这些复杂农艺系统的最佳工具。(C)2015爱思唯尔B.V.保留所有权利。
In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a crop model for a specific objective, location and implementation scale is a difficult task. A look inside the different crop and soil modules to understand how outputs are obtained might facilitate model choice. The objectives of this paper were (i) to assess the usefulness of crop models to be used within a crop insurance analysis and design and (ii) to select the most suitable crop model for drought risk assessment in semi-arid regions in Spain. For that purpose first, a pre-selection of crop models simulating wheat yield under rainfed growing conditions at the field scale was made, and second, four selected models (Aquacrop, CERES-Wheat, CropSyst and WOFOST) were compared in terms of modelling approaches, process descriptions and model outputs. Outputs of the four models for the simulation of winter wheat growth are comparable when water is not limiting, but differences are larger when simulating yields under rainfed conditions. These differences in rainfed yields are mainly related to the dissimilar simulated soil water availability and the assumed linkages with dry matter formation. We concluded that for the simulation of winter wheat growth at field scale in such semi-arid conditions, CERES-Wheat and CropSyst are preferred. WOFOST is a satisfactory compromise between data availability and complexity when detail data on soil is limited. Aquacrop integrates physiological processes in some representative parameters, thus diminishing the number of input parameters, what is seen as an advantage when observed data is scarce. However, the high sensitivity of this model to low water availability limits its use in the region considered. Contrary to the use of ensembles of crop models, we endorse that efforts be concentrated on selecting or rebuilding a model that includes approaches that better describe the agronomic conditions of the regions in which they will be applied. The use of such complex methodologies as crop models is associated with numerous sources of uncertainty, although these models are the best tools available to get insight in these complex agronomic systems. (C) 2015 Elsevier B.V. All rights reserved.