Using a cropping system model at regional scale: Low-data approaches for crop management information and model calibration
Using a cropping system model at regional scale: Low-data approaches for crop management information and model calibration
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DOI：
10.1016/j.agee.2010.05.007
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发表时间：
2011-07
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影响因子：
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通讯作者：
O. Thérond;H. Hengsdijk;É. Casellas;D. Wallach;M. Adam;H. Belhouchette;R. Oomen;Graham Russell;F. Ewert;J. Bergez;S. Janssen;J. Wery;M. V. van Ittersum
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文献类型：
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作者：
O. Thérond;H. Hengsdijk;É. Casellas;D. Wallach;M. Adam;H. Belhouchette;R. Oomen;Graham Russell;F. Ewert;J. Bergez;S. Janssen;J. Wery;M. V. van Ittersum
种植制度模型是区域影响评价的有力工具，但其对大型异质区域的输入数据要求很难满足。因此，本文的目的是提供低数据方法，以确定耕作制度模型所需的详细管理数据，并校准适用于欧洲联盟(EU)12个地区的默认作物参数。针对不同的数据类型应用了各种缩小和扩大尺度的程序来满足这两个目标。农业生产和外部性模拟器(APES)模型用于说明目的。将易于收集的区域作物管理信息和专家知识相结合，能够制定通用的、基于专家的规则，以指定作物管理。这些基于专家的管理规则对模拟产量和氮素淋失的影响以APE为例进行了说明。用默认作物物候学参数模拟的籽粒玉米、软小麦和硬粒小麦的产量与12个欧盟地区的作物产量进行了比较。模拟产量的精确度参差不齐，但总体来说很差。根据三种作物在每个地区的平均播种日期和收获期的温度和，制定了一个区域校准系数Kheno。应用这个校准因子在所有情况下都提高了模拟产量。结果表明，有可能制定基于专家的管理规则，并通过使用所提出的低数据方法来捕捉整个欧盟的产量变化。
Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches.