Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

中文翻译：

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一种在数据稀疏条件下模拟小麦产量风险状况的方法

基于过程的作物模型是评估气候对作物生产力和种植系统长期生存能力的影响的可靠方法。然而，这些模型需要无法始终满足的高质量气候数据。为了克服这个问题，目前的研究测试了一种简单的方法来扩展每日数据并推断模拟作物产量的长期风险概况。测试了一种极端情况，其中高质量的天气数据只能在一个地点（参考地点：南澳大利亚斯诺敦，南纬 33.78°，东经 138.21°）获得，而该地区 49 个研究地点的天气数据有限。澳大利亚谷物带（横跨北纬 26.67 至 38.02°S，经度 115.44 至 151.85°E）。每日天气数据受到增量因子的干扰，该因子计算为来自参考站点和研究站点的平均气候数据之间的差异。风险概况是使用从最简单（仅调整的降水系列）到最详细（调整的降水、温度和太阳辐射系列）和可变记录长度（从 10 年到 100 年）的逐步组合来构建的）。最简单的调整和最短的记录长度在 41% 的站点中产生了在 -10 和 10% 之间的模拟产量谷物风险概况的偏差，在调整最详细和最长记录（100 年）的研究站点中，这一偏差增加到 86%。结果表明，风险概况外推的质量对所应用的调整次数更敏感，而不是记录长度本身。