Identifying indicators for extreme wheat and maize yield losses
Identifying indicators for extreme wheat and maize yield losses
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DOI：
10.1016/j.agrformet.2016.01.009
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发表时间：
2016-04-15
影响因子：
6.2
通讯作者：
Makowski, David
中科院分区：
文献类型：
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作者：
Ben-Ari, Tamara;Adrian, Juliette;Makowski, David
产量预测一般是以专家知识、调查数据、统计分析和模型模拟为基础的。这些预测一旦公开，就会影响作物价格，并可用于估计季末库存。因此，此类产品的技能和局限性很重要，因为它们为贸易政策提供了信息。在欧洲，通过每月《农业资源监测公报》向利益攸关方提供整个生长季节的产量预测。MARS作物产量预测系统依赖于对过去气候、短期天气预报和作物生长模拟的深入分析。在本文中，我们专注于发生异常低的产量和评估如何准确的农业气候指标和模型输出预测其发生在两个欧洲国家的对比农业气候条件的两种作物品种。重要的是，这里考虑的指标包括一个大范围的复杂程度，其中几个被用来通知在MARS公报中提出的产量预测。每个指标都独立地用于预测1976-2013年生长季节期间法国和西班牙冬小麦和非灌溉谷物玉米异常产量损失（以下称为极端）的发生。指标根据量化其准确区分极端和非极端收益率损失事件的能力的分数进行排名。我们提供并深入分析了我们的排名对极端收益率损失的其他定义的稳健性。没有一个指标系统地表现良好（例如，无论国家或作物种类）但有几个显示出可接受的分数（例如，平均温度、蒸汽压差、降水量、潜在产量）。我们发现指标的复杂程度与其准确性之间没有明显的关系。单一的气候变量，如温度或降水量往往表现以及作物模型集成温度和降水对作物生长的影响。也就是说，月平均最高温度和降水量排名最高的两种作物品种在法国。西班牙的小麦和玉米干旱指数表现良好。我们认为，我们的透明框架可以用于评估和改善全球作物监测系统。(C)© 2016 Elsevier B. V.版权所有。
Yield forecasts are generally based on a combination of expert knowledge, survey data, statistical analyses and model simulations. These forecasts, when public, influence crop prices and can be used to estimate end-of-season stocks. Thus, the skills and limitations of such products are important because they inform trade policies. In Europe, yield forecasts are made available to stakeholders throughout the growing season via the monthly MARS (Monitoring Agricultural ResourceS) Bulletin. The MARS Crop Yield Forecasting System relies on an in-depth analysis of past climate, short-term weather forecasts and crop growth simulations. In this paper, we focus on the occurrence of abnormally low yields and evaluate how accurately agro-climatic indicators and model outputs anticipate their occurrences for two crop species in two European countries of contrasted agroclimatic conditions. Importantly, the indicators considered here encompass a large range of complexity levels and several are used to inform the yield forecasts presented in the MARS Bulletin. Each indicator is independently used to predict the onset occurrence of an abnormal yield loss (henceforth named extreme) in France and in Spain for both winter wheat and non irrigated grain maize for a period covering the 1976-2013 growing seasons. Indicators are ranked based on a score quantifying their ability to accurately separate extreme from non-extreme yield loss events. We provide and in-depth analysis of the robustness of our ranking to alternative definitions of extreme yield loss. No single indicator performs systematically well (e.g., whatever the country or crop species) but several show acceptable scores (e.g., averaged temperatures, vapor pressure deficit, precipitation, potential yield). We find no obvious relationship between the level of complexity of indicators and their accuracy. Single climate variables such as temperature or precipitation often perform as well as a crop model integrating temperatures and precipitation effects on crop growth. Namely, monthly averaged maximum temperatures and precipitation rank highest for both crop species in France. Drought indices perform well in Spain for wheat and for maize. We argue that our transparent framework can be useful to evaluate and improve crop-monitoring systems worldwide. (C) 2016 Elsevier B.V. All rights reserved.