Data Science for Weather Impacts on Crop Yield
Data Science for Weather Impacts on Crop Yield
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DOI：
10.3389/fsufs.2020.00052
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发表时间：
2020-05-19
影响因子：
4.7
通讯作者：
Ganguly, Auroop R.
中科院分区：
文献类型：
--
作者：
Konduri, Venkata Shashank;Vandal, Thomas J.;Ganguly, Auroop R.
食品、能源和零售等行业的私营企业以及公共部门和联邦机构都对预测了解天气对作物产量的影响感兴趣，这是粮食安全的一个重要方面。科学文献主要研究作物产量如何受到季节平均天气指数的影响。虽然有几项研究在分析中确实考虑了极端天气，但其范围要么限于衡量极端天气与产量的条件关系，要么考虑的极端事件类型有限。回归模型的选择，无论是更常用的线性方法还是非线性方法，在这方面都没有得到适当的证明。在这里，我们开发了数据驱动的方法来检查两个相互关联的假设，以提高科学理解和增强预测建模。第一个假设，极端天气指数有统计上显着的信息内容，在他们被发现是有效的线性和非线性方法的基础上成对依赖。第二个假设，检查非线性回归方法的附加值，并建议线性方法可能是不够的。这项研究的结果可以为气候对作物产量影响背景下的指数和端到端风险评估系统的科学理解、生成和相关性提供信息。一个直接的应用可能是在美国航天局地球交换项目的范围内，该项目利用从卫星获得的大量数据集和模型输出，促进产生和传播与影响有关的气象数据和指数。
Private businesses in sectors, such as food, energy, and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season-averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.