Complex systems modelling of UK winter wheat yield
Complex systems modelling of UK winter wheat yield
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DOI：
10.1016/j.compag.2023.107855
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发表时间：
2023-04-27
影响因子：
8.3
通讯作者：
Hanna，E.
中科院分区：
文献类型：
--
作者：
Hall，R. J.;Wei，H. -L.;Hanna，E.
小麦是全球最重要的作物之一，了解小麦产量的驱动因素具有重大的社会效益。气候变量在决定小麦产量的年际变化方面特别重要，它们要么是直接影响小麦生长阶段的主要因素，要么是通过对病虫害和土壤条件的影响而成为次要因素。本文提出了一种小麦产量模型的新方法；一种基于非线性复杂系统辨识的经验方法，称为NARMAX（非线性自回归移动平均外源输入模型）。我们在英国Rothamsted的一个特定地点部署了NARMAX分析方法，那里有详细的气象变量，以及关于现场条件和作物生长阶段的具体信息。将NARMAX产量预测与WOFOST作物模型和9个最先进的机器学习（ML）模型进行了比较；实验结果表明，NARMAX在预测精度和模型可解释性方面都优于所有比较方法。我们还开发了一种新的网格气象数据产品的区域小麦产量预报。NARMAX方法对一个验证期的洛桑小麦产量进行了熟练的预测（r = 0.78），误差很小。与WOFOST相比，NARMAX区域预报所依据的具体信息较少，但也显示出较高的技能程度（r = 0.73）。此外，为模型选择的预测项是可识别的，可以帮助深入了解在特定地点确定小麦产量所涉及的潜在关键过程。这种方法原则上可以扩展到其他作物类型和位置。它使用有限数量的气象预测变量，可以从基于站点的观测或网格化的气象数据集中获取，实现起来简单而廉价。该方法是了解小麦产量的年度环境驱动因素的新工具。
Wheat is one of the most important global crops, understanding the drivers of wheat yield has significant societal benefits. Climate variables are particularly important in determining interannual variations in wheat yield, either as primary factors which directly influence the stages of wheat growth, or as secondary factors through their influence on pests, diseases and soil conditions. Here we present a new approach to model wheat yield; an empirical method based on nonlinear complex systems identification, known as NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs model). We deploy the NARMAX analytical approach for a specific site, Rothamsted, UK, where detailed meteorological variables are available, together with specific information on site conditions and crop growth stages. NARMAX yield forecasts are compared with those from the WOFOST crop model and nine state-of-the-art machine learning (ML) models; experimental results show that NARMAX outperforms all the compared methods in both prediction accuracy and model interpretability. We also develop regional wheat yield forecasts derived from a new gridded meteorological data product.The NARMAX approach produces skillful forecasts (r = 0.78) of Rothamsted wheat yield for a validation period, with small errors. The NARMAX regional forecasts, based on less specific information than WOFOST, also show a high degree of skill (r = 0.73). In addition, the predictor terms chosen for the model are identifiable and can help to give insight into potential key processes involved in the determination of wheat yield at a specific location. This approach can be extended in principle to other crop types and locations. It is straightforward and inexpensive to implement, using a limited number of meteorological predictor variables, which can be taken from site-based observations, or from gridded meteorological datasets. The method is a new tool to understand the environmental drivers of wheat yields on an annual basis.