Xu Tianfang, Guan Kaiyu, Peng Bin, Wei Shiqi, Zhao Lei
School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, United States.
College of Agriculture, Consumer, and Environmental Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Front Artif Intell. 2021 May 28;4:647999. doi: 10.3389/frai.2021.647999. eCollection 2021.
Better understanding the variabilities in crop yield and production is critical to assessing the vulnerability and resilience of food production systems. Both environmental (climatic and edaphic) conditions and management factors affect the variabilities of crop yield. In this study, we conducted a comprehensive data-driven analysis in the U.S. Corn Belt to understand and model how rainfed corn yield is affected by climate variability and extremes, soil properties (soil available water capacity, soil organic matter), and management practices (planting date and fertilizer applications). Exploratory data analyses revealed that corn yield responds non-linearly to temperature, while the negative vapor pressure deficit (VPD) effect on corn yield is monotonic and more prominent. Higher mean yield and inter-annual yield variability are found associated with high soil available water capacity, while lower inter-annual yield variability is associated with high soil organic matter (SOM). We also identified region-dependent relationships between planting date and yield and a strong correlation between planting date and the April weather condition (temperature and rainfall). Next, we built machine learning models using the random forest and LASSO algorithms, respectively, to predict corn yield with all climatic, soil properties, and management factors. The random forest model achieved a high prediction accuracy for annual yield at county level as early as in July ( = 0.781) and outperformed LASSO. The gained insights from this study lead to improved understanding of how corn yield responds to climate variability and projected change in the U.S. Corn Belt and globally.
更好地理解作物产量和生产的变异性对于评估粮食生产系统的脆弱性和恢复力至关重要。环境（气候和土壤）条件以及管理因素都会影响作物产量的变异性。在本研究中，我们在美国玉米带进行了全面的数据驱动分析，以了解并建立模型，探究雨养玉米产量如何受到气候变异性和极端事件、土壤特性（土壤有效持水量、土壤有机质）以及管理措施（种植日期和肥料施用）的影响。探索性数据分析表明，玉米产量对温度呈非线性响应，而负水汽压亏缺（VPD）对玉米产量的影响是单调的且更为显著。发现较高的平均产量和年际产量变异性与高土壤有效持水量相关，而较低的年际产量变异性与高土壤有机质（SOM）相关。我们还确定了种植日期与产量之间的区域依赖性关系，以及种植日期与4月天气状况（温度和降雨量）之间的强相关性。接下来，我们分别使用随机森林和套索算法构建机器学习模型，以利用所有气候、土壤特性和管理因素预测玉米产量。随机森林模型早在7月就实现了县级年产量的高预测准确率（ = 0.781），且表现优于套索算法。本研究获得的见解有助于增进对美国玉米带乃至全球范围内玉米产量如何响应气候变异性和预计变化的理解。
