Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors

Reliable prediction for crop yield is crucial for economic planning, food security monitoring, and agricultural risk management. This study aims to develop a crop yield forecasting model at large spatial scales using meteorological variables closely related to crop growth. The influence of climate patterns on agricultural productivity can be spatially inhomogeneous due to local soil and environmental conditions. We propose a Bayesian spatially varying functional model (BSVFM) to predict county-level corn yield for five Midwestern states, based on annual precipitation and daily maximum and minimum temperature trajectories modeled as multivariate functional predictors. The proposed model accommodates spatial correlation and measurement errors of functional predictors, and respects the spatially heterogeneous relationship between the response and associated predictors by allowing the functional coefficients to vary over space. The model also incorporates a Bayesian variable selection device to further expand its capacity to accommodate spatial heterogeneity. The proposed method is demonstrated to outperform other highly competitive methods in corn yield prediction, owing to the flexibility of allowing spatial heterogeneity with spatially varying coefficients in our model. Our study provides further insights into understanding the impact of climate change on crop yield. Supplementary materials for this article are available online.

对作物产量进行可靠预测，对于经济规划、粮食安全监测以及农业风险管理均具有至关重要的意义。本研究旨在利用与作物生长密切相关的气象变量，构建适用于大空间尺度的作物产量预测模型。由于局地土壤与环境条件的差异，气候型对农业生产力的影响存在空间异质性。我们提出贝叶斯空间变系数泛函模型（Bayesian spatially varying functional model, BSVFM），基于被建模为多变量泛函预测因子的年降水量与逐日最高、最低气温序列，对美国中西部五个州的县级玉米产量进行预测。所提模型能够适配泛函预测因子的空间相关性与测量误差，并通过允许泛函系数随空间变化，刻画响应变量与关联预测因子之间的空间异质性关系。该模型还集成了贝叶斯变量选择机制，进一步拓展了其适配空间异质性的能力。由于本模型允许泛函系数随空间变化以适配空间异质性的灵活性，所提方法在玉米产量预测任务中被证明优于其他极具竞争力的基准模型。本研究为理解气候变化对作物产量的影响提供了进一步的理论视角。本文的补充材料可在线获取。