Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors
Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors
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DOI：
10.1080/01621459.2022.2123333
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发表时间：
2022-09
影响因子：
3.7
通讯作者：
Yeonjoo Park;Bo Li;Yehua Li
中科院分区：
文献类型：
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作者：
Yeonjoo Park;Bo Li;Yehua Li
可靠的作物产量预测对经济规划、粮食安全监测和农业风险管理至关重要。本研究旨在利用与作物生长密切相关的气象变量建立大空间尺度的作物产量预测模型。由于当地土壤和环境条件的不同，气候模式对农业生产力的影响在空间上是不均匀的。本文提出了一个贝叶斯空间变化功能模型（BSVFM）来预测中西部5个州的县级玉米产量，该模型基于年降水量和日最高和最低温度轨迹作为多变量函数预测因子。该模型考虑了功能预测因子的空间相关性和测量误差，并通过允许功能系数随空间变化而尊重响应与相关预测因子之间的空间异质性关系。该模型还结合了贝叶斯变量选择装置，以进一步扩大其适应空间异质性的能力。结果表明，该方法在玉米产量预测方面优于其他竞争激烈的方法，因为我们的模型可以灵活地考虑空间异质性和空间变化系数。我们的研究为理解气候变化对作物产量的影响提供了进一步的见解。本文的补充材料可在网上获得。
Abstract 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.