Abstract
Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.
Highlights
Wheat (Triticum aestivum L.) is the leading cereal grain in the world, providing the most calories and protein for world food supply [1,2]
Extensive studies have focused on crop yield prediction at different spatial scales by either using statistical regression models or crop models in various countries [13,14,15,16]
Given the spectral indices can dynamically capture crop growing conditions through various combinations, and these various products have commonly shared and complementary information to contribute to yield prediction, we focused on three vegetation indices (VIs) associated strongly with yield: EVI, GCVI, and Normalized Difference Vegetation Index (NDVI), to determine their contributions to wheat yield prediction in China [43,44,45,46]
Summary
Wheat (Triticum aestivum L.) is the leading cereal grain in the world, providing the most calories and protein for world food supply [1,2]. And reliable crop yield prediction has increasingly become one of the key issues for food security, supply chain planning of agriculture industry, and market prediction for the entire population [9,10]. Such yield estimations will help farmers to make informed management and financial decisions in advance [11,12]. Extensive studies have focused on crop yield prediction at different spatial scales by either using statistical regression models or crop models in various countries [13,14,15,16]. The problems associated with the above two methods highlight the need for another novel approach with an ability to provide timely, reliable, and cost-effective wheat yield prediction
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