Search

Rice is the most important staple food in Asia. However, high-spatiotemporal-resolution rice yield datasets are limited over this large region. The lack of such products greatly hinders studies that are aimed at accurately assessing the impacts of climate change and simulating agricultural production. Based on annual rice maps in Asia, we incorporated multisource predictors into three machine learning (ML) models to generate a high-spatial-resolution (4 km) seasonal rice yield dataset (AsiaRiceYield4km) for the 1995–2015 period. Predictors were divided into four categories that considered the most comprehensive rice growth conditions, and the optimal ML model was determined based on an inverse probability weighting method. The results showed that AsiaRiceYield4km achieves good accuracy for seasonal rice yield estimation (single rice: R2=0.88 , RMSE = 920 kg ha -1 ; double rice: R2=0.91 , RMSE = 554 kg ha -1 ; and triple rice: R2=0.93 , RMSE = 588 kg ha -1). Compared with single rice from the Spatial Production Allocation Model (SPAM), the R2 of AsiaRiceYield4km was improved by 0.20, and the RMSE was reduced by 618 kg ha -1 on average. In particular, constant environmental conditions, including longitude, latitude, elevation and soil properties, contributed the most (∼ 45 %) to rice yield estimation. For different rice growth periods, we found that the predictors of the reproductive period had greater impacts on rice yield prediction than those of the vegetative period and the whole growing period. AsiaRiceYield4km is a novel long-term gridded rice yield dataset that can fill the unavailability of high-spatial-resolution seasonal yield products across major rice production areas and promote more relevant studies on agricultural sustainability worldwide. AsiaRiceYield4km can be downloaded from the following open-access data repository: https://doi.org/10.5281/zenodo.6901968 (Wu et al., 2022). [ABSTRACT FROM AUTHOR]