Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China
KAUST DepartmentBiological and Environmental Science and Engineering (BESE) Division
Earth System Observation and Modelling
Environmental Science and Engineering Program
Water Desalination and Reuse Research Center (WDRC)
Embargo End Date2024-06-18
Permanent link to this recordhttp://hdl.handle.net/10754/679595
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AbstractThe accurate and timely prediction of crop yield at a large scale is important for food security and the development of agricultural policy. An adaptable and robust method for estimating maize yield for the entire territory of China, however, is currently not available. The inherent trade-off between early estimates of yield and the accuracy of yield prediction also remains a confounding issue. To explore these challenges, we employ indicators such as GPP, ET, surface temperature (Ts), LAI, soil properties and maize phenological information with random forest regression (RFR) and gradient boosting decision tree (GBDT) machine learning approaches to provide maize yield estimates within China. The aims were to: (1) evaluate the accuracy of maize yield prediction obtained from multimodal data analysis using machine-learning; (2) identify the optimal period for estimating yield; and (3) determine the spatial robustness and adaptability of the proposed method. The results can be summarized as: (1) RFR estimated maize yield more accurately than GBDT; (2) Ts was the best single indicator for estimating yield, while the combination of GPP, Ts, ET and LAI proved best when multi-indicators were used (R2 = 0.77 and rRMSE = 16.15% for the RFR); (3) the prediction accuracy was lower with earlier lead time but remained relatively high within at least 24 days before maturity (R2 > 0.77 and rRMSE <16.92%); and (4) combining machine-learning algorithms with multi-indicators demonstrated a capacity to cope with the spatial heterogeneity. Overall, this study provides a reliable reference for managing agricultural production.
CitationCheng, M., Penuelas, J., McCabe, M. F., Atzberger, C., Jiao, X., Wu, W., & Jin, X. (2022). Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agricultural and Forest Meteorology, 323, 109057. https://doi.org/10.1016/j.agrformet.2022.109057
SponsorsThis research was supported by the National Key Research and Development Program of China (grant 2021YFD1201602), National Natural Science Foundation of China (Grant No. 42071426, 51922072, 51779161, 51009101), and Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Agricultural Sciences (Grant Nos. Y2020YJ07), the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences, Hainan Yazhou Bay Seed Lab (B21HJ0221), and Special Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu, China (CX(21)3065).