Sun, Q.; Zhang, Y.; Che, X.; Chen, S.; Ying, Q.; Zheng, X.; Feng, A.
Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields. Agriculture 2022, 12, 1791.
https://doi.org/10.3390/agriculture12111791
AMA Style
Sun Q, Zhang Y, Che X, Chen S, Ying Q, Zheng X, Feng A.
Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields. Agriculture. 2022; 12(11):1791.
https://doi.org/10.3390/agriculture12111791
Chicago/Turabian Style
Sun, Qing, Yi Zhang, Xianghong Che, Sining Chen, Qing Ying, Xiaohui Zheng, and Aixia Feng.
2022. "Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields" Agriculture 12, no. 11: 1791.
https://doi.org/10.3390/agriculture12111791
APA Style
Sun, Q., Zhang, Y., Che, X., Chen, S., Ying, Q., Zheng, X., & Feng, A.
(2022). Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields. Agriculture, 12(11), 1791.
https://doi.org/10.3390/agriculture12111791
