The Relationships Between Extreme Precipitation and Rice and Maize Yields Using Machine Learning in Sichuan Province, China

Main Article Content

Jun Fan*
Attachai Jintrawet
Chanchai Sangchyoswat


Rice and maize are two staple crops that play a critical role in food security in the southwest of China. They are sensitive to extreme climate events such as drought and flood. Therefore, the assessment of the future production of these crops in an era of climate change is essential. However, current assessment tools are time consuming and require extensive input datasets and expert knowledge. This study is an attempt to provide an alternative tool for prefecture level users using an aggregate Z-index which uses precipitation as an input. A machine learning algorithm (Random Forest) was introduced to build and train the model. Finally, future precipitation projected from Global Climate Models (GCMs) was used as an input to assess the future rice and maize yield variations. This tool outperformed other conventional statistical tools and was especially suitable for assessing extreme cases. Crop yields are significantly affected by drought in the study area. Z-indexes derived from three GCMs on decadal time scales were used for assessing future yield variations. Under the lower emission Representative Concentration Pathway (RCP) 4.5, average maize yields and rice yields are likely to be reduced by -0.58% and -1.49%, respectively, over the next three decades in Mianyang prefecture compared to the baseline period. Similarly, under the higher emission (RCP) 8.5, maize yields and rice yields may decrease by
-0.75% and -1.30%, respectively. This assessment tool can be applied in other locations, providing that datasets are available to meet the user’s needs.


Keywords: extreme precipitation; machine learning; climate scenarios; yield forecasting; random forest

*Corresponding author: Tel: 053-221275, Fax: 053-210000



Download data is not yet available.

Article Details

Original Research Articles


Statistical Bureau of Sichuan, 2018. Sichuan Statistical Yearbook. [online] Available at:

Zhan, S., 2017. Riding on self-sufficiency: Grain policy and the rise of agrarian capital in China. Journal of Rural Studies, 54, 151-161.

Huang, J.K., Wei, W., Cui, Q. and Xie, W., 2017. The prospects for China’s food security and imports: Will China starve the world via imports? Journal of Integrative Agriculture, 16(12), 2933-2944.

He, Z.H. and Bonjean, A.P.A., 2010. Cereals in China. [e-book] Mexico, D.F.: CIMMYT. Available through: Sematic Scholar <>

Wu, M.X. and Lu, H.Q., 2016. A modified vegetation water supply index (MVWSI) and its application in drought monitoring over Sichuan and Chongqing, China. Journal of Integrative Agriculture, 15(9), 2132-2141.

Pan, N., Wei, R.J., Zhan, C. and Liang, C., 2017. Applicability analysis of drought indexes in Sichuan Province. South-to-North Water Transfers and Water Science & Technology, 15(4), 71-78. (in Chinese).

Sui, Y., Lang, X.M. and Jiang, D.B., 2018. Projected signals in climate extremes over China associated with a 2°C global warming under two RCP scenarios. International Journal of Climatology, 38(S1), e678-e697.

Easterling, D.R., Kunkel, K.E., Wehner, M.F. and Sun, L., 2016. Detection and attribution of climate extremes in the observed record. Weather and Climate Extremes, 11, 17-27.

Camuffo, D., Valle, A.D. and Becherini, F., 2020. A critical analysis of the definitions of climate and hydrological extreme events. Quaternary International, 538, 5-13.

Cheng, Q., Gao, L., Zuo, X. and Zhong, F., 2019. Statistical analyses of spatial and temporal variabilities in total, daytime, and nighttime precipitation indices and of extreme dry/wet association with large-scale circulations of Southwest China, 1961-2016. Atmospheric Research, 219, 166-182.

Jia, J.Y., Han, L.Y., Liu, Y.F. and He, N., 2016. Drought risk analysis of maize under climate change based on natural disaster system theory in Southwest China. Acta Ecologica Sinica, 36(5), 340-349.

Chen, W.Z., Zhu, D., Huang, C.J. and Ciais, P., 2019. Negative extreme events in gross primary productivity and their drivers in China during the past three decades. Agricultural and Forest Meteorology, 275, 47-58.

Feng, P.Y., Wang, B., Liu, D.L. and Yu, Q., 2019. Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia. Agricultural Systems, 173, 303-316.

Liu, X.F., Pan, Y.Z., Zhu, X.F. and Yang, T.T., 2018. Drought evolution and its impact on the crop yield in the North China Plain. Journal of Hydrology (Amsterdam), 564, 984-996.

He, Q.J., Zhou, G.S., Lü, X.M. and Zhou, M.Z., 2019. Climatic suitability and spatial distribution for summer maize cultivation in China at 1.5 and 2.0 °C global warming. Science Bulletin, 64(10), 690-697.

Xu, C.C., Wu, W.X. and Ge, Q.S., 2018. Impact assessment of climate change on rice yields using the ORYZA model in the Sichuan Basin, China. International Journal of Climatology, 38(7), 2922-2939.

Powell, J.P. and Reinhard, S., 2016. Measuring the effects of extreme weather events on yields. Weather and Climate Extremes, 12, 69-79.

Rötter, R.P., Appiah, M., Fichtler, E. and Kersebaum, K.C., 2018. Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes-A review. Field Crops Research, 221, 142-156.

Harrison, M.T., Cullen, B.R. and Rawnsley, R.P., 2016. Modelling the sensitivity of agricultural systems to climate change and extreme climatic events. Agricultural Systems, 148, 135-148.

Perondi, D., Fraisse, C.W., Staub, C.G. and Cerbaro, V.A., 2019. Crop season planning tool: Adjusting sowing decisions to reduce the risk of extreme weather events. Computers and Electronics in Agriculture, 156, 62-70.

Ma, Z.F., Liu, J., Zhang, S.Q. and Chen, W.X., 2013. Observed climate changes in Southwest China during 1961-2010. Advances in Climate Change Research, 4(1), 30-40.

Xu, C.X., An, W.L., Wang, S.Y.S., Yi, L., Ge, J., Nakatsuka, T., Sano, M. and Guo, Z.T., 2019. Increased drought events in southwest China revealed by tree ring oxygen isotopes and potential role of Indian ocean dipole. Science of the Total Environment, 661, 645-653.

Qi, D.M., Li, Y.Q., Wang, Y. and Deng, M.Y., 2017. Temporal-spatial abnormity characteristics of drought in Sichuan province based on Z index. Journal of Arid Meteorology, 35(5), 734-744. (in Chinese)

Racines, N.C., Tarapues, J., Thornton, P., Jarvis, A., and Villegas, J.R., 2020. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Scientific Data, 7(1), 1-14.

Chlingaryan, A., Sukkarieh, S. and Whelan, B., 2018. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61-69.

Butler, M. and Kazakov, D., 2011. The effects of variable stationarity in a financial time-series on Artificial Neural Networks. Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), Paris, France, April 11-15, 2011, 1-8.

Vogel, E., Donat, M.G., Alexander, L.V., Meinshausen, M., Ray, D.K., Karoly, D., Meinshausen, N. and Frieler, K., 2019. The effects of climate extremes on global agricultural yields. Environmental Research Letters, 14(5), 054010, ab154b

Breiman, L., 2001. Random Forests. Machine Learning, 45, 5-32. [online] Available at:

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V. and Thirion, B., 2011. Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12(2011), 2825-2830.

Ronaghan, S., 2018. The Mathematics of Decision Trees, Random Forest and Feature Importance in Scikit-learn and Spark. [online] Available at: the-mathematics-of-decision-trees-random-forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3

Louppe, G., 2015. Understanding Random Forests: From Theory to Practice. Ph.D. University of Liège.

Jeong, J.H., Resop, J.P., Mueller, N.D. and Fleisher, D.H., 2016. Random forests for global and regional crop yield predictions. Plos One, 11(6), e0156571, journal.pome.o156571

Feng, S.F., Hao, Z.C., Zhang, X. and Hao, F.H., 2019. Probabilistic evaluation of the impact of compound dry-hot events on global maize yields. Science of the Total Environment, 689, 1228-1234.

Aslam, M., Maqbool, M.A. and Cengiz, R., 2015. Effects of drought on maize. In: M.L. Zea, ed. Drought Stress in Maize. Cham: Springer, pp. 5-17.

Crane-Droesch, A., 2018. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environmental Research Letters, 13(11), 114003,

Liu, X.L., Li, X., Dai, C.C. and Zhou, J.Y., 2017. Improved short-term drought response of transgenic rice over-expressing maize C4 phosphoenolpyruvate carboxylase via calcium signal cascade. Journal of Plant Physiology, 218, 206-221.

Wu, H., Hayes, M.J., Wilhite, D.A. and Svoboda, M.D., 2005. The effect of the length of record on the standardized precipitation index calculation. International Journal of Climatology, 25(4), 505-520.

Randall, D.A., Wood, R.A., Bony, S. and Colman, R., 2007. Climate models and their evaluation. In: S. Solomon, D.H. Qin, M. Manning and Z. Chen, eds. Climate Change 2007: The Physical Science Basis. Cambridge and New York: Cambridge University Press, pp. 591-662.