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Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning

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  • Yuexia Sun

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Shuai Zhang

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fulu Tao

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    Natural Resources Institute Finland (Luke), 00790 Helsinki, Finland)

  • Rashad Aboelenein

    (Barley Research Department, Field Crops Research Institute, Agricultural Research Center, Giza 583121, Egypt)

  • Alia Amer

    (Medicinal and Aromatic Plants Department, Horticulture Research Institute, Agricultural Research Center, Giza 583121, Egypt)

Abstract

To meet the challenges of climate change, population growth, and an increasing food demand, an accurate, timely and dynamic yield estimation of regional and global crop yield is critical to food trade and policy-making. In this study, a machine learning method (Random Forest, RF) was used to estimate winter wheat yield in China from 2014 to 2018 by integrating satellite data, climate data, and geographic information. The results show that the yield estimation accuracy of RF is higher than that of the multiple linear regression method. The yield estimation accuracy can be significantly improved by using climate data and geographic information. According to the model results, the estimation accuracy of winter wheat yield increases dramatically and then flattens out over months; it approached the maximum in March, with R 2 and RMSE reaching 0.87 and 488.59 kg/ha, respectively; this model can achieve a better yield forecasting at a large scale two months in advance.

Suggested Citation

  • Yuexia Sun & Shuai Zhang & Fulu Tao & Rashad Aboelenein & Alia Amer, 2022. "Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning," Agriculture, MDPI, vol. 12(5), pages 1-16, April.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:571-:d:796878
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    References listed on IDEAS

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    1. Nendel, C. & Berg, M. & Kersebaum, K.C. & Mirschel, W. & Specka, X. & Wegehenkel, M. & Wenkel, K.O. & Wieland, R., 2011. "The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics," Ecological Modelling, Elsevier, vol. 222(9), pages 1614-1625.
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