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Comprehensive Analysis of Grain Production Based on Three-Stage Super-SBM DEA and Machine Learning in Hexi Corridor, China

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  • Zhengxiao Yan

    (Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA)

  • Wei Zhou

    (College of Agriculture and Food Science, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA)

  • Yuyi Wang

    (Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Weijin Road 92, Tianjin 300072, China
    Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Weijin Road 92, Tianjin 300072, China)

  • Xi Chen

    (Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Weijin Road 92, Tianjin 300072, China
    Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Weijin Road 92, Tianjin 300072, China)

Abstract

Food security is always a pressing agenda worldwide. The grain production in many areas has decreased due to the reduction in agricultural research funding and infrastructure investment. In this paper, we employed the Extreme-Tree algorithm to determine the main effectors in grain production in Hexi Corridor, Gansu, China, during 2002–2018. First, we applied the three-stage super-SBM DEA to precisely assess agricultural production. Then, we used the Extremely randomized trees algorithm to quantify the importance of each factor. Our results show that the variant of average efficiency score at the first stage was minimal. After removing the influence of environmental factors on production efficiency, the more accurate efficiency score was decreasing from 2002 to 2018. The R 2 value of the Extra-Tree model was 0.989 in the grain production analysis. Our research shows that grain production in the Hexi Corridor was controlled by human-driven but not nature-driven during our research period. Based on the importance attribution analysis of each model, it showed that the importance of human-driven investment occupied 93.7% of grain production. The importance of nature-driving was about 6.3%. Accordingly, we proposed corresponding opinions and suggestions to government and growers.

Suggested Citation

  • Zhengxiao Yan & Wei Zhou & Yuyi Wang & Xi Chen, 2022. "Comprehensive Analysis of Grain Production Based on Three-Stage Super-SBM DEA and Machine Learning in Hexi Corridor, China," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8881-:d:867126
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