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Grassland Health in Xilin Gol League from the Perspective of Machine Learning—Analysis of Grazing Intensity on Grassland Sustainability

Author

Listed:
  • Zefu Gao

    (Department of Electrical and Optical Engineering, Space Engineering University, Beijing 101400, China
    Key Laboratory of Space Measurement, Operation and Control, Ministry of Education, Beijing 101400, China
    These authors contributed equally to this work.)

  • Qinyu Zhu

    (School of Space Command, Space Engineering University, Beijing 101400, China
    These authors contributed equally to this work.)

  • Haicheng Tao

    (Beijing Space Information Transmission Center, Beijing 101400, China)

  • Yiwen Jiao

    (Department of Electrical and Optical Engineering, Space Engineering University, Beijing 101400, China
    Key Laboratory of Space Measurement, Operation and Control, Ministry of Education, Beijing 101400, China)

Abstract

As one of the most widespread and important types of terrestrial vegetation in the world, grasslands play an irreplaceable role in global climate change. The grasslands of Inner Mongolia, represented by the Xilin Gol League, are typical of Eurasian grasslands and have an important ecological status in the world. In this paper, taking the grassland of Xilin Gol League as the research object, based on the machine learning method, we mainly carry out two aspects of work: the prediction of grassland soil health and evaluation of grassland sustainable development. To address the issue of predicting soil health in grasslands, we focus on an important indicator in grasslands: soil moisture. By analyzing the characteristics of soil moisture time series values and related influencing factors, based on a NAR neural network model, three important factors of soil moisture were predicted: soil evaporation data, average air temperature, and precipitation. Subsequently, the corresponding soil moisture calculation model was trained using regression models based on hyperparameter optimization, and the final predicted soil moisture values were obtained for different months and depths in 2023 and 2024. To evaluate the sustainability of grassland development, we developed a model for the degree of grassland desertification based on the kernel principal component analysis, focusing on three dimensions: environmental factors, surface factors, and human factors. Based on this, a quantitative definition of soil denudation is given by analyzing the main influencing factors of grassland soil degradation. At the same time, a prediction model for the evaluation of soil slumping was established based on a fuzzy comprehensive evaluation matrix, and the evaluation weights of each major factor were given and analyzed. Based on the above research, this paper suggests a reasonable grazing strategy for the grassland areas of the Xilin Gol League: when the grazing intensity is medium and the total number of grazing days is [85, 104] days in a year, the degree of soil slumping and soil desertification in the pastures is minimized. The research results of this paper are useful for the future maintenance and management of the grasslands of Xilin Gol League and other similar areas.

Suggested Citation

  • Zefu Gao & Qinyu Zhu & Haicheng Tao & Yiwen Jiao, 2023. "Grassland Health in Xilin Gol League from the Perspective of Machine Learning—Analysis of Grazing Intensity on Grassland Sustainability," Sustainability, MDPI, vol. 15(4), pages 1-31, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3398-:d:1066667
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    References listed on IDEAS

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