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Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

Author

Listed:
  • Zunfang Liu

    (Department of Geological Engineering, Qinghai University, Xining 810016, China)

  • Haochuan Lei

    (Department of Geological Engineering, Qinghai University, Xining 810016, China)

  • Lei Lei

    (Department of Geological Engineering, Qinghai University, Xining 810016, China)

  • Haiyan Sheng

    (College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China)

Abstract

In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inversion models of partial least squares regression (PLSR), back propagation neural network (BPNN) and support vector machines (SVM) with remote sensing data to predict the TN content in Datong County, Xining City, Qinghai Province, China. The results showed that the average TN content was 1.864 g/kg, and the coefficient of variation (CV) was 30.596%. The prediction accuracy of the SVM model (R 2 = 0.676, RMSE = 0.296) among the three inversion models was higher than that of the BPNN model (R 2 = 0.560, RMSE = 0.305) and the PLSR model (R 2 = 0.374, RMSE = 0.334). The model with the highest accuracy predicted the spatial distribution of TN, and TN content showed a spatial distribution trend which was high in the northwest and low in the southeast, and gradually decreased from north to south. This study provides reference basis and support for soil fertility evaluations and sustainable agricultural development.

Suggested Citation

  • Zunfang Liu & Haochuan Lei & Lei Lei & Haiyan Sheng, 2022. "Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11998-:d:922462
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    References listed on IDEAS

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    1. Rumi Wang & Runyan Zou & Jianmei Liu & Luo Liu & Yueming Hu, 2021. "Spatial Distribution of Soil Nutrients in Farmland in a Hilly Region of the Pearl River Delta in China Based on Geostatistics and the Inverse Distance Weighting Method," Agriculture, MDPI, vol. 11(1), pages 1-12, January.
    2. Wu Xiao & Wenqi Chen & Tingting He & Linlin Ruan & Jiwang Guo, 2020. "Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    3. Pablo L. Peri & Yamina M. Rosas & Brenton Ladd & Santiago Toledo & Romina G. Lasagno & Guillermo Martínez Pastur, 2019. "Modeling Soil Nitrogen Content in South Patagonia across a Climate Gradient, Vegetation Type, and Grazing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
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    Cited by:

    1. Truong Ngoc Cuong & Sam-Sang You & Le Ngoc Bao Long & Hwan-Seong Kim, 2022. "Seaport Resilience Analysis and Throughput Forecast Using a Deep Learning Approach: A Case Study of Busan Port," Sustainability, MDPI, vol. 14(21), pages 1-25, October.

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