IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i11p1984-d1268832.html
   My bibliography  Save this article

Prediction of Spatial Distribution of Soil Organic Carbon in Helan Farmland Based on Different Prediction Models

Author

Listed:
  • Yuhan Zhang

    (School of Geography and Planning, Ningxia University, Yinchuan 750021, China
    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China)

  • Youqi Wang

    (Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China
    School of Ecology and Environment, Ningxia University, Yinchuan 750021, China)

  • Yiru Bai

    (School of Geography and Planning, Ningxia University, Yinchuan 750021, China
    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China)

  • Ruiyuan Zhang

    (School of Geography and Planning, Ningxia University, Yinchuan 750021, China
    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China)

  • Xu Liu

    (School of Geography and Planning, Ningxia University, Yinchuan 750021, China
    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China)

  • Xian Ma

    (School of Geography and Planning, Ningxia University, Yinchuan 750021, China
    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China)

Abstract

Soil organic carbon (SOC) is widely recognized as an essential indicator of the quality of arable soils and the health of ecosystems. In addition, an accurate understanding of the spatial distribution of soil organic carbon content for precision digital agriculture is important. In this study, the spatial distribution of organic carbon in topsoil was determined using four common machine learning methods, namely the back-propagation neural network model (BPNN), random forest algorithm model (RF), geographically weighted regression model (GWR), and ordinary Kriging interpolation method (OK), with Helan County as the study area. The prediction accuracies of the four different models were compared in conjunction with multiple sources of auxiliary variables. The prediction accuracies for the four models were BPNN (MRE = 0.066, RMSE = 0.257) > RF (MRE = 0.186, RMSE = 3.320) > GWR (MRE = 0.193, RMSE = 3.595) > OK (MRE = 0.198, RMSE = 4.248). Moreover, the spatial distribution trends for the SOC content predicted with the four different models were similar: high in the western area and low in the eastern area of the study region. The BPNN model better handled the nonlinear relationship between the SOC content and multisource auxiliary variables and presented finer information for spatial differentiation. These results provide an important theoretical basis and data support to explore the spatial distribution trend for SOC content.

Suggested Citation

  • Yuhan Zhang & Youqi Wang & Yiru Bai & Ruiyuan Zhang & Xu Liu & Xian Ma, 2023. "Prediction of Spatial Distribution of Soil Organic Carbon in Helan Farmland Based on Different Prediction Models," Land, MDPI, vol. 12(11), pages 1-15, October.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:11:p:1984-:d:1268832
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/11/1984/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/11/1984/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    2. Zihao Wu & Yiyun Chen & Yuanli Zhu & Xiangyang Feng & Jianxiong Ou & Guie Li & Zhaomin Tong & Qingwu Yan, 2023. "Mapping Soil Organic Carbon in Floodplain Farmland: Implications of Effective Range of Environmental Variables," Land, MDPI, vol. 12(6), pages 1-15, June.
    3. Guozhu Ma & Shenghai Cheng & Wenli He & Yixuan Dong & Shaowu Qi & Naimei Tu & Weixu Tao, 2023. "Effects of Organic and Inorganic Fertilizers on Soil Nutrient Conditions in Rice Fields with Varying Soil Fertility," Land, MDPI, vol. 12(5), pages 1-17, May.
    4. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    2. Bharat Prasad Bhandari & Subodh Dhakal & Ching-Ying Tsou, 2024. "Assessing the Prediction Accuracy of Frequency Ratio, Weight of Evidence, Shannon Entropy, and Information Value Methods for Landslide Susceptibility in the Siwalik Hills of Nepal," Sustainability, MDPI, vol. 16(5), pages 1-25, March.
    3. Zhao, Yingrui & Hu, Songhua & Zhang, Ming, 2024. "Evaluating equitable Transit-Oriented development (TOD) via the Node-Place-People model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    4. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    5. Salari, Mahmoud & Javid, Roxana J., 2016. "Residential energy demand in the United States: Analysis using static and dynamic approaches," Energy Policy, Elsevier, vol. 98(C), pages 637-649.
    6. Lu Gan & Yuanyuan Wang & Yusheng Wang & Benjamin Lev & Wenjing Shen & Wen Jiang, 2021. "Coupling coordination analysis with data-driven technology for disaster–economy–ecology system: an empirical study in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2123-2153, July.
    7. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    8. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    9. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    10. Syed Muhammad Raza Abidi & Mushtaq Hussain & Yonglin Xu & Wu Zhang, 2018. "Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    11. Liu, Xiang & Chen, Xiaohong & Tian, Mingshu & De Vos, Jonas, 2023. "Effects of buffer size on associations between the built environment and metro ridership: A machine learning-based sensitive analysis," Journal of Transport Geography, Elsevier, vol. 113(C).
    12. Yaquelin Verenice Pantoja-Pacheco & Armando Javier Ríos-Lira & José Antonio Vázquez-López & José Alfredo Jiménez-García & Martha Laura Asato-España & Moisés Tapia-Esquivias, 2021. "One Note for Fractionation and Increase for Mixed-Level Designs When the Levels Are Not Multiple," Mathematics, MDPI, vol. 9(13), pages 1-20, June.
    13. Radhakrishnan Angamuthu Chinnathambi & Anupam Mukherjee & Mitch Campion & Hossein Salehfar & Timothy M. Hansen & Jeremy Lin & Prakash Ranganathan, 2018. "A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets," Forecasting, MDPI, vol. 1(1), pages 1-21, July.
    14. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    15. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    16. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    17. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
    18. Lawal, Abiola S. & Servadio, Joseph L. & Davis, Tate & Ramaswami, Anu & Botchwey, Nisha & Russell, Armistead G., 2021. "Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators," Applied Energy, Elsevier, vol. 283(C).
    19. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    20. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:11:p:1984-:d:1268832. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.