IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248655.html
   My bibliography  Save this article

Spatial variability of soil nutrients in seasonal rivers: A case study from the Guo River Basin, China

Author

Listed:
  • Cangyu Li
  • Xinhui Wang
  • Mingzhou Qin

Abstract

Agricultural non-point source pollution refers that substance such as nitrogen and phosphorus cause water environment pollution through surface runoff and underground leakage in agricultural production activities. Water environment pollution related to agricultural non-point source pollution in the Huaihe River Basin is becoming more and more prominent. Therefore, it is necessary to analyze the characteristic of soil nutrient in cultivated land and explore the spatial variation and influencing factors of soil nutrients at the watershed scale. A total of 239 topsoil samples were collected from the Guo river basin, and the related factors of soil organic matter (SOM), total carbon (TC), total nitrogen (TN), total phosphorous (TP), total potassium (TK) and potential of hydrogen (PH) were studied by using descriptive statistics and geostatistical methods. The results showed that TK and PH were weak variation, while SOM, TC, TN and TP were medium variation. Soil pH, TP, TK, TC and SOM had moderate spatial variability, which was caused by both random factors and structural factors such as soil texture, soil type, fertilization and local ecological restoration management. Soil TN showed a strong spatial correlation, mainly due to soil texture and soil type. If the recommended fertilization amount is still given based on the average value of soil nutrients ignoring the spatial heterogeneity, it will not only affect crop production efficiency and fertilizer utilization, but may also cause greater environmental pollution. This study can provide a theoretical basis for the management of agro-ecological environments throughout the basin area.

Suggested Citation

  • Cangyu Li & Xinhui Wang & Mingzhou Qin, 2021. "Spatial variability of soil nutrients in seasonal rivers: A case study from the Guo River Basin, China," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-19, March.
    • Handle: RePEc:plo:pone00:0248655
    DOI: 10.1371/journal.pone.0248655
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248655
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248655&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0248655?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    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. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    2. Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2016. "A model for availability growth with application to new generation offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 83-94.
    3. Zhang, Wei & (Ato) Xu, Wangtu, 2017. "Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 203-230.
    4. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    5. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    6. Song, Zhouzhou & Zhang, Hanyu & Liu, Zhao & Zhu, Ping, 2023. "A two-stage Kriging estimation variance reduction method for efficient time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    9. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation : Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038)," Other publications TiSEM 4915047b-afe4-4fc7-8a1c-4, Tilburg University, School of Economics and Management.
    10. Stephen Ntiri Asomani & Jianping Yuan & Longyan Wang & Desmond Appiah & Kofi Asamoah Adu-Poku, 2020. "The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)," Energies, MDPI, vol. 13(9), pages 1-29, May.
    11. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    12. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    13. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    14. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    15. J.-J. Sinou & L. Nechak & S. Besset, 2018. "Kriging Metamodeling in Rotordynamics: Application for Predicting Critical Speeds and Vibrations of a Flexible Rotor," Complexity, Hindawi, vol. 2018, pages 1-26, March.
    16. Mert Edali, 2022. "Pattern‐oriented analysis of system dynamics models via random forests," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 135-166, April.
    17. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    18. Morales-Enciso, Sergio & Branke, Juergen, 2015. "Tracking global optima in dynamic environments with efficient global optimization," European Journal of Operational Research, Elsevier, vol. 242(3), pages 744-755.
    19. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    20. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0248655. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.