IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i10p1615-d1492517.html
   My bibliography  Save this article

Exploring the Effect of Sampling Density on Spatial Prediction with Spatial Interpolation of Multiple Soil Nutrients at a Regional Scale

Author

Listed:
  • Prava Kiran Dash

    (Department of Soil Science, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar 751003, Odisha, India
    Regional Research and Technology Transfer Station, Mahisapat, Odisha University of Agriculture and Technology, Bhubaneswar 759013, Odisha, India)

  • Bradley A. Miller

    (Department of Agronomy, College of Agriculture and Life Sciences, Iowa State University, Ames, IA 50010, USA)

  • Niranjan Panigrahi

    (Center for Water, Environment, and Development, Cranfield University, Cranfield MK43 0AL, UK)

  • Antaryami Mishra

    (Department of Soil Science, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar 751003, Odisha, India
    Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Sciences, Siksha ‘O’ Anusandhan University, Bhubaneswar 751029, Odisha, India)

Abstract

Essential soil nutrients are dynamic in nature and require timely management in farmers’ fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km 2 located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R 2 of model fit. Lin’s concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R 2 of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents.

Suggested Citation

  • Prava Kiran Dash & Bradley A. Miller & Niranjan Panigrahi & Antaryami Mishra, 2024. "Exploring the Effect of Sampling Density on Spatial Prediction with Spatial Interpolation of Multiple Soil Nutrients at a Regional Scale," Land, MDPI, vol. 13(10), pages 1-24, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1615-:d:1492517
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/10/1615/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/10/1615/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Junxiao Wang & Xiaorui Wang & Shenglu Zhou & Shaohua Wu & Yan Zhu & Chunfeng Lu, 2016. "Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations," IJERPH, MDPI, vol. 13(10), pages 1-12, September.
    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. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    2. Xinyu Hu & Chun Dong & Yu Zhang, 2024. "Impacts of Cropland Utilization Patterns on the Sustainable Use Efficiency of Cropland Based on the Human–Land Perspective," Land, MDPI, vol. 13(6), pages 1-27, June.

    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:13:y:2024:i:10:p:1615-:d:1492517. 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.