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

Prava Kiran Dash (), Bradley A. Miller, Niranjan Panigrahi and Antaryami Mishra
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Prava Kiran Dash: Department of Soil Science, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar 751003, 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

Land, 2024, vol. 13, issue 10, 1-24

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.

Keywords: nutrient management; spatial interpolation; kriging; semi-variograms; sampling density; prediction accuracy; maps (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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