Exploring the effect of sampling density on spatial prediction with spatial interpolation of multiple soil nutrients at a regional scale

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dc.contributor.authorDash, Prava Kiran
dc.contributor.authorMiller, Bradley A.
dc.contributor.authorPanigrahi, Niranjan
dc.contributor.authorMishra, Antaryami
dc.date.accessioned2025-01-10T11:34:19Z
dc.date.available2025-01-10T11:34:19Z
dc.date.freetoread2025-01-10
dc.date.issued2024-10-01
dc.date.pubOnline2024-10-04
dc.description.abstractEssential 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 km2 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 R2 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, R2 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.
dc.description.journalNameLand
dc.description.sponsorshipDepartment of Science and Technology, Philippine American Educational Foundation, United States Department of State
dc.identifier.citationDash PK, Miller BA, Panigrahi N, Mishra A. (2024) Exploring the effect of sampling density on spatial prediction with spatial interpolation of multiple soil nutrients at a regional scale. Land, Volume 13, Issue 10, October 2024, Article number 1615
dc.identifier.eissn2073-445X
dc.identifier.elementsID554857
dc.identifier.issn2073-445X
dc.identifier.issueNo10
dc.identifier.paperNo1615
dc.identifier.urihttps://doi.org/10.3390/land13101615
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23353
dc.identifier.volumeNo13
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2073-445X/13/10/1615
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectnutrient management
dc.subjectspatial interpolation
dc.subjectkriging
dc.subjectsemi-variograms
dc.subjectsampling density
dc.subjectprediction accuracy
dc.subjectmaps
dc.subject41 Environmental Sciences
dc.subject4106 Soil Sciences
dc.subject3301 Architecture
dc.subject3304 Urban and regional planning
dc.subject4104 Environmental management
dc.titleExploring the effect of sampling density on spatial prediction with spatial interpolation of multiple soil nutrients at a regional scale
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-29

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