Soil Particle Prediction using Spatial Ordinary Logistic Regression

Henny Pramoedyo, Atiek Iriany, Wigbertus Ngabu and Sativandi Riza
Additional contact information
Henny Pramoedyo: Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia
Atiek Iriany: Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia
Wigbertus Ngabu: Statistics Studies program, Faculty of Mathematics and Natural Sciences, University of San Pedro, Kupang, Indonesia
Sativandi Riza: Department of Soil, Faculty of Agriculture, Brawijaya University, Malang, Indonesia

Advances in Decision Sciences, 2024, vol. 28, issue 2, 66-92

Abstract: [Purpose] This study embarks on a novel journey to create a predictive model for soil texture. We utilize the Spatial Ordinary Logistic Regression model to estimate soil particles in the topsoil with unprecedented accuracy. This involves employing Geographically Weighted Ordinary Logistic Regression to analyze and map the spatial distribution of these particles based on primary data collected from the field. [Design/methodology/approach] This study distinguishes itself by adopting a meticulous approach to gathering soil particulate and geospatial data from various random locations. This method is crucial in addressing the complexity of modelling soil texture, an essential aspect of soil management. The study analyzes soil texture, a combination of sand, silt, and clay, using Digital Elevation Model (DEM) data. By leveraging topographical variations, the study predicts soil texture, employing Geographically Weighted Ordinary Logistic Regression for areas without direct observations. This approach significantly enhances both understanding and prediction in soil science. [Findings] The proposed model will be cross-validated to ensure precision. Aimed at aiding land and resource management, this study focuses on examining spatial variations in topsoil particle sizes and their influencing factors. The Geographic Weighted Ordinary Logistic Regression (GWOLR) model, designed for estimating soil particle sizes using a fixed bi-square weight, demonstrated superior effectiveness with a 90% accuracy rate compared to the standard model’s 88%. Further findings show that all topographical predictors exhibit significant spatial autocorrelation (Moran’s I, p

Keywords: Soil; Spatial; Logistic Regression (search for similar items in EconPapers)
JEL-codes: C21 C31 Q15 Q24 R12 R14 R32 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://iads.site/soil-particle-prediction-using-s ... logistic-regression/
https://iads.site/wp-content/uploads/2025/06/Soil- ... istic-Regression.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aag:wpaper:v:28:y:2024:i:2:p:66-92

Access Statistics for this article

More articles in Advances in Decision Sciences from Asia University, Taiwan Contact information at EDIRC.
Bibliographic data for series maintained by Vincent Pan ().