Soil Quality Assessment and Its Spatial Variability in an Intensively Cultivated Area in India

Rajath Ellur (), Ananthakumar Maddur Ankappa, Subramanian Dharumarajan, Thimmegowda Puttavenkategowda, Thimmegowda Matadadoddi Nanjundegowda, Prakash Salekoppal Sannegowda, Arun Pratap Mishra, Bojan Đurin () and Dragana Dogančić
Additional contact information
Rajath Ellur: Zonal Agricultural Research Station, V C Farm, Mandya, University of Agricultural Sciences Bangalore, Gandalu 571405, India
Ananthakumar Maddur Ankappa: Zonal Agricultural Research Station, V C Farm, Mandya, University of Agricultural Sciences Bangalore, Gandalu 571405, India
Subramanian Dharumarajan: National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bengaluru 560024, India
Thimmegowda Puttavenkategowda: Zonal Agricultural Research Station, V C Farm, Mandya, University of Agricultural Sciences Bangalore, Gandalu 571405, India
Thimmegowda Matadadoddi Nanjundegowda: Department of Agricultural Meteorology, University of Agricultural Sciences Bangalore, Bengaluru 560065, India
Prakash Salekoppal Sannegowda: College of Agriculture, V C Farm, Mandya, University of Agricultural Sciences Bangalore, Mandya 571405, India
Arun Pratap Mishra: Department of Forestry and Remote Sensing, Earthtree Enviro Private Limited, Shillong 793012, India
Bojan Đurin: Department of Civil Engineering, University North, 42000 Varaždin, Croatia
Dragana Dogančić: Faculty of Geotechnical Engineering, University of Zagreb, 42000 Varaždin, Croatia

Land, 2024, vol. 13, issue 7, 1-22

Abstract: Intensive agricultural practices lead to a deterioration in soil quality, causing a decline in farm productivity and quality, and disturbing the ecosystem balance in command areas. To achieve sustainable production and implement effective soil management strategies, understanding the state and spatial variability of soil quality is essential. The study aims to enhance the understanding of soil quality variability and provide actionable insights for sustainable soil management. In this regard, principal component analysis (PCA) and digital soil mapping were used to assess and map the spatial variability of the soil quality index (SQI) in the Cauvery command area, Mandya district, Karnataka, India. A total of 145 georeferenced soil samples were drawn at 0–15 cm depth and analyzed for physico-chemical properties. PCA was used to reduce the dataset into a minimum dataset as eight important soil indicators and to determine relative weightage factors, which were used for assessing SQI with linear and non-linear scoring methods. For spatial assessment of SQI, the random forest algorithm with environmental covariates was used to map eight soil indicators selected in the minimum dataset. The soil property maps were subjected to linear and non-linear scoring, followed by multiplying with corresponding weightage factors and summation to produce SQI maps. Results reveal that values of SQI calculated using linear scoring, range from 0.10 to 0.64, with a mean of 0.39, while non-linear scoring exhibits a wider range of 0.12 to 0.78 and a mean of 0.48. With a slight higher sensitivity index of 6.5, non-linear scoring proved to be the better scoring method compared to linear scoring. Spatial assessment shows that the R 2 and LCC between the calculated and predicted SQI were higher for non-linear scoring (0.66 and 0.66) compared to linear scoring (0.60 and 0.65). The SQI maps reveal high spatial variability with more than 40 percent of soils classified as moderate-to-low index. The soils with low SQI were distributed in eastern parts, whereas western parts exhibited high-to-very-high soil quality. To achieve production goals and improve soil quality in the eastern region, sustainable soil and crop management strategies must be developed, and their effects on soil quality should be assessed.

Keywords: soil quality assessment; spatial variability; intensively cultivated area; principal component analysis; digital soil mapping (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/7/970/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/7/970/ (text/html)

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:gam:jlands:v:13:y:2024:i:7:p:970-:d:1427139

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().