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

Author:

Ellur Rajath1ORCID,Ankappa Ananthakumar Maddur1ORCID,Dharumarajan Subramanian2ORCID,Puttavenkategowda Thimmegowda1,Nanjundegowda Thimmegowda Matadadoddi3ORCID,Sannegowda Prakash Salekoppal4,Pratap Mishra Arun5ORCID,Đurin Bojan6ORCID,Dogančić Dragana7

Affiliation:

1. Zonal Agricultural Research Station, V C Farm, Mandya, University of Agricultural Sciences Bangalore, Gandalu 571405, India

2. National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bengaluru 560024, India

3. Department of Agricultural Meteorology, University of Agricultural Sciences Bangalore, Bengaluru 560065, India

4. College of Agriculture, V C Farm, Mandya, University of Agricultural Sciences Bangalore, Mandya 571405, India

5. Department of Forestry and Remote Sensing, Earthtree Enviro Private Limited, Shillong 793012, India

6. Department of Civil Engineering, University North, 42000 Varaždin, Croatia

7. Faculty of Geotechnical Engineering, University of Zagreb, 42000 Varaždin, Croatia

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 R2 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.

Funder

University North, Croatia

Publisher

MDPI AG

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