Assessment of Land Use Systems on for Soil Quality in the Semi-A Arid Region, of Bengaluru, India

Understanding the impact of diverse land-use systems (LUS) on soil quality is crucial for sustainable land management practices. This study was conducted in Bengaluru, India, to estimate the soil quality index (SQI) under different LUSs. Twenty-four sampling sites were identified in four different LUSs across the Bangaluru, and soil samples were collected monthly over five months during the Rabi cropping season of 2020-2021. The soil quality assessment involved selecting the minimum data set (MDS) via principal component analysis (PCA) and correlation, scoring soil indicators, and combining these scores to create the soil quality index (SQI). PCA was used to identify key soil properties, which included soil organic carbon (SOC), pH, dehydrogenase, nitrogen (N), and urease, for different LUSs derived from the MDS. The SQI was highest in the horticulture cropping system (0.58), followed by the agro + horticulture cropping system (0.53) and the vegetable cropping system (0.49), and lowest in the pulse cropping system (0.44). These findings emphasize the importance of sustainable land management practices to preserve and boost soil quality across cropping systems.

Authors

Institutions

Publication Details

Journal
Journal of Geography Environment and Earth Science International
Published
2024-09-05
DOI
https://doi.org/10.9734/jgeesi/2024/v28i9810
Citations
1
Primary Topic
Soil and Land Suitability Analysis
Type
article
Field-Weighted Citation Impact
0.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessment of Land Use Systems on for Soil Quality in the Semi-A Arid Region, of Bengaluru, India

Karan Sathish, A. Kokila, A. Vishnu Moorthy, Govindaraju Shruthi et al.
1 citations
Journal of Geography Environment and Earth Science International
Soil and Land Suitability Analysis
0.71
article

Assessment of Land Use Systems on for Soil Quality in the Semi-A Arid Region, of Bengaluru, India

Karan Sathish, A. Kokila, A. Vishnu Moorthy, Govindaraju Shruthi, Saleemali Kannihalli, Narayanaswamy Jeevan, C. Vairavan, Shweta Saraswat, Chethan Kumar K B, D Thrilekha, V Kousalya, M. A. Shankar, A. S. Devakumar
article en
1 citations

Abstract

Understanding the impact of diverse land-use systems (LUS) on soil quality is crucial for sustainable land management practices. This study was conducted in Bengaluru, India, to estimate the soil quality index (SQI) under different LUSs. Twenty-four sampling sites were identified in four different LUSs across the Bangaluru, and soil samples were collected monthly over five months during the Rabi cropping season of 2020-2021. The soil quality assessment involved selecting the minimum data set (MDS) via principal component analysis (PCA) and correlation, scoring soil indicators, and combining these scores to create the soil quality index (SQI). PCA was used to identify key soil properties, which included soil organic carbon (SOC), pH, dehydrogenase, nitrogen (N), and urease, for different LUSs derived from the MDS. The SQI was highest in the horticulture cropping system (0.58), followed by the agro + horticulture cropping system (0.53) and the vegetable cropping system (0.49), and lowest in the pulse cropping system (0.44). These findings emphasize the importance of sustainable land management practices to preserve and boost soil quality across cropping systems.

Journal of Geography Environment and Earth Science InternationalVol. 28(9)
Tamil Nadu Agricultural University (IN)
Openalex Percentile: Top 22%
Soil and Land Suitability Analysis
0.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.