Geostatistical analysis of soil fertility and land resource inventory - Guided nutrient management strategies for improving soil health and agricultural productivity in the Bankanahalli micro-watershed

Soil fertility variability is an important factor that affects nutrient use efficiency and crop productivity in varied landscapes. This variability stands out in semi-arid regions. Here, using general fertilizer recommendations often causes nutrient imbalances, lowers soil health and results in lower crop yields. In this context, present study was conducted in Bankanahalli micro-watershed of Mandya district of Karnataka state to evaluate the spatial variability of soil physico-chemical properties and develop site-specific nutrient management strategies. A total of 45 gridbased soil samples were collected and analysed for pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P₂O₅) and potassium (K₂O). Geostatistical approaches, including semivariogram modelling and kriging interpolation, were employed to generate spatial variability maps. Best-fitted semivariogram models were identified using RMSE, nugget/sill ratio and range values, highlighting strong to moderate spatial dependence across nutrients. The results revealed that soil pH ranged from slightly alkaline to strongly alkaline, while organic carbon (OC) content ranged from low to medium. Available nitrogen (N) and potassium (K₂O) were deficient, whereas phosphorus (P₂O₅) was relatively abundant across the study area. A correlation matrix and principal component analysis (PCA) biplot were developed to examine interrelationships among soil parameters. Spatial maps identified nutrient-deficient and nutrient-rich zones, while correlation and PCA analyses showed strong relationships of pH and EC with K₂O and OC with N availability. Based on these outputs, nutrient management strategies were developed under Low-High (L-M-H) and very low-very high (VL-L-M-H-VH) classification schemes, demonstrating significant potential for fertilizer savings and improved input efficiency. Integrating spatial variability analysis with land resource inventory (LRI)-based nutrient management strategies, along with green manuring and mulching, was effective in maintaining soil health and boosting crop productivity within the micro-watershed.

Authors

Institutions

Publication Details

Journal
Plant Science Today
Published
2026-01-04
DOI
https://doi.org/10.14719/pst.11700
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Geostatistical analysis of soil fertility and land resource inventory - Guided nutrient management strategies for improving soil health and agricultural productivity in the Bankanahalli micro-watershed

P S Fathima, A Sathish, S B Yogananda, K S Girish et al.
Plant Science Today
Soil Geostatistics and Mapping
article

Geostatistical analysis of soil fertility and land resource inventory - Guided nutrient management strategies for improving soil health and agricultural productivity in the Bankanahalli micro-watershed

P S Fathima, A Sathish, S B Yogananda, K S Girish, P Thimmegowda, M A Ananthakumar
article en

Abstract

Soil fertility variability is an important factor that affects nutrient use efficiency and crop productivity in varied landscapes. This variability stands out in semi-arid regions. Here, using general fertilizer recommendations often causes nutrient imbalances, lowers soil health and results in lower crop yields. In this context, present study was conducted in Bankanahalli micro-watershed of Mandya district of Karnataka state to evaluate the spatial variability of soil physico-chemical properties and develop site-specific nutrient management strategies. A total of 45 gridbased soil samples were collected and analysed for pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P₂O₅) and potassium (K₂O). Geostatistical approaches, including semivariogram modelling and kriging interpolation, were employed to generate spatial variability maps. Best-fitted semivariogram models were identified using RMSE, nugget/sill ratio and range values, highlighting strong to moderate spatial dependence across nutrients. The results revealed that soil pH ranged from slightly alkaline to strongly alkaline, while organic carbon (OC) content ranged from low to medium. Available nitrogen (N) and potassium (K₂O) were deficient, whereas phosphorus (P₂O₅) was relatively abundant across the study area. A correlation matrix and principal component analysis (PCA) biplot were developed to examine interrelationships among soil parameters. Spatial maps identified nutrient-deficient and nutrient-rich zones, while correlation and PCA analyses showed strong relationships of pH and EC with K₂O and OC with N availability. Based on these outputs, nutrient management strategies were developed under Low-High (L-M-H) and very low-very high (VL-L-M-H-VH) classification schemes, demonstrating significant potential for fertilizer savings and improved input efficiency. Integrating spatial variability analysis with land resource inventory (LRI)-based nutrient management strategies, along with green manuring and mulching, was effective in maintaining soil health and boosting crop productivity within the micro-watershed.

Plant Science TodayVol. 12(sp4)
University of Agricultural Sciences, Dharwad (IN), University of Agricultural Sciences, Bangalore (IN), Seva Mandir (IN), Experimental Station (US), Indian Agricultural Research Institute (IN)
Openalex Percentile: Top 98%
Soil Geostatistics and Mapping
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.