Delineation and evaluation of management zones for site-specific nutrient management using a geostatistical and fuzzy C mean cluster approach
Abstract Expansive soil spatial variability plays a key role in the over- and under-application of fertilizers, contributing to environmental pollution. Assess soil variability and delineate it into management zones to adopt site-specific nutrient management for balanced fertilization and sustainabl...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-07283-0 |
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| Summary: | Abstract Expansive soil spatial variability plays a key role in the over- and under-application of fertilizers, contributing to environmental pollution. Assess soil variability and delineate it into management zones to adopt site-specific nutrient management for balanced fertilization and sustainable agriculture. To assess spatial variability by geostatistical methods and delineate and evaluate nutrient management zones for site-specific nutrient management and variable rate fertilizer application using fuzzy c-means clustering. Overall, 200 soil samples (0–15 cm depth) with geographical coordinates were collected with a grid size of 14.2 m × 14.2 m from a 4-ha maize cultivated 4-ha of Mahagoan village of Bhainsa Mandal, Nirmal district, Telangana, India. The collected samples were tested with different reagents to determine the soil reaction and available nutrient status. Soil spatial variability was assessed by the geostatistical method, and delineation of nutrient management zones was carried out by integrating principal component analysis and fuzzy c-means clustering. Geostatistical analysis revealed spherical (pH, electrical conductivity, organic carbon, available sulfur, and available Zn) and Gaussian (available nitrogen, available P2O5, available K2O, available Fe, available Zn, and available Cu) as the best-fit semivariogram model with strong spatial dependence. Five management zones were delineated by principal component analysis and fuzzy c-means clustering based on fuzzy performance index (FPI) and normalized classification entropy (NCE) indices. Variable rates of fertilizer recommendations in different management zones were calculated using a soil test crop response equation. The results show the highest grain yield and fertilizer saving in MZ−5, followed by MZ−4, MZ−3, MZ−2, and MZ−1, compared to farmer fertilizer practices. The study aims to delineate the management zone to reduce fertilizer application, ensure balanced fertilizer application, minimize environmental pollution, and increase crop grain yield and profitability. |
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| ISSN: | 2045-2322 |