Delineation and evaluation of management zones for site-specific nutrient management using a geostatistical and fuzzy C mean cluster approach

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. © 2025 Elsevier B.V., All rights reserved.

Авторы
Pandit Vaibhav Bhagwan 1 , Anjaiah Theerthala 2 , Ravali Chitteti 1 , Chary Darshanoju Srinivasa 3 , Zamani Abu Taha 4 , Ullah Sajid 5, 6 , Rebouh Nazih Y. 7 , Tariq Aqil 8
Издательство
Springer Nature
Номер выпуска
1
Язык
English
Статус
Published
Номер
20991
Том
15
Год
2025
Организации
  • 1 Department of Soil Science and Agricultural Chemistry, SR University, Warangal, India
  • 2 Institute of Soil Health Management, Professor Jayashankar Telangana Agricultural University, Hyderabad, India
  • 3 Department of Mathematics and Statistics, Acharya N.G. Ranga Agricultural University, Guntur, India
  • 4 Department of Computer Science, Northern Border University, Arar, Saudi Arabia
  • 5 Department of Water Resources and Environmental Engineering, Nangarhar University, Jalalabad, Nangarhar, Afghanistan
  • 6 School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, China
  • 7 Department of Environmental Management, RUDN University, Moscow, Russian Federation
  • 8 Department of Wildlife, College of Forest Resources, Mississippi State, United States
Ключевые слова
Fuzzy C-means clustering; Fuzzy performance index; Management zone; Normalized classification index; Principal component analysis; Soil test crop response; Spatial variability
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