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Abstract <p>Understanding the spatial variability of soil microbiological properties is essential for optimizing nutrient cycling, enhancing soil fertility and promoting sustainable agriculture. Microbial processes are inherently heterogeneous, making it crucial to study their distribution patterns across cultivated landscapes. Therefore, a study was conducted in 2022–2023 at Deen Dayal Upadhyay Centre of Excellence for Organic Farming, CCS Haryana Agricultural University, Hisar, India in semi-arid region where eighty-nine surface soil samples were collected and subjected to comprehensive laboratory analysis to determine various soil physico-chemical and microbiological properties. The results revealed considerable variability in microbial activity, with microbial biomass carbon ranging from 190.02 to 462.74 mg/kg, dehydrogenase activity from 44.93 to 110.73 μg tri-phenyl formazan (TPF)/(g soil 24 h), phosphatase activity from 117.39 to 252.73 μg p-nitrophenyl phosphate (PNP)/(g soil h) and urease activity from 32.36 to 84.17 μg <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\text{NH}}_{4}^{ + }\)</EquationSource> <!--SoilSci2560416Singh-m1--> </InlineEquation>-N/(g soil h). Semivariogram analysis through ordinary kriging demonstrated that exponential, gaussian and circular models were the best fit models for different soil microbiological properties with moderate to strong spatial dependence. Nugget values varied from 0 to 0.002 while sill values varied from 0.002 to 0.004. The semivariogram ranges varied markedly, with shorter ranges for dehydrogenase (102.36 m), urease (107.13 m) and phosphatase (123.81 m) activities, while microbial biomass carbon showed notably larger range (170.54 m). Four principal components (eigenvalues ≥ 1), accounting for 75.57% of total variability, were subjected to fuzzy c-means clustering, which delineated three management zones with statistically significant differences in soil properties. Managing spatial soil variability with zone delineation and microbial indicators enhances precision agriculture practices overall.</p>

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Geostatistical and GIS-Based Mapping of Soil Microbiological Variability for Management Zone Delineation in an Organically Managed Farm in Hisar, India

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • A. Singh,
  • N. Yadav,
  • D. Tomar,
  • M. Kumar,
  • K. Golui,
  • P. Phogat,
  • Y. Rajrana,
  • S. Acharya

摘要

Abstract

Understanding the spatial variability of soil microbiological properties is essential for optimizing nutrient cycling, enhancing soil fertility and promoting sustainable agriculture. Microbial processes are inherently heterogeneous, making it crucial to study their distribution patterns across cultivated landscapes. Therefore, a study was conducted in 2022–2023 at Deen Dayal Upadhyay Centre of Excellence for Organic Farming, CCS Haryana Agricultural University, Hisar, India in semi-arid region where eighty-nine surface soil samples were collected and subjected to comprehensive laboratory analysis to determine various soil physico-chemical and microbiological properties. The results revealed considerable variability in microbial activity, with microbial biomass carbon ranging from 190.02 to 462.74 mg/kg, dehydrogenase activity from 44.93 to 110.73 μg tri-phenyl formazan (TPF)/(g soil 24 h), phosphatase activity from 117.39 to 252.73 μg p-nitrophenyl phosphate (PNP)/(g soil h) and urease activity from 32.36 to 84.17 μg \({\text{NH}}_{4}^{ + }\) -N/(g soil h). Semivariogram analysis through ordinary kriging demonstrated that exponential, gaussian and circular models were the best fit models for different soil microbiological properties with moderate to strong spatial dependence. Nugget values varied from 0 to 0.002 while sill values varied from 0.002 to 0.004. The semivariogram ranges varied markedly, with shorter ranges for dehydrogenase (102.36 m), urease (107.13 m) and phosphatase (123.81 m) activities, while microbial biomass carbon showed notably larger range (170.54 m). Four principal components (eigenvalues ≥ 1), accounting for 75.57% of total variability, were subjected to fuzzy c-means clustering, which delineated three management zones with statistically significant differences in soil properties. Managing spatial soil variability with zone delineation and microbial indicators enhances precision agriculture practices overall.