African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6902

Article in Press

Spatial variability of soil microbiological properties under different land use systems

Bhabani Prasad Mondal, Bharpoor S. Sekhon, Koushik Banerjee, Sandeep Sharma, R.K. Setia, Bappa Das, Suman Dutta, Ranjan Bhattacharya, Mohamed A.E. AbdelRahman, Antonio Scopa, Marios Drosos, Ali R.A. Moursy

  •  Received: 28 June 2024
  •  Accepted: 09 August 2024
Understanding the spatial variability of soil microbial properties is essential for assessing their role in nutrient cycling and ecosystem functioning across various land use systems. While many studies focus on soil physicochemical characteristics, the spatial heterogeneity of microbiological attributes is less explored. This research examined the spatial distribution of microbial biomass carbon (MBC) and dehydrogenase activity (DHA) in surface soils of berseem, rice-wheat, and poplar-wheat cropping systems. Forty-eight georeferenced surface soil samples (0-0.15 m) were collected from each system and analyzed for MBC, DHA, and physicochemical properties. Sensitivity analysis determined the minimum sample size for effective sampling. Results indicated strong spatial dependence for berseem and poplar-wheat systems. Principal component analysis (PCA) and discriminant analysis (DA) identified MBC, DHA, and soil organic carbon as key variables distinguishing the land use systems. Soil microbiological characteristics exhibited greater variability than chemical properties, with DHA showing more variability than MBC, necessitating larger sample sizes to detect changes. This study underscores the importance of understanding spatial variation in soil microbial properties for designing sampling protocols and implementing sustainable management practices, ultimately promoting ecosystem functioning and nutrient cycling in diverse agricultural systems.

Keywords: DHA, Discriminant analysis, Geo-statistics, MBC, Spatial variability