Understanding the spatial variability of soil microbial properties is crucial for assessing their role in nutrient cycling and ecosystem functioning across various land use systems. While many studies emphasize 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 needed for effective sampling. Results showed strong spatial dependence for the berseem and poplar-wheat systems. Principal component analysis (PCA) and discriminant analysis (DA) identified MBC, DHA, and soil organic carbon (SOC) 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 highlights the importance of understanding spatial variation in soil microbial properties for designing sampling protocols and implementing sustainable management practices, ultimately enhancing ecosystem functioning and nutrient cycling in diverse agricultural systems. Key words: Dehydrogenase activity (DHA), discriminant analysis, geo-statistics, microbial biomass carbon (MBC), spatial variability. Understanding the spatial variability of soil microbial properties is crucial for assessing their role in nutrient cycling and ecosystem functioning across various land use systems. While many studies emphasize 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 needed for effective sampling. Results showed strong spatial dependence for the berseem and poplar-wheat systems. Principal component analysis (PCA) and discriminant analysis (DA) identified MBC, DHA, and soil organic carbon (SOC) 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 highlights the importance of understanding spatial variation in soil microbial properties for designing sampling protocols and implementing sustainable management practices, ultimately enhancing ecosystem functioning and nutrient cycling in diverse agricultural systems. Key words: Dehydrogenase activity (DHA), discriminant analysis, geo-statistics, microbial biomass carbon (MBC), spatial variability.

Phosphorus (P) is an essential plant nutrient and plays a vital role in achieving potential yields in rice-wheat sequence. conventional analytical techniques for assessing plant available P in soil are time consuming, relatively expensive and are not environment friendly. Diffuse reflectance spectroscopy particularly visible-near infrared (VIS-NIR) reflectance spectroscopy is an effective alternative means for rapid, nondestructive and eco-friendly assessment of available phosphorus and other soil properties. Geo-referenced surface soil (0–15 cm) samples (170) were collected from a rice-wheat field in Ludhiana district of Punjab state for performing both chemical (using Olsen method) and hyper-spectral analysis (350–2500 nm). A multivariate modeling technique, namely partial least square regression (PLSR) was employed to develop spectral model. Statistical diagnostics of coefficient of determination (R²), root mean square of error of prediction (RMSEP) and ratio of performance deviation (RPD) were used to evaluate the predictive performance of the developed spectral model. the R², RMseP and RPd values were 0.42, 1.15 and 1.40, respectively for separate validation dataset of the PLsR model. however, the R² and RMseP were better when the same set was used as validation set by using one-leave-out method. the RPd value suggested acceptable prediction accuracy of the spectral model. however, low R² value and somewhat higher RMseP suggested exploring new modelling or data preprocessing techniques to enhance prediction accuracy.

International Journal of Current Microbiology and Applied Sciences ISSN:2319-7692(Print), ISSN:2319-7706(Online) is a multidisciplinary peer-reviewed journal with reputable academics and experts as members of its Editorial Board. The Current Microbiology aims to publish all the latest and outstanding research articles. Review and letters in all areas of major importance to techniques of microbiology and applied research with publishes high quality of review and research articles on novel aspects of Microbiology including Environmental, Food, Agricultural, Medical, Pharmaceutical, Veterinary, Soil, Water and Biodeterioration.