Soil enzymes and microbial elemental stoichiometry as bio-indicators of soil quality in diverse cropping systems and nutrient management practices of Indian Vertisols

Abstract

Organic agriculture is attaining increasing popularity as it sustains crop productivity and soil health without deteriorating climate. But comprehensive information on carbon (C), nitrogen (N), phosphorus (P) and sulphur (S) cycling enzymes, microbial elemental stoichiometry and soil functional diversity are scanty in organic vis-à-vis conventional systems across the globe. The most discriminant biological factor(s) and enzyme activity based quick, effective, sensitive index of soil quality are unavailable for organic agriculture. To address these issues, we considered four major cropping systems of India i.e. soybean-wheat, soybean-mustard, soybean-pea and soybean-linseed under six nutrient management practices, namely, M1 (mineral fertilizers as used by farmers), M2 (recommended dose of mineral fertilizers), M3 (50% Inorganic +50% Organic), M4 (25% Inorganic +75% Organic), M5 (75% Organic + Innovative) and M6 (Organic; 100% Organic) in a Vertisol. Activities of eleven enzymes, soil microbial biomass C, N and P and soil organic C were analysed and geometric mean enzyme activity (GMEA) and other parameters were computed for surface and subsurface soils. Enzyme activities were significantly (P < 0.05) increased by organic amendments (~50–75%). Exceptionally, phenol oxidase and peroxidase activities were ~51 and 50% higher in subsurface than surface soils. Specific enzyme activities were lower in M6; it ascertained the capability of organic system to sequester higher amounts of C than M3 and M1. However, soil functional diversity was nearly 10 and 20% lower for M6 than M3 and M1, respectively. Discriminant function analysis confirmed SOC, arylsulfatase activity and available P to be the most effective discriminant factors between the conventional and organic nutrient management practices in India. The GMEA was ~55% higher for M6 than M1. It was strongly and positively correlated with treated soil quality index. Further, GMEA was cross validated with an independently performed principal component analysis to find its suitability as soil quality index. The GMEA was correlated (P < 0.05) with scores of first component and proved to be consolidative enough for explaining functional distinction between conventional and organic management by reducing the several soil enzyme activities to a single numerical point. Thus, GMEA is proposed to indicate soil quality quickly and accurately in Vertisol. Microbial biomass C to N ratio was correlated with system productivity and GMEA. But, we recommend for its recertification after developing baseline values to use it as a robust indicator of soil productivity.