Comparative evaluation of three contrasting land use systems for soil carbon, microbial and biochemical indicators in North-Western Himalaya

- Three contrasting land uses were evaluated in terms of measured soil indicators.
- LTFE emerged to be the most ecologically viable one.
- Guava system appeared to be the most stressed one owing to the highest qCO2.
- A set of indicators was screened using PCA and discriminant analysis.
- Study highlighted the importance of IC in soil C dynamics and sequestration.

The present investigation was aimed to identify the most ecologically viable agroecosystem among the three dominant, differently managed land use systems which were, agriculture (Long Term Fertilizer Experiment - LTFE, continuous pulse and organic system), horticulture (mango: Mangifera indica and guava: Psidium guajava) and agroforestry (bamboo: Bambusa sp., Dendrocalamus sp., poplar: Populus deltoides, and Dalbergia sissoo) on the basis of selected soil carbon fractions, microbial (microbial biomass carbon, basal soil respiration), biochemical parameters (dehydrogenase activity, easily extractable glomalin related soil protein, total extractable glomalin related soil protein) and microbial indices. The study area targeted the Mollisol of Uttarakhand which typifies the typical fragile ecosystem of Terai region of North-Western Himalayan region. Results indicated that LTFE, mango and Dalbergia system appeared as the most sustainable based on soil carbon, biochemical and microbiological attributes among their own agro-ecosystem. Our study also revealed guava system to be the most stressed as shown by the highest value of metabolic quotient. We further attempted to enumerate the sensitivity of a set of soil quality indicators controlling soil ecological process. In discriminant function analysis, a composite of indicators (inorganic carbon, dehydrogenase activity, easily extractable and total extractable glomalin related soil protein) discriminated well in the three studied agro-ecosystems. Furthermore, this study highlighted the necessity of inclusion of soil inorganic carbon and glomalin fractions for generating soil quality indicators.

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