Climate change imprints on soil are projected primarily through the changes in soil moisture and surge in soil temperature and CO₂ levels in response to climate change and is anticipated to have varying impacts on soil characteristics and processes that are instrumental in the restoration of soil fertility as well as productivity. Climate change encompasses a major concern of sharing its impact on the stability and functionality of soil microbiome and is characterized by one or more chief stability metrics encircling resistance, resilience, and functional redundancy. Nevertheless, the explorations over the past years have unveiled the potential of microbial interventions in the regeneration of soils or assurance of perked-up resilience to crops. The strategies involved therein encompass harnessing the native capability of soil microbes for carbon sequestration, phyto-stimulation, bio fertilization, rhizo-mediation, biocontrol of plant pathogens, enzyme-mediated breakdown, antibiosis, prompting of anti-oxidative defense mechanism, exudation of volatile organic compounds (VOCs) and induced systemic resistance (ISR) response in the host plant. However, the short storage and shelf-life of microbe-based formulations stay a significant constraint and rigorous efforts are necessary to appraise their additive impact on crop growth under changing climate scenarios.

The knowledge about the spatial distribution of soil organic carbon stock (SOCS) helps in sustainable land-use management and ecosystem functioning. No such study has been attempted in the complex topography and land use of Himalayas, which is associated with great spatial heterogeneity and uncertainties. Therefore, in this study digital soil mapping (DSM) was used to predict and evaluate the spatial distribution of SOCS using advanced geostatistical methods and a machine learning algorithm in the Himalayan region of Jammu and Kashmir, India. Eighty-three soil samples were collected across different land uses. Auxiliary variables (spectral indices and topographic parameters) derived from satellite data were used as predictors. Geostatistical methods—ordinary kriging (OK) and regression kriging (RK)—and a machine learning method—random forest (RF)—were used for assessing the spatial distribution and variability of SOCS with inter-comparison of models for their prediction performance. The best fit model validation criteria used were coefficient of determination (R2) and root mean square error (RMSE) with resulting maps validated by cross-validation. The SOCS concentration varied from 1.12 Mg/ha to 70.60 Mg/ha. The semivariogram analysis of OK and RK indicated moderate spatial dependence. RF (RMSE = 8.21) performed better than OK (RMSE = 15.60) and RK (RMSE = 17.73) while OK performed better than RK. Therefore, it may be concluded that RF provides better estimation and spatial variability of SOCS; however, further selection and choice of auxiliary variables and higher soil sampling density could improve the accuracy of RK prediction.

Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences.

Soil quality assessment serves as an index for appraising soil sustainability under varied soil management approaches. Our current investigation was oriented to establish a minimum data set (MDS) of soil quality indicators through the selection of apt scoring functions for each indicator, thus evaluating soil quality in the Himalayan foothills. The experiment was conducted during two consecutive years, viz. 2016 and 2017, and comprised of 13 treatments encompassing different combinations of chemical fertilizers, organic manure, and biofertilizers, viz. (i) the control, (ii) 20 kg P + PSB (Phosphorus solubilizing bacteria), (iii) 20 kg P + PSB + Rhizobium, (iv) 20 kg P + PSB + Rhizobium+ FYM, (v) 20 kg P + 0.5 kg Mo + PSB, (vi) 20 kg P + 0.5 kg Mo + PSB + Rhizobium, (vii) 20 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM, (viii) 40 kg@ P + PSB, (ix) 40 kg P + PSB + Rhizobium, (x) 40 kg P + PSB + Rhizobium+ FYM, (xi) 40 kg P + 0.5 kg Mo + PSB, (xii) 40 kg P + 0.5 kg Mo + PSB + Rhizobium, and (xiii) 40 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM. Evaluating the physical, chemical, and biological indicators, the integrated module of organic and inorganic fertilization reflected a significant improvement in soil characteristics such as the water holding capacity, available nitrogen, phosphorus, potassium, and molybdenum, different carbon fractions and soil biological characteristics encircling microbial biomass carbon (MBC), and total bacterial and fungal count. A principal component analysis (PCA) was executed for the reduction of multidimensional data ensued by scoring through the transformation of selected indicators. The soil quality index (SQI) established for different treatments exhibited a variation of 0.105 to 0.398, while the magnitude of share pertaining to key soil quality indicators for influencing soil quality index encircled the water holding capacity (WHC), the dehydrogenase activity (DHA), the total bacteria count, and the available P. The treatments that received an integrated nutrient package exhibited a higher SQI (T10—0.398; T13—0.372; T7—0.307) in comparison to the control treatment (T1—0.105). An enhanced soil quality index put forth for all organic treatments reflected an edge of any conjunctive package of reduced synthetic fertilizers with prime involvement of organic fertilizers over the sole application of inorganic fertilizers.