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.

Soil microbial activity (SMA) is vital concerning carbon cycling, and its functioning is recognized as the primary factor in modifying soil carbon storage potential. The composition of the microbial community (MC) is significant in sustaining environmental services because the structure and activity of MC also influence nutrient turnover, distribution, and the breakdown rate of soil organic matter. SMA is an essential predictor of soil quality alterations, and microbiome responsiveness is imperative in addressing the escalating sustainability concerns in the Himalayan ecosystem. This study was conducted to evaluate the response of soil microbial and enzyme activities to land conversions in the Northwestern Himalayas (NWH), India. Soil samples were collected from five land use systems (LUSs), including forest, pasture, apple, saffron, and paddy-oilseed, up to a depth of 90 cm. The results revealed a significant difference (p < 0.05) in terms of dehydrogenase (9.97–11.83 TPF µg g⁻¹ day⁻¹), acid phosphatase (22.40–48.43 µg P-NP g⁻¹ h⁻¹), alkaline phosphatase (43.50–61.35 µg P-NP g⁻¹ h⁻¹), arylsulphatase (36.33–48.12 µg P-NP g⁻¹ h⁻¹), fluorescein diacetate hydrolase (12.18–21.59 µg g⁻¹ h⁻¹), bacterial count (67.67–123.33 CFU × 10⁶ g⁻¹), fungal count (19.33–67.00 CFU × 10⁵ g⁻¹), and actinomycetes count (12.00–42.33 CFU × 10⁴ g⁻¹), with the highest and lowest levels in forest soils and paddy-oilseed soils, respectively. Soil enzyme activities and microbial counts followed a pattern: forest > pasture > apple > saffron > paddy-oilseed at all three depths. Paddy-oilseed soils exhibited up to 35% lower enzyme activities than forest soils, implying that land conversion facilitates the depletion of microbiome diversity from surface soils. Additionally, reductions of 49.80% and 62.91% were observed in enzyme activity and microbial counts, respectively, with soil depth (from 0–30 to 60–90 cm). Moreover, the relationship analysis (principal component analysis and correlation) revealed a high and significant (p = 0.05) association between soil microbial and enzyme activities and physicochemical attributes. These results suggest that land conversions need to be restricted to prevent microbiome depletion, reduce the deterioration of natural resources, and ensure the sustainability of soil health.

Current study was conducted to evaluate the effect of important land uses and soil depth on soil organic carbon pools viz. total organic carbon, Walkley and black carbon, labile organic carbon, particulate organic carbon, microbial biomass carbon and carbon management index (CMI) in the north Western Himalayas, India. Soil samples from five different land uses viz. forest, pasture, apple, saffron and paddy-oilseed were collected up to a depth of 1 m (0–30, 30–60, 60–90 cm). The results revealed that regardless of soil depth, all the carbon pools differed significantly (p < 0.05) among studied land use systems with maximum values observed under forest soils and lowest under paddy-oilseed soils. Further, upon evaluating the impact of soil depth, a significant (p < 0.05) decline and variation in all the carbon pools was observed with maximum values recorded in surface (0–30 cm) soils and least in sub-surface (60–90 cm) layers. CMI was higher in forest soils and lowest in paddy-oilseed. From regression analysis, a positive significant association (high R-squared values) between CMI and soil organic carbon pools was also observed at all three depths. Therefore, land use changes and soil depth had a significant impact on soil organic carbon pools and eventually on CMI, which is used as deterioration indicator or soil carbon rehabilitation that influences the universal goal of sustainability in the long run.

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.

Purpose: Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods: In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion: Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R²_adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R² = 0.99), and the SVM model was the lowest (R² = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion: The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.