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.

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.

The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation, and carbon sequestration. Conventional direct BD measurement methods are often time-consuming, costly, and laborious. In contrast, the use of pedotransfer functions (PTFs) based on readily available soil properties offers a cost-effective alternative approach that can provide indirect quick estimates of soil BD. Although several region-specific PTFs are available, none is tailored for use in the hilly North-Western Himalayas (NWH) area. Thus, this work developed a novel PTF to estimate the soil BD of 300 soil samples collected from the NWH area and analyzed by stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT). The site-specific SR-based equation performed better than the PLSR model by explaining up to 72.2% variability of BD and achieving a coefficient of determination (R²) of 0.73, a mean absolute error (MAE) of 0.0379 g cm⁻³, and a root mean square error (RMSE) of 0.0487 g cm⁻³ for the BD prediction. Soil organic matter and clay contents were the most significant contributors, showing the highest importance in the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation (R²: 0.006–0.71, RMSE: 0.0649–0.405 gcm^− 3, MAE: 0.0512–0.395 g cm^− 3). The promising SR equation and PTF developed in this work showed high accuracy and excellent suitability for stakeholders and professionals in the study area and the scientific community as a reliable BD estimator for hilly soils. This study introduces proposed the first-ever site-specific PTF for BD estimation in hilly soils, contributing to scientific challenge by enhancing soil database and improving BD predictions in complex terrains worldwide. In terms of global significance, the outcomes of this study are essential for soil quality and vulnerability studies, resilience indicators estimates carbon sequestration initiatives, and digital soil mapping, and can also be used to construct a BD database for hilly soils across the globe. Graphical Abstract The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation and carbon sequestration, and soil quality, vulnerability and resilience. Conventional direct methods to measure soil BD are often time-consuming, costly and laborious, whereas pedotransfer functions (PTFs) based on readily available soil properties offer an efficient alternative approach that provides indirect quick estimates of soil BD. However, none of the several region-specific PTFs currently available, is tailored for use in the hilly North-Western Himalayas (NWH) area. A novel PTF was developed in this work to evaluate the BD of 300 soil samples collected from agriculture, horticulture, forest and fallow hilly soils in the North West Himalaya area. Stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT) were tentatively applied for BD estimation of the studied soils, showing that the site-specific SR-based equation performed the best. Various soil properties were evaluated in the models, with soil organic matter and clay contents being the most significant contributors to the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation for the hilly soils studied in this work. In conclusion, the SR equation and PTF developed in this work show excellent and promising suitability for stakeholders and professionals in the study area and for the global scientific community as a reliable BD estimator for hilly soils. Furthermore, the results of this work can be used for soil management, land use planning, and climate change mitigation in fragile ecosystems like the Himalayas.

Aims: Conversion of land from forest to cropping has a serious effect on soil phosphorus and its fractions Results: Land use is now widely understood to be a primary factor in environmental change across all time and space scales. The purpose of this research was to ascertain how different land uses affected the concentration of phosphorus in soil. Soil phosphorus (P) reserves are depleted when land is converted from natural vegetation to permanent agricultural cropping. The transformation of North-Western Himalayas from a forest-dominated to a grassland-dominated ecosystem is just one example of the diversity of land significantly less soil aggregation occurred when agricultural land was cleared of its native vegetation. Total organic carbon in soils was reduced when grassland was converted to cropland. Reduced total organic carbon (TOC) concentrations by 62-79% and organic phosphorus (Po) concentrations by 47-53%. Even though, the total silt+clay fraction's contribution was negligible, it contained a significant amount of C and Po reserves and the C/Po ratio has been holding fairly steady, they have proven to be more robust. This impact of cropping on soil P reserves has been demonstrated in research, but changing land use practices can alleviate these problems significantly.