Rawal et al. (2026) Variation in Soil Organic Carbon Along an Altitudinal Gradient Across Different Aspects in the Timberline Zone of the Western Himalaya, India

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Plants
• Year: 2026
• Date: 2026-09-10
• Authors: Renu Rawal, Ankur Sharma, Gaurav Mishra, Tanay Barman, Lalit M. Tewari
• DOI: 10.3390/plants15182775

Research Groups

• Department of Geology, University of Delhi
• Wildlife Institute of India (WII)
• Indian Institute of Remote Sensing (IIRS)

Short Summary

This study investigates soil organic carbon (SOC) changes across different topographic orientations in the Western Himalaya and evaluates machine-learning models for spatial SOC prediction. The results show that aspect-induced microclimatic gradients play a significant role in controlling soil characteristics near the timberline.

Objective

• Investigate the spatial and altitudinal changes in soil organic carbon (SOC) across different topographic orientations in the Western Himalaya.
• Evaluate machine-learning models for spatial SOC prediction in the timberline ecotone of the Kedarnath Wildlife Sanctuary.

Study Configuration

• Spatial Scale: Local to regional scale, focusing on the Kedarnath Wildlife Sanctuary (2100-3300 m).
• Temporal Scale: Not specified; likely a snapshot or point-in-time study.

Methodology and Data

• Models used: Random Forest (RF), Support Vector Machine (SVM), XGBoost.
• Data sources: Soil samples, topographic data, spectral data from satellite imagery, climatic covariates.

Main Results

• SOC trends varied significantly by aspect; the North-East aspect exhibited a considerable increase in SOC with elevation.
• Digital Soil Mapping using the RF model outperformed SVM and XGBoost, explaining 62% of surface and 74% of subsurface SOC variability.

Contributions

• This study provides new insights into the complex relationships between topographic orientation, microclimatic gradients, and soil characteristics near the Himalayan timberline.
• The results highlight the potential of machine-learning models like RF for capturing spatial patterns in SOC variability.

Funding

• Not specified; likely funded by a combination of government grants, research projects, and institutional funding.

Citation

@article{Rawal2026Variation,
  author = {Rawal, Renu and Sharma, Ankur and Mishra, Gaurav and Barman, Tanay and Chaturvedi, Ravi K. and Tewari, Lalit M.},
  title = {Variation in Soil Organic Carbon Along an Altitudinal Gradient Across Different Aspects in the Timberline Zone of the Western Himalaya, India},
  journal = {Plants},
  year = {2026},
  doi = {10.3390/plants15182775},
  url = {https://doi.org/10.3390/plants15182775}
}