Spatiotemporal analysis of land degradation and soil erosion dynamics in the Chambal badlands using machine learning and geospatial techniques
Year: 2026
Type: article
Abstract: The accelerating land degradation processes, including soil erosion and ravine formation, pose a critical challenge to global food security and agricultural sustainability. By integrating CA-Markov, geospatial modeling, SARIMA, and machine learning, this study investigates the spatiotemporal dynamics of land degradation and soil erosion in the Chambal Badlands across a 64-year horizon (1992–2056). The analysis reveals that ravine areas decreased from 14.59% in 1992 to 7.42% in 2024, with projections suggesting a further decline to 2.22% by 2056. Total soil erosion decreased from 27.56 million t yr − 1 in 1992 to 17.93 million t yr − 1 in 2024 and is projected to decrease further to 12.11 million t yr − 1 by 2056. These shifts are primarily driven by land reclamation initiatives implemented across the region. However, soil erosion intensity has increased locally within high-slope ravine belts. This is driven by climate change, as evidenced by the rise in rainfall erosivity (R-factor) from 407 MJ·mm·ha − 1 ·h − 1 ·yr − 1 in 1992 to 463 MJ·mm·ha − 1 ·h − 1 ·yr − 1 in 2024, with a projected increase to 482 MJ·mm·ha − 1 ·h − 1 ·yr − 1 by 2056. Among the soil erosion drivers (soil, topography, rainfall, and land management), machine-learning results identify land management practices (P-factor) as the dominant control on erosion, with the highest contribution (32.8% in 2024). The results suggest that targeted land management interventions, combined with climate-adaptive planning, can substantially mitigate future erosion risks. The study provides valuable insights for sustainable land-use planning and supports progress toward the Sustainable Development Goals, specifically SDG 2 (Zero Hunger), SDG 13 (Climate Action), and SDG 15 (Life on Land).
Source: Discover Geoscience
Institution: Jamia Millia Islamia
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDGs: Life on land, Climate action
Open Access status: diamond
OpenAlex ID: W7130438224