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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kumar, Amit | en_US |
| dc.contributor.author | Mohanasundari, Thangave | en_US |
| dc.date.accessioned | 2026-05-14T12:28:17Z | - |
| dc.date.available | 2026-05-14T12:28:17Z | - |
| dc.date.issued | 2026 | - |
| dc.identifier.citation | Kumar, A., Govindaraj, G., Rajkumar, M., & Mohanasundari. (2026). Land Degradation Vulnerability in Central India: A Multidimensional Assessment Using Biophysical and Socioeconomic Indicators. Land Degradation and Development. https://doi.org/10.1002/ldr.70606 | en_US |
| dc.identifier.issn | 1085-3278 | - |
| dc.identifier.other | EID(2-s2.0-105035902198) | - |
| dc.identifier.uri | https://dx.doi.org/10.1002/ldr.70606 | - |
| dc.identifier.uri | https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18213 | - |
| dc.description.abstract | Land degradation poses a significant environmental challenge to ecological sustainability and rural livelihoods, especially in the central Indian state of Madhya Pradesh. This study used a multidimensional approach by integrating biophysical and socioeconomic indicators under six dimensions: soil, climate, terrain, land use, hazards, and socioeconomic conditions. All variables were normalised using a min-max method and combined through an equal-weight composite index to maintain consistency and avoid subjective bias for creating the district-level land degradation vulnerability index (LDVI). Spatial statistical validation was conducted using Spatial Autocorrelation (Global Moran's I) and Hot Spot Analysis (Local Getis-Ord Gi*). The LDVI findings demonstrated a strong spatial pattern, with western and southwestern districts showing consistently high vulnerability across multiple indices. Districts such as Barwani (0.728), Alirajpur (0.655), Jhabua (0.587), and Dhar (0.578) ranked among the most vulnerable, mainly due to high soil erosion, greater exposure to climate hazards, and agricultural dependence. However, districts like Datia (0.283), Bhind (0.320), and Tikamgarh (0.337) were categorised as lowest vulnerability, benefiting from relatively stable terrain, lower hazard exposure, better vegetation cover, and moderate socioeconomic stress. The LDVI showed a significant clustered pattern across districts (Moran's I = 0.612). The Local Getis-Ord Gi* analysis identified statistically significant hotspot districts in western Madhya Pradesh and cold spot clusters in the northern region, supporting the spatial consistency and reliability of the index. These findings identify priority districts for targeted soil conservation, climate adaptation, watershed management, and livelihood support, offering a replicable, policy-relevant LDVI framework that supports evidence-based land restoration efforts and advances India's progress toward SDG-15. © 2026 John Wiley & Sons Ltd. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | John Wiley and Sons Ltd | en_US |
| dc.source | Land Degradation and Development | en_US |
| dc.title | Land Degradation Vulnerability in Central India: A Multidimensional Assessment Using Biophysical and Socioeconomic Indicators | en_US |
| dc.type | Journal Article | en_US |
| Appears in Collections: | School of Humanities and Social Sciences | |
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