No abstract

Desertification has emerged as a major economic, social and environmental problem in the western part of India. The best way of dealing with desertification is to take appropriate measures to arrest land degradation, especially in areas prone to desertification. This requires an early warning system for desertification based on scientific inputs. Hence, in the present study, an attempt has been made to develop a comprehensive model for the assessment of desertification risk in the Jodhpur district of Rajasthan, India, using 23 desertification indicators. Indicators including soil, climate, vegetation and socioeconomic parameters were integrated into a GIS environment to get environmental sensitive areas (ESAs) to desertification. Desertification risk index (DRI) was calculated based on ESAs to desertification, the degree of land degradation and significant desertification indicators obtained from the stepwise multiple regression model. DRI was validated by using independent indicators such as soil organic matter content and cation exchange capacity. Multiple regression analysis shows that 16 indicators out of 23 were found to be significant for assessing desertification risk at a 99% confidence interval with R 2 = 0.83. The proposed methodology provides a series of effective indicators that would help to identify where desertification is a current or potential problem, and what could be the actions to alleviate the problem over time.

No abstract

No abstract

As one of the Earth’s most important natural resources, soil plays a prominent role in regulating ecosystem services, human food production systems and in facilitating a region’s sustainable development. Of late, due recognition has been given to soil sciences and soil information systems as they act as a core to achieve the targets of land degradation neutrality and help in fostering soil governance. In this regard, the availability of global soil databases paves the way for implementing successful soil information systems. Currently, harmonized world soil database from the Food and Agriculture Organization and SoilGrids from International Soil References and Information Centre serve various global soil data products in a geospatial-ready format for the scientific fraternity. In this study, SoilGrids 2.0 is validated with in situ measurements in the arid region of the Thar Desert. Soil fractions and pH at the top surface (0–5 cm) and subsurface (5–15 cm) were measured through soil sample analysis collected from the study area and compared with the values retrieved from SoilGrids 2.0 for the same location. This comparison shows that the SoilGrids 2.0 has underestimated the sand fragments up to ~28% and overestimated ~14% for silt and clay fragments. Deviation of pH in SoilGrids 2.0 was also observed with the root mean square error of one unit. However, in the comparison of soil texture classes from the field and the one given by SoilGrids 2.0, a systematic shift was found, indicating the robustness of SoilGrids prediction algorithm that can be fine-tuned by incorporating additional soil profiles (from contributing agencies) as the current snapshot of the soil database lacks dense and well-distributed soil profiles in this arid region.