Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas.

Evaluation of grazing impacts on land degradation processes is a difficult task due to the heterogeneity and complex interacting factors involved. In this paper, we designed a new methodology based on a predictive index of grazing susceptibility to land degradation index (GSLDI) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery. Seventeen environmental factors (e.g., derivatives of the digital elevation model, small ruminants' stock), and 55 subsequent attributes (e.g., classes/features) were assigned to each sampling point. The impact of SRs stock density on the land degradation process has been evaluated and estimated with two extreme SRs' density scenarios: absence (no stock), and double density (overstocking). We applied the GSLDI methodology to the Curvature Subcarpathians, a region that experiences the highest erosion rates in Romania, and found that SRs grazing is not the major contributor to land degradation, accounting for only 4.6%. This methodology could be replicated in other steep slope grazing areas as a tool to assess and predict susceptible to land degradation, and to establish common strategies for sustainable land-use practices.