Soil erosion often leads to land degradation, agricultural production reduction, and environmental deterioration, which seriously restricts the sustainable development of regions. Clarifying the driving factors of soil erosion is the premise of preventing soil erosion. Given the lack of current research on the driving factors/force changes of soil erosion in different regions or under different erosion intensity grades, this paper pioneered to use machine learning methods to address this problem.

Land is the key asset in the agricultural sector and hence land policy is one of the key elements that determine whether SDGs are achieved in developing counties or not. In developing countries, land titling programs have been seen as a strategy for addressing SDGs. Even though the government of Ethiopia launched the rural land registration and certification program (LRCP) to secure the land rights of rural households in 1998, currently, there are limited empirical studies to examine the contribution of LRCP in addressing sustainable development goals (SDGs).

Whereas most contemporary frameworks evaluating land management aspects focus on institutional settings at a national level, the 8R framework of responsible land management aims at evaluating individual land management projects or interventions. This 8R framework is, however, still under development and needs testing, validation and further detailing, such that specific operational characteristics and internal and external effects can be included in the evaluation.