Food flow data provide unique insights into the debates surrounding the sustainability of land based production and consumption at multiple scales. Trade flows disguise the spatial correspondence of production and consumption and make their connection to land difficult. Two key components of this spatial disjuncture are land use displacement and economic regional decoupling. By displacing the environmental impact associated with food production from one region to another, environmental trajectories can falsely appear to be sustainable at a particular site or scale.

Land degradation monitoring is of vital importance to provide scientific information for promoting sustainable land utilization. This paper presents an expert knowledge and BP-ANN-based approach to detect and monitor land degradation in an effort to overcome the deficiencies of image classification and vegetation index-based approaches.

This article analyzes agricultural sustainability in the context of land degradation, rural poverty and social inequality, taking China’s Loess Hills as an example. The analysis attempts to understand the multi-dimensionality of sustainability at the farm level and its relationship with physical-socio-economic-infrastructural-technological framework conditions in the context of the land set-aside program viz. the Grain for Green Project (GGP).