The sustainability of society hinges on the future of agriculture. Though alternatives to unsustainable, high-input industrial agriculture are available, agricultural systems have been slow to transition to them. Much of the resistance to adopting alternative techniques stems from the perceived costs of alternative agriculture, mainly in terms of yields.

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).