Extensive grazing is the predominant form of land use on at least a quarter of the world’s land surface, in which livestock are raised on food that comes mainly from rangelands. Extensive grazing differs from crop or forestry production, in which the produce remains in situ whilst growing. Evaluation for extensive grazing, unlike that for cropping or forestry, must take into account the production of both grazing forage, termed primary production, and the livestock that feed on this forage, termed secondary production.

This issue of Unasylva analyses the urban forestry situation and considers its potential for increasing living standards in different urban settings.

High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm) and high spatial resolution LiDAR data (2 m) and the field mapping method. The project not only provides the first-ever high-precision land-use maps for northern Maine, but it also yields accurate hectarage estimates of different land-use types, in particular grassland, defined as fallow land, pasture, and hay field.

The Huang-Huai-Hai Plain is the most important winter wheat and maize production region in China. In response to the increasing population pressure, the Chinese authorities strongly invested in improving the irrigation systems and increasing ground and surface water exploitation within the plain to increase the crop productivity. This resulted in a reduction of water resource availability and in turn indirectly affected the suitability of various agricultural ecosystems in the plain.

Multicriteria-Spatial Decision Support Systems (MC-SDSS) are increasingly popular tools in decision-making processes and in policy making, thanks to their significant new capabilities in the use of spatial or geospatial information. Many spatial problems are complex and require the use of integrated analysis and models. The present paper illustrates the development of a MC-SDSS approach for studying the ecological connectivity of the Piedmont Region in Italy.