This paper examines the preservation of farmland through purchasable development rights. In a comparison of the estimated costs and benefits associated with the development of open space from 1982 to 1992, we show that these programs may provide a net benefit to society. An econometric model is employed to address the question of what factors explain both the creation of these programs and the magnitude of farmland preservation.

Land use and land cover changes have complex linkages to climate variability and change, biophysical resources, and socioeconomic driving forces. To assess these land change dynamics and their causes in the Great Plains, we compare and contrast contemporary changes across 16 ecoregions using Landsat satellite data and statistical analysis. Large-area change analysis of agricultural regions is often hampered by change detection error and the tendency for land conversions to occur at the local-scale.

Coastal dunes and sand areas are reported to be among the habitats most invaded by alien species in Europe. Landscape pattern could be a significant driver in invasion processes in parallel with land-use legacy. Fragmentation of natural habitats combined with the availability of propagules from the surrounding matrix may enhance the invisibility of ecological communities.

Since mean nitrate concentration along single river channels increases significantly with percent arable land use upstream of sampling points and autumn/early winter flushes in nitrate concentration are widespread, it is generally concluded that farmers contribute most of the nitrate. For the River Derwent in North Yorkshire, the correlation between nitrate concentration and percent arable land use is much poorer when tributary data are included in the equation, because of greater variations in dilution by water draining upland areas and in other N input sources.

Water, forage and predation constrain ungulate distributions in savannas. To understand these constraints, we characterized distributions of 15 herbivore species from water, locations of peak density and degree of clustering around the peaks using zero-inflated count data models and mapping census data collected in the Mara reserve and the adjoining pastoral ranches in Kenya during a wet and dry year.

Land retirement is ceasing irrigation with the goal of reducing load, in general, of dissolved constituents and, in particular, of trace elements, present in subsurface drainage generated from irrigated lands. Retirement is achieved through a process of goal setting, strategy development and determining effects, developing land selection criteria, implementation, and monitoring. In this study, effects of land retirement are evaluated using hydrologic,soil and economic models as well as results from a field demonstration study.

Habitat models constitute useful instruments for understanding species-habitat interactions and can constitute helpful conservation tools. The Upper Paraná Atlantic Forest (UPAF) of South America still holds the world’s southernmost jaguar (Panthera onca) population.

Concerns about climate change and water quality make it necessary to have a better understanding of the cycling of carbon (C) and nitrogen (N) within landscapes. In New Zealand, pastoral farming on hill country is a major land use, and there is little information available at a landscape level on the cycling of C and N within these systems, particularly the impacts of land use intensification.

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI).

The time-integrated normalized difference vegetation index (iNDVI) provides key remote-sensing-derived information on the interactions between vegetation growth, climatic and soil conditions, and land use. Using a time-series of Landsat imagery obtained for Queensland, Australia, it has been demonstrated how robust geostatistics can be used to predict iNDVI. This approach is novel because it explicitly quantifies the uncertainty of prediction and uses Winsorizing, a data-censoring method, to minimize the distorting effects of outliers.