This paper provides an original account of global land, water, and nitrogen use in support of industrialized livestock production and trade, with emphasis on two of the fastest-growing sectors, pork and poultry. Our analysis focuses on trade in feed and animal products, using a new model that calculates the amount of “virtual” nitrogen, water, and land used in production but not embedded in the product. We show how key meat-importing countries, such as Japan, benefit from “virtual” trade in land, water, and nitrogen, and how key meat-exporting countries, such as Brazil, provide these resources without accounting for their true environmental cost. Results show that Japan's pig and chicken meat imports embody the virtual equivalent of 50% of Japan's total arable land, and half of Japan's virtual nitrogen total is lost in the US. Trade links with China are responsible for 15% of the virtual nitrogen left behind in Brazil due to feed and meat exports, and 20% of Brazil's area is used to grow soybean ...

Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics.

This review looks at the role of the livestock sector in carbon (C) and nitrogen (N) cycles from a global perspective and considers impacts at the various stages of the commodity chain. With regard to livestock, N and C cycles are closely connected to livestock's role in land use and land-use change. Livestock's land use includes grazing land and cropland dedicated to the production of feed crops and fodder. Considering emissions along the entire commodity chain, livestock currently contribute about 18% to the global warming effect. Livestock contribute about 9% of total carbon dioxide (CO₂) emissions, but 37% of methane (CH₄), and 65% of nitrous oxide (N₂O). The latter will substantially increase over the coming decades, as the pasture land is currently at maximum expanse in most regions; future expansion of the livestock sector will increasingly be crop based. The chapter also reviews mitigation options to reduce C and N emissions from livestock's land use, production, and animal waste.

We present a study on the impact of soil and climatic variability on the yield of winter wheat in the Herault-Libron-Orb Valley in southern France. The study was based on the use of a crop simulation model (Euro-ACCESS), run at 63 individual sites throughout the study area, for the current climate (1976 to 1984) and for potential future changes in temperature and precipitation (2047 to 2054). Three climate scenarios were selected to represent low, mid and high changes, although significant winter wheat yield decreases were only observed for the climate scenario with the largest change. In general, the influence of climate change on yields was small (less than 0.1 t ha(-1) over the whole simulation period), but strong inter-annual variation was found, which is typical of the Mediterranean climate. Soil variability within the study region was the most important source of spatial variability for the simulated yields, and the soil available water capacity was identified as a good indicator of yield change for large climatic change. Soil variability was important in this study because of the small size of the study region and because of the strong influence of water limitation on crop growth in Mediterranean areas. Statistical relationships were established between crop yields, yield changes and the soil available water capacity. These relationships were used to extrapolate the crop simulation results from individual sites to the whole region using data from soil maps at a scale of 1:250 000. This modelling exercise demonstrated the importance of explicit consideration of soil as well as climatic variability in crop-climate impact studies

Modelling and predicting the hydrological processes associated with discontinuous, perennial crop growing areas, like the French vine-cultivated Mediterranean region, requires detailed quantitative information on spatial crop structure. A method was developed to provide such information by spatial frequency analysis on very high spatial resolution data. A simple crop geometry model, based on general knowledge and field observations was applied to the Fourier power spectrum of aerial colour imagery obtained over the La Peyne valley (Herault, France). This method, applied on a per-field basis, using a digital land register map, allowed the identification of vineyards and characterization of their crop spacing, orientation and training mode. Results showed good performances of this procedure under all conditions encountered, i.e. very variable soil surface optical properties and spatial structure. The main vineyard training modes, goblet and wire-trained, as well as orchards and continuous crop/fallow fields were well classified. Only non-standard, intermediate vine training modes were not always correctly recognized. Additionally, the frequency analysis provided, may be useful to environmental studies. The geometrical accuracy of image warping and minimal field size and width required by the method are discussed.