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 exports. The complexity of trade in meat, feed, water, and nitrogen is illustrated by the dual roles of the US and The Netherlands as both importers and exporters of meat. Mitigation of environmental damage from industrialized livestock production and trade depends on a combination of direct-pricing strategies, regulatory approaches, and use of best management practices. Our analysis indicates that increased water- and nitrogen-use efficiency and land conservation resulting from these measures could significantly reduce resource costs.
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.
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.
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