Mapping a livestock-oriented agricultural production system classification for the developing regions of the world
MetadataShow full item record
Kruska, R.L.; Reid, R.S.; Thornton, P.K.; Henninger, N.; Kristjanson, P.M. 2003. Mapping a livestock-oriented agricultural production system classification for the developing regions of the world. Agricultural Systems. vol. 77. p. 39-63.
Permanent link to this item: http://hdl.handle.net/10568/665
Questions as to whether public investment in international agricultural research is a ‘Good Thing’ or not may best be addressed using two arguments: (1) justifications based on whether or not past investments have yielded substantial benefits to societies and the resource-poor; and (2) that future investments need to be made as effectively and efficiently as possible, which means they must be targeted as closely as possible. A major component of any impact assessment framework that aspires to comprehensiveness is information on the location of different agricultural systems and pertinent characteristics of the resource-poor who operate them. Given the importance of livestock to the diets and incomes of poor farming households, and the predicted increase in demand for livestock products throughout the developing world over the next few decades, understanding how livestock fit into these systems, and how these systems may evolve in the future, is critical. This is especially true in Africa, where approximately 27% (162 million people) of the world's poor livestock keepers live. In this paper, we further develop a global livestock production system classification put forward by Seré and Steinfeld in 1996. These livestock systems fall into four categories: landless systems, livestock only/rangeland-based systems (areas with minimal cropping), mixed rainfed systems (mostly rainfed cropping combined with livestock) and mixed irrigated systems (a significant proportion of cropping uses irrigation and is interspersed with livestock). We then describe a method for mapping the classification, based on agro-climatology (length of growing period), land cover, and human population density. We conclude with a discussion of how the maps could be refined, and indicate their potential use in a range of different policy and research and development applications