Measuring the sustainability of crop-livestock systems in sub-Saharan Africa: methods and data requirements
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Permanent link to this item: http://hdl.handle.net/10568/2782
Internet URL: https://cgspace.cgiar.org/handle/10568/401
Livestock are an important component of farming systems in sub-Saharan Africa. They are raised mainly for meat, milk and skin and provide a flexible financial reserve in years of crop failure. They also play a critical role in the agricultural intensification process by providing draft power and manure for crop production. With increasing human population and economic changes, cultivated areas in many sub-Saharan African countries have expanded on to marginal lands and fallow period are being shortened. As a result, large areas of land have been degraded and crop and animal yields have fallen. Improved crop-livestock production systems and technologies are currently being developed in response to the growing demand for food and the degradation of the natural resource base. These technologies must enhance food production; they also need to maintain ecological stability and preserve the natural resource base, i.e. they must be sustainable. However, the notion of sustainability has been of limited operational use to policy makers and researchers attempting to evaluate new technologies and/or determine the effects of various policies and technologies. This paper discusses a methodology for measuring the sustainability and economic viability of crop-livestock systems. The approach is based on the concept of intertemporal and interspatial total factor productivity, paying particular attention to the International Livestock Centre for Africa (ILCA). Intertemporal and interspatial total factor productivity indices are computed for three farming systems in south-western Nigeria. Results show that the sustainability and economic viability measures are sensitive to changes in the stock and flow of soil nutrients as well as to material inputs and outputs. The advantage of this approach is that intertemporal and interspatial total factor productivity measures are computed using only price and quality data, thus eliminating the need for econometric estimation.