Testing new approaches to mapping livestock variables in Uganda : integrating census and survey data : Intégrer les donnees du Recensement et de l'enquete : tester de nouvelles approches de mappage des variables de l'elevage en Ouganda (French)
There is evidence that livestock provide a multitude of livelihood services to farm households. They are a source of food, cash income, draught power and hauling services, saving and insurance, dung, and social capital. There is also evidence that a large... See More +
There is evidence that livestock provide a multitude of livelihood services to farm households. They are a source of food, cash income, draught power and hauling services, saving and insurance, dung, and social capital. There is also evidence that a large share of rural households keep livestock. Targeted investments in the sector could thus definitely contribute to improved livelihoods and a decline in rural poverty. Data integration, the combined use of data from different sources, is an effective way to derive detailed policy and investment indications, which could not be obtained by using individual datasets on their own. The brief presents three sets of results. First, the density of large ruminants at the sub-county level are predicted and then compared to actual values in the census. The other two estimated models allow predicting and mapping livestock per capita income and household's share of income from livestock at sub-county level. In conclusion, data integration is essential for effective investment decisions because it generates information from surveys, which, on their own, cannot be produced. For data integration to be feasible it is important that the various methods of data collection be consistent. Finally, the timing of the various surveys is also relevant as it may be of little use to compare data from surveys undertaken in different years, particularly when addressing issues pertaining to fast, or relatively fast growing sectors.
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