The objective of our study is twofold: on one side, to complement earlier analyses that estimate the spatial
density of livestock holdings using different methods; on the other, to show that by combining different data
sources—the 2009/10 Uganda National Panel Survey (UNPS) and the 2008 Uganda National Livestock Census
(UNLC)—and applying the Small Area Estimation (SAE) technique, it is possible to provide a finer spatial
disaggregation and representation of missing livestock measures in the census. First, we combine our livestock
population and density figures with those from the UNLC. Second, we fit an estimation model of livestock income
and share on the UNPS to generate an out-of-sample prediction of the missing information in the UNLC, mapping
livestock income and share at the local level. Our results suggest that the integrated use of multiple data sources,
such as household surveys, censuses, and administrative data, together with spatial analysis techniques, such as
SAE, can provide reliable, coherent, and location-specific insights to guide policy and investment. This work shows
a useful method that allows for a reliable spatial livestock analysis, whenever sectorial databases offer greater
coverage of the population of interest, but more limited information than specialized surveys. This method can be
applied in all countries where there is a similar livestock information system, and common support between
livestock census and household surveys with detailed agricultural/livestock modules. Cross-validation across data
sources provides clearer insights into livestock-related policy and a better springboard for effective povertyreduction
strategies.