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Pig production has a high potential for increasing household incomes of both rural and urban smallholder farmers. However, this opportunity is constrained by the high cost of feeds and its scarcity. As such, farmers usually feed pigs on multiple feed resources within their reach. Although the diversity of pig feeds is important in overcoming the challenges, there is limited information on how this can be achieved, and the factors influencing it. Using a cross-section of 240 pig farming households, this study assessed how socio-demographic, technical and institutional factors influenced household pig feed diversity. Statistical analysis was performed at p < 0.1, p < 0.05 and p < 0.01), respectively. Results showed that marital status (p = 0.012), household size (p = 0.085), number of crop (p = 0.008) and other livestock enterprises (p = 0.055), ownership of mobile phone (p = 0.091), buying feeds (p = 0.005), and use of radio (p = 0.032) and friend as sources (p = 0.002) of agricultural information had a positive significant influence on household pig feed diversity. Additionally, age (p = 0.042), land size owned (p = 0.085) and breed [rearing local (p = 0.009) and crosses (p = 0.000) as opposed to exotic breeds] had a negative significant influence on household pig feed diversity. The study recommends that focus should be placed on improving access to farm inputs, agricultural and market information for smallholder pig farmers so as to improve pig feed diversity and subsequently enhance production and productivity. Radio and mobile phone technologies are opportunities that can support information sharing.

African swine fever (ASF) is a highly contagious, lethal and economically devastating haemorrhagic disease of domestic pigs. Insights into the dynamics and scale of virus transmission can be obtained from estimates of the basic reproduction number (R0). We estimate R0 for ASF virus in small holder, free-range pig production system in Gulu, Uganda. The estimation was based on data collected from outbreaks that affected 43 villages (out of the 289 villages with an overall pig population of 26,570) between April 2010 and November 2011. A total of 211 outbreaks met the criteria for inclusion in the study. Three methods were used, specifically; (i) GIS- based identification of the nearest infectious neighbour based on the Euclidean distance between outbreaks, (ii) epidemic doubling time, and (iii) a compartmental susceptible-infectious (SI) model. For implementation of the SI model, three approaches were used namely; curve fitting (CF), a linear regression model (LRM) and the SI/N proportion. The R0 estimates from the nearest infectious neighbour and epidemic doubling time methods were 3.24 and 1.63 respectively. Estimates from the SI-based method were 1.58 for the CF approach, 1.90 for the LRM, and 1.77 for the SI/N proportion. Since all these values were above one, they predict the observed persistence of the virus in the population. We hypothesize that the observed variation in the estimates is a consequence of the data used. Higher resolution and temporally better defined data would likely reduce this variation. This is the first estimate of R0 for ASFV in a free range smallholder pig keeping system in sub-Saharan Africa and highlights the requirement for more efficient application of available disease control measures.