With the increasing availability of both molecular and topo‐climatic data, the main challenges facing landscape genomics – that is the combination of landscape ecology with population genomics – include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large‐scale genetic and environmental data sets. samβada identifies candidate loci using genotype–environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype–environment associations. In addition, samβada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with samβada, bayenv, lfmm and an F_ST outlier method (FDIST approach in arlequin) and compare their results. samβada – an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada – outperforms other approaches and better suits whole‐genome sequence data processing.

Livestock conservation practice is changing rapidly in light of policy, climate change and market demands. The last decade saw a step change in technological and analytical approaches to define, manage and conserve Farm Animal Genomic Resources (FAnGR). These changes pose challenges for FAnGR conservation in terms of technological continuity, analytical capacity and the methodologies needed to exploit new, multidimensional data. The ESF Genomic Resources program final conference addressed these problems attempting to contribute to the development of the research and policy agenda for the next decade. We broadly identified four areas related to methodological and analytical challenges, data management and conservation. The overall conclusion is that there is a need for the use of current state-of-the-art tools to characterise the state of genomic resources in non-commercial and local breeds. The livestock genomic sector, which has been relatively well-organised in applying such methodologies so far, needs to make a concerted effort in the coming decade to enable to the democratisation of the powerful tools that are now at its disposal, and to ensure that they are applied in the context of breed conservation as well as development.

Uganda is a low‐income country with the largest pig population in East Africa. Pig keeping has a large potential, commercially and as a tool for poverty reduction, but African swine fever (ASF) is a major hurdle for development of the sector. The objective of this study was to evaluate knowledge, attitudes and practices related to ASF in the smallholder pig production value chain in northern Uganda. The study included three separate series of participatory rural appraisals (PRA), comprising purposively selected farmers and other actors in the pig production value chain. In the PRAs, various participatory epidemiology tools were used. A total of 49 PRAs and 574 participants, representing 64 different villages, were included. The results indicate that participants were well aware of the clinical signs of ASF, routes for disease spread and measures for disease control. However, awareness of the control measures did not guarantee their implementation. A majority of middlemen and butchers acknowledged having sold live pigs, carcasses or pork they believed infected with ASF. Outbreaks of ASF had a strong negative impact on participants’ socio‐economic status with loss of revenue and reversal into more severe poverty. In conclusion, lack of knowledge is not what is driving the continuous circulation of ASF virus in this setting. To control ASF and reduce its impact, initiatives that stimulate changes in management are needed. Because the behaviour of all actors in the value chain is largely influenced by the deep rural poverty in the region, this needs to be combined with efforts to reduce rural poverty.

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.

The African swine fever virus (ASFV) is currently causing a world-wide pandemic of a highly lethal disease in domestic swine and wild boar. Currently, recombinant ASF live-attenuated vaccines based on a genotype II virus strain are commercially available in Vietnam. With 25 reported ASFV genotypes in the literature, it is important to understand the molecular basis and usefulness of ASFV genotyping, as well as the true significance of genotypes in the epidemiology, transmission, evolution, control, and prevention of ASFV. Historically, genotyping of ASFV was used for the epidemiological tracking of the disease and was based on the analysis of small fragments that represent less than 1% of the viral genome. The predominant method for genotyping ASFV relies on the sequencing of a fragment within the gene encoding the structural p72 protein. Genotype assignment has been accomplished through automated phylogenetic trees or by comparing the target sequence to the most closely related genotyped p72 gene. To evaluate its appropriateness for the classification of genotypes by p72, we reanalyzed all available genomic data for ASFV. We conclude that the majority of p72-based genotypes, when initially created, were neither identified under any specific methodological criteria nor correctly compared with the already existing ASFV genotypes. Based on our analysis of the p72 protein sequences, we propose that the current twenty-five genotypes, created exclusively based on the p72 sequence, should be reduced to only six genotypes. To help differentiate between the new and old genotype classification systems, we propose that Arabic numerals (1, 2, 8, 9, 15, and 23) be used instead of the previously used Roman numerals. Furthermore, we discuss the usefulness of genotyping ASFV isolates based only on the p72 gene sequence.