Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. Current challenges involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure or by inferring simultaneously environmental and demographic effects. Here we present Sam!ada, an integrated software for landscape genomic analysis of large datasets. This tool was developed in the framework of NextGen with the objective of characterising traditional Ugandan cattle breeds using single nucleotide polymorphisms (SNPs) data.