Background: Changes in land use and associated ecosystem change have been described as one of the causal drivers in emerging and re-emerging of infectious diseases, but there is a notable scarcity of scientific knowledge to show whether, and how, land use change plays this role. Land use change may include the invasion of non-native woody species. We studied how and to what extent Prosopis juliflora, a most powerful invasive woody species, influences the prevalence of bovine tuberculosis (bTB) in cattle in the Afar Region, Ethiopia between November 2013 and April 2016. We examined the potential underlying mechanisms by which ecological consequences of land use, such as an invading woody species, alters the risk of bTB transmission. Methods: A total of 2550 cattle from 102 herds were investigated for the presence of bTB using the comparative intradermal tuberculin test (CITT). Landsat images from 2014 were used to quantify the proportion of different land use types by applying a k-means unsupervised classification, and analyzing this within a buffer of 16km from the location of each cattle herd. A generalized linear model was used to quantify the relationship between bTB prevalence and the proportion of land use types. Then, multiple regression tree analyses were used to identify the most important land use predictor accounting for the variation in bTB prevalence. Results: A model averaging analyses identified the proportion of P. juliflora as a significant risk factor for increasing bTB prevalence in cattle (b=12.2, 95%CI=8.9-15.5, p<0.001), and multiple regression tree analysis identified the proportion of Prosopis as the most important land use predictor of bTB in cattle. Conclusions: The loss in host species evenness and the increase in cattle movement as a consequence of the loss of palatable grasses in Prosopis areas could be potential mechanisms accounting for the observed higher bTB prevalence in these areas. Given the projected spread of Prosopis, land use changes and associated changes in host community composition could affect the risk of infectious diseases, which is important for decision makers when formulating disease control strategies.