Toward a time-series of global livestock data over 2000-2015

High spatial and temporal resolution information on environmental drivers (e.g. climate, primary productivity) are essential to many applications in ecology, economy and health sciences. Livestock densities in particular have an important role in agricultural, socio-economics, food security and epidemiology, thus knowing their spatial and temporal distribution is crucial. For this reason, several methodologies have been proposed to disaggregate livestock census data to continuous gridded density distribution (Wint and Robinson, 2007). However, these products referred only to a particular year and a temporal continuous product is still lacking. Using stratified random forest models (Gilbert et al., 2018) and a set of environmental predictors having a continuous temporal domain (Bontemps et al., 2013; Stevens et al., 2015; Karger et al., 2017; Zhang et al., 2017), we present a first attempt at generating a data product of the global geographic distribution of several livestock species (cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks) from 2000 to 2015 with a 0.083° spatial resolution (~10k at the equator). Beyond the immediate production of a set of temporally detailed data, we elaborated a methodological framework that we can apply again as new data time points become available. The ability to study livestock distribution and associated processes with temporal depth will be valuable considering the profound changes affecting this activity globally