title: Using Random Forest to Improve the Downscaling of Global Livestock Census Data.
abstract: Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.
authors.affiliation: Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium.|Fonds National de la Recherche Scientifique, Brussels, Belgium.
authors.forename: Gaëlle
authors.initials: G
authors.lastname: Nicolas
authors.affiliation: International Livestock Research Institute (ILRI), Livestock Systems and Environment (LSE), Nairobi, Kenya.
authors.forename: Timothy P
authors.initials: TP
authors.lastname: Robinson
authors.affiliation: Environmental Research Group Oxford (ERGO) - Department of Zoology, University of Oxford, Oxford, United Kingdom.
authors.forename: G R William
authors.initials: GR
authors.lastname: Wint
authors.affiliation: Animal Production and Health Division (AGA), Food and Agriculture Organization of the United Nations (FAO), Rome, Italy.
authors.forename: Giulia
authors.initials: G
authors.lastname: Conchedda
authors.affiliation: Animal Production and Health Division (AGA), Food and Agriculture Organization of the United Nations (FAO), Rome, Italy.
authors.forename: Giuseppina
authors.initials: G
authors.lastname: Cinardi
authors.affiliation: Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium.|Fonds National de la Recherche Scientifique, Brussels, Belgium.
authors.forename: Marius
authors.initials: M
authors.lastname: Gilbert
mesh_terms: D000818:Animals; D016208:Databases, Factual; D058751:Livestock; D008962:Models, Theoretical