Random forest code for downscaling county-level livestock statistics to 1 km resolution using environmental factors

We introduce a random forest–based framework designed to downscale county-level livestock census data to a fine spatial resolution of 1 km by integrating key environmental factors (e.g., climate, topography, and vegetation indices). This approach addresses the spatial mismatch between administrative statistics and ecological processes, thereby providing more accurate and ecologically meaningful estimates of livestock distribution. We provide code for relevance analysis to screen environmental variables as well as code for the random forest model. The county-level livestock census data for the Qinghai-Tibet Plateau from 2000 and 2020 used to build the random forest model, along with the corresponding environmental factor tables, are also provided.