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 the code together with county-level livestock census data and corresponding environmental factor tables for the years 2000 and 2020 on the Qinghai-Tibet Plateau, which were used to construct the random forest models.