ArticleScientific data2023
High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A comparative study of gut microbiota and metabolites in Tibetan sheep during cold and warm seasons.Frontiers in veterinary science · 2026Article
- Climate Change vs. Human Activities: Conflicting Future Impacts on a High-Altitude Endangered Snake (Biology · 2025Article
- The Combination of Safety, Attractiveness, and Accessibility Lead to Bias in Inventory of Wetland Plants on the Qinghai-Tibet Plateau.Ecology and evolution · 2025Article
- A long-term high-resolution dataset of grasslands grazing intensity in China.Scientific data · 2024Article
- Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest.Scientific reports · 2024Article
- Gridded livestock density database and spatial trends for Kazakhstan.Scientific data · 2023Article
- Epidemiological Investigation of Yak (Animals : an open access journal from MDPI · 2023Article
- High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020.Scientific data · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world's most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region.
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