Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest.

Yaping Liu, Juanle Wang, Keming Yang, Altansukh Ochir

Abstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Yaping LiuCollege of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China.

Juanle WangState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. wangjl@igsnrr.ac.cn.

Keming YangCollege of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China.

Altansukh OchirEnvironmental Engineering Laboratory, Department of Environment and Forest Engineering, School of Engineering and Applied Sciences and Institute for Sustainable Development, National University of Mongolia, Ulaanbaatar, 14201, Mongolia.

Funding

Construction Project of China Knowledge Center for Engineering Sciences and Technology CKCEST-2023-1-5Key Project of Innovation LREIS KPI006Key R&D and Achievement Transformation Plan Project in Inner Mongolia Autonomous Region No. 2023KJHZ0027Mongolian Foundation for Science and Technology CHN2022/276Mongolian Foundation for Science and Technology NSFC_2022/01National Key R&D Program of China 2022YFE0119200National Nature Science Foundation of China 32161143025Science & Technology Fundamental Resources Investigation Program of China No.2022FY101905

6 · The paper itself

Abstract

Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0-125 sheep/km

Identifiers

PMID38750227

DOI10.1038/s41598-024-61959-7

PMCPMC11096379

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.