Gridded livestock density database and spatial trends for Kazakhstan.

Area of Science:

• Environmental Science
• Agricultural Science
• Geospatial Analysis

Background:

• Livestock rearing is vital for livelihoods in dryland Asia, but increasing livestock density (LSK_D) poses ecological and health risks.
• Existing livestock data lacks high-resolution spatial distribution, hindering effective management and research.
• Understanding livestock density is critical for ecosystem health, climate change adaptation, and disease prevention.

Purpose of the Study:

• To develop a high-resolution (1 km) gridded livestock density database for horses, sheep, and goats in Kazakhstan.
• To analyze the spatial distribution and temporal changes of livestock density from 2000-2019.
• To identify factors influencing livestock distribution for improved downscaling of data.

Main Methods:

• Employed random forest (RF) regression modeling to create the gridded livestock density database.
• Integrated vegetation proxies, climatic, socioeconomic, topographic, and proximity variables.
• Utilized district-level census data, downscaled using factors like population density and nighttime lights.

Main Results:

• Identified high-density livestock hotspots in south-central and southeastern Kazakhstan.
• Observed medium-density clusters in northern and northwestern regions.
• Demonstrated the effectiveness of population density, proximity to settlements, nighttime lights, and temperature in downscaling livestock data.

Conclusions:

• The developed high-resolution gridded livestock database provides crucial insights into livestock distribution in Kazakhstan.
• This resource will support stakeholders, researchers, land managers, and policymakers in regional and national planning.
• The findings highlight the importance of integrated data and modeling for effective livestock management and environmental stewardship.