Artikel

Gridded livestock density database and spatial trends for Kazakhstan

Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSK D ) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSK D database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000–2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

Sprache
Englisch

Erschienen in
Journal: Scientific Data ; ISSN: 2052-4463 ; Volume: 10 ; Year: 2023 ; Pages: 1-15 ; Berlin: Springer Nature

Klassifikation
Wirtschaft

Ereignis
Geistige Schöpfung
(wer)
Kolluru, Venkatesh
John, Ranjeet
Saraf, Sakshi
Chen, Jiquan
Hankerson, Brett
Robinson, Sarah
Kussainova, Maira
Jain, Khushboo
Ereignis
Veröffentlichung
(wer)
Springer Nature
(wo)
Berlin
(wann)
2023

DOI
doi:10.1038/s41597-023-02736-5
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Kolluru, Venkatesh
  • John, Ranjeet
  • Saraf, Sakshi
  • Chen, Jiquan
  • Hankerson, Brett
  • Robinson, Sarah
  • Kussainova, Maira
  • Jain, Khushboo
  • Springer Nature

Entstanden

  • 2023

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