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ARS Home » Plains Area » Mandan, North Dakota » Northern Great Plains Research Laboratory » Research » Publications at this Location » Publication #422770
Research Project: Transdisciplinary Research that Improves the Productivity and Sustainability of Northern Great Plains Agroecosystems and the Well-Being of the Communities They Serve

Location: Northern Great Plains Research Laboratory

Title: A geospatial model of livestock carrying capacity in the Akmola Oblast, Kazakhstan

Author
item QI, JIANGUO - Michigan State University
item LIN, ZIHAN - Cleveland State University
item Weltz, Mark
item SPAETH, KEN - Natural Resources Conservation Service (NRCS, USDA)
item Nesbit, Jason
item Toledo, David
item ISKAKOVA, GULNAZ - Kazakh National Agrarian University
item YESPOLOV, TLEKLES - Kazakh National Agrarian University
item KUSSAINOVA, MAIRA - Kazakh National Agrarian University
item MAKHMUDOVA, LYAZZAT - Kazakh National Agrarian University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/2025
Publication Date: 4/21/2025
Citation: Qi, J., Lin, Z., Weltz, M.A., Spaeth, K., Nesbit, J.E., Toledo, D.N., Iskakova, G., Yespolov, T., Kussainova, M., Makhmudova, L. 2025. A geospatial model of livestock carrying capacity in the Akmola Oblast, Kazakhstan. Remote Sensing. https://doi.org/10.3390/rs17081477.
DOI: https://doi.org/10.3390/rs17081477

Interpretive Summary: In Kazakhstan, rangeland conditions vary widely, making it hard to assess Livestock Carrying Capacity (LCC), which is important for food security and economic planning. This research uses remote sensing technology to develop a model that tracks LCC changes over time and space at a regional level. By combining satellite data with on-the-ground measurements, the study creates a geospatial model to assess LCC in the Akmola oblast. The model successfully captured rangeland conditions and showed how LCC can change annually due to factors like rainfall. This approach provides valuable insights for planning livestock management and for investments. The model can also be adapted to other regions in Kazakhstan.

Technical Abstract: Significant spatial disparities exist in rangeland conditions throughout Kazakhstan, posing challenges in field-based assessments of Livestock Carrying Capacity (LCC) crucial for food security and economic planning. Leveraging the capabilities of remote sensing, this research aims to develop a geospatial modeling approach that captures the spatial and temporal dynamics of LCC at the oblast level in Kazakhstan. Integrating remotely sensed information, capturing nuanced spatial patterns in vegetation, water resources, and other critical geographic characteristics, with in-situ measurements enables scaling up from small plot levels to larger areas. This paper uses a case study in the Akmola oblast to develop and validate a geospatial livestock carrying capacity (GLCC) model and demonstrate the usefulness and value of remotely sensed data for rangeland analyses. The results demonstrate the feasibility and efficacy of this integrated approach to quantitatively assess the LCC for the entire Akmola oblast and to capture the spatial patterns of rangeland conditions. Furthermore, the model also captured substantial annual variations in LCC, influenced by changing rainfall patterns, offering valuable insights for risk assessment in future livestock investment strategies. Grounded in established geospatial techniques and utilizing publicly available data, this GLCC is adaptable for application in other regions across the country, subject to model parameter adjustments and validation.