Remote Sensing Applications for Pasture Assessment in Kazakhstan

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works.

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Publication Details

Journal
Agronomy
Published
2025-02-21
DOI
https://doi.org/10.3390/agronomy15030526
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43
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article

Remote Sensing Applications for Pasture Assessment in Kazakhstan

Asset Arystanov, Beybit Nasıyev, Jay Sagin, Gulnara Kabzhanova et al.
2 citations
Agronomy
Remote Sensing in Agriculture
3.43
article

Remote Sensing Applications for Pasture Assessment in Kazakhstan

Asset Arystanov, Beybit Nasıyev, Jay Sagin, Gulnara Kabzhanova, Anuarbek Bissembayev, Aisulu Kurmasheva, Ranida Arystanova
article en
2 citations

Abstract

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works.

AgronomyVol. 15(3)
Western Michigan University (US), Kazakh-British Technical University (KZ), Al-Farabi Kazakh National University (KZ), Zhangir Khan West Kazakhstan Agrarian Technical University (KZ)
Zero hunger
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
3.43
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