As a typical arid and semi-arid region, Central Asia is subject to varying degrees of hydrothermal constraints and environmental limitations for sustainable land and agricultural development. Analysis and prediction of land use potential is essential to guarantee regional food security and reduce the adverse effects of climate change. This dataset is oriented to the sustainable agricultural development of five Central Asian countries, and the potential evaluation of land use and agroecology from the perspective of land resource development and utilization potential is carried out with dry farming, irrigated agriculture, forestry, and grass-pastoralism as land use targets. The multi-objective land resource development and utilization evaluation factors include climate (heat and water resources), topography, irrigation and water extraction conditions, and soil conditions, which are greater than 10℃ cumulative temperature, average temperature in January, average temperature in July, precipitation, precipitation variation coefficient, elevation, slope, water extraction distance, groundwater level, soil organic matter, soil texture, soil acidity and alkalinity, among which the precipitation variation coefficient is based on The precipitation variation coefficient is based on precipitation conversion, and the slope information is extracted from the elevation data. Variable climate elements including future monthly-scale precipitation, mean temperature, maximum and minimum air temperature, and humidity are derived from bias-corrected and downscaled CMIP6's ACCESS-CM2, BCC-CSM2-MR, CanESM5, CAS-ESM2-0, CESM2-WACCM, EC-Earth3, GFDL-ESM4, KACE-1-0-G multi-model ensemble averaged data with experiments of r1i1p1f1. This data can provide a basis for future land resources development and utilization, agricultural development, etc. in the five Central Asian countries. The data can provide basic data support for the future development and utilization of land resources and agricultural development in five Central Asian countries.

File naming and required software

In GeoTiff format, you can use ArcMap to open, view and analyze. The file is named as "year+type of land development and utilization in Central Asia+spatial distribution map of potential".

Data Citations Data citation guideline What's data citation?

Cite as:

Yao, L., Zhou, H. (2022). Dataset of future land resources suitability in Central Asia (V1.0). A Big Earth Data Platform for Three Poles, DOI: 10.11888/HumanNat.tpdc.273032. CSTR: 18406.11.HumanNat.tpdc.273032. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Yao, L.L., Zhou*, H.F., Yan, Y.J., & Su, Y. (2022). Projection of suitability for the typical agro-ecological types in Central Asia under four SSP-RCP scenarios. European Journal of Agronomy, 126599.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)