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Assessment of Soil Salinity Changes under the Climate Change in the Khorezm Region, Uzbekistan

Author

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  • Mukhamadkhan Khamidov

    (Department of Irrigation and Melioration, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University (“TIIAME” NRU), Kary-Niyaziy 39, Tashkent 100000, Uzbekistan
    These authors contributed equally.)

  • Javlonbek Ishchanov

    (Department of Irrigation and Melioration, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University (“TIIAME” NRU), Kary-Niyaziy 39, Tashkent 100000, Uzbekistan
    These authors contributed equally.)

  • Ahmad Hamidov

    (Department of Irrigation and Melioration, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University (“TIIAME” NRU), Kary-Niyaziy 39, Tashkent 100000, Uzbekistan
    Research Area 3 “Agricultural Landscape Systems”, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374 Müncheberg, Germany)

  • Cenk Donmez

    (Research Area 3 “Agricultural Landscape Systems”, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374 Müncheberg, Germany
    Landscape Architecture Department, Remote Sensing and GIS Lab, Cukurova University, Adana 01330, Turkey)

  • Kakhramon Djumaboev

    (Regional Representative Office for Central Asia, International Water Management Institute (IWMI), Apartment 120, House 6, Osiyo Street, Tashkent 100000, Uzbekistan)

Abstract

Soil salinity negatively affects plant growth and leads to soil degradation. Saline lands result in low agricultural productivity, affecting the well-being of farmers and the economic situation in the region. The prediction of soil salinization dynamics plays a crucial role in sustainable development of agricultural regions, in preserving the ecosystems, and in improving irrigation management practices. Accurate information through monitoring and evaluating the changes in soil salinity is essential for the development of strategies for agriculture productivity and efficient soil management. As part of an ex-ante analysis, we presented a comprehensive statistical framework for predicting soil salinity dynamics using the Homogeneity test and linear regression model. The framework was operationalized in the context of the Khorezm region of Uzbekistan, which suffers from high levels of soil salinity. The soil salinity trends and levels were projected under the impact of climate change from 2021 to 2050 and 2051 to 2100. The results show that the slightly saline soils would generally decrease (from 55.4% in 2050 to 52.4% by 2100 based on the homogeneity test; from 55.9% in 2050 to 54.5% by 2100 according to the linear regression model), but moderately saline soils would increase (from 31.2% in 2050 to 32.5% by 2100 based on the homogeneity test; from 31.2% in 2050 to 32.4% by 2100 according to the linear regression model). Moreover, highly saline soils would increase (from 13.4% in 2050 to 15.1% by 2100 based on the homogeneity test; from 12.9% in 2050 to 13.1% by 2100 according to the linear regression model). The results of this study provide an understanding that soil salinity depends on climate change and help the government to better plan future management strategies for the region.

Suggested Citation

  • Mukhamadkhan Khamidov & Javlonbek Ishchanov & Ahmad Hamidov & Cenk Donmez & Kakhramon Djumaboev, 2022. "Assessment of Soil Salinity Changes under the Climate Change in the Khorezm Region, Uzbekistan," IJERPH, MDPI, vol. 19(14), pages 1-13, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8794-:d:866686
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    References listed on IDEAS

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    2. A. N. Pettitt, 1979. "A Non‐Parametric Approach to the Change‐Point Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 126-135, June.
    3. World Bank, 2013. "Uzbekistan : Overview of Climate Change Activities," World Bank Publications - Reports 17550, The World Bank Group.
    4. Miller, Jennifer & Franklin, Janet & Aspinall, Richard, 2007. "Incorporating spatial dependence in predictive vegetation models," Ecological Modelling, Elsevier, vol. 202(3), pages 225-242.
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