Issue |
E3S Web of Conf.
Volume 381, 2023
International Scientific and Practical Conference “Development and Modern Problems of Aquaculture” (AQUACULTURE 2022)
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Article Number | 01018 | |
Number of page(s) | 8 | |
Section | Agriculture, River Ecosystems and Environment | |
DOI | https://doi.org/10.1051/e3sconf/202338101018 | |
Published online | 14 April 2023 |
Risk identification and assessment of irrigated land erosion in Tashkent province, Uzbekistan
1 Tashkent State Agrarian University, 2, University street, Tashkent, 100140, Uzbekistan
2 National Research University “TIIAME”, 39, Kari Niyaziy street, Tashkent, 100000, Uzbekistan
* Corresponding author: n.kuchkarova@tdau.uz
Irrigated fields are important assets for agricultural development since they supply vital food and fiber to people all over the world. These areas are, however, also susceptible to erosion, which can result in the deterioration of the soil and a loss in production. It is crucial to identify and evaluate the erosion risk in irrigated areas in order to maintain sustainable agriculture and guarantee the resources' long-term availability. The objective of this study is to create a thorough system for recognizing and evaluating the erosion risk in irrigated areas. The methodology will take into account a number of variables, including climate, topography, soil properties, land management techniques, and other pertinent variables that affect the risk of erosion. To identify and assess the erosion hazard of irrigated lands of the farm named after. S. Rakhimov of the Chinaz district of the Tashkent province, Uzbekistan, we laid 4 key sites, depending on the steepness, length, slope exposure and sown crops, and mapped the soils at a scale of 1:1000. At each site, profiles were laid that sequentially cut all the elements of the slope, from the top to the plume, and all soil varieties found on this slope. In the alignment of the profiles, on all elements of the relief, 25 reference sections were laid, characterizing all soil varieties in this area. To identify the boundaries of the contours of individual soil varieties, a series of hollows and pits were laid. Soil morphology was studied on the reference sections, and soil samples were taken according to genetic horizons for subsequent analysis.
Key words: Remote sensing / machine learning / soil salinity map / salinity index
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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