MAHALLIY SHAROITDA SUV TEJOVCHI TEXNOLOGIYALARNI KOʻP MEZONLI BAHOLASHNING IQTISODIY YOʻNALTIRILGAN GIBRID MODELI VA UNING AMALIY QOʻLLANILISHI
DOI:
https://doi.org/10.5281/zenodo.23181429Abstract
Markaziy Osiyoning arid hududlarida suv resurslarining degradatsiyasi qishloq xo‘jaligi barqarorligiga jiddiy tahdid solmoqda. Irrigatsiya umumiy suv iste’molining 80–90% ini tashkil etadi, sho‘rlanish va yer osti suvlari darajasining pasayishi esa hosildorlikni 15–45% ga kamaytirmoqda. Tadqiqotda mahalliy sharoitda suv tejovchi texnologiyalarni tanlash uchun iqtisodiy yo‘naltirilgan fuzzy-AHP–TOPSIS–DEA gibrid modeli ishlab chiqildi va Xorazm hamda Farg‘ona viloyatlari ma’lumotlari asosida sinovdan o‘tkazildi. Model iqtisodiy mezonlarga 60%, ekologik omillarga 25% va ijtimoiy-texnik mezonlarga 15% og‘irlik beradi. Natijalar shuni ko‘rsatdiki, tuproq namligi sensorlari bilan jihozlangan tomchilatib sug‘orish tizimi eng yuqori kompleks bahoga ega (TOPSIS = 0,82; DEA = 1,00), IRR 23,4%, NPV 1 320 USD/ga (10 yil, 7% diskont). Suv unumdorligi 47% ga oshib, sho‘rlanish ΔECe = –1,2 dS m⁻¹ ga kamaydi. Monte-Karlo simulyatsiyasi (10 000 iteratsiya; ±25% narx va iqlimiy o‘zgarishlar) modelning 94% holatda barqarorligini tasdiqladi. 35% subsidiya sharoitida texnologiya qamrovi 18% dan 68% ga oshishi va yillik 620 mln USD makroiqtisodiy foyda keltirishi mumkin. Taklif etilgan model suv tanqisligi sharoitida agrar siyosatni optimallashtirish uchun ilmiy asos yaratadi.Keywords
suv tejovchi texnologiyalar, fuzzy-AHP–TOPSIS–DEA gibrid modeli, iqtisodiy samaradorlik, tomchilatib sug‘orish, suv unumdorligi, rebound effect, Markaziy Osiyo, O‘zbekiston, stokastik simulyatsiya.References
1. FAO. (2024). Global assessment of salt-affected soils. https://www.fao.org/newsroom/detail/fao-launches-first-major-global-assessment-of-salt-affected-soils-in-50-years/en
2. Hajkowicz, S. (2008). Outranking and multiple criteria analysis techniques. https://doi.org/10.1007/s11269-007-9193-5
3. Lee, Y.-C., et al. (2023). Fuzzy-based multi-criteria decision-making for water systems. https://doi.org/10.1016/j.jclepro.2023.137380
4. Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research. https://doi.org/10.1016/0377-2217(78)90138-8
5. Qadir, M., et al. (2014). Economics of salt-induced land degradation and restoration. https://doi.org/10.1111/1477-8947.12054
6. Djanibekov, U., et al. (2022). Rebound effects in irrigated agriculture. https://doi.org/10.3390/agriculture12010165
7. Ibrakhimov, M., et al. (2021). Reclamation of saline soils in Khorezm region. https://doi.org/10.1016/j.agsy.2021.103135
8. Isaev, S., et al. (2021). Water-saving technologies in cotton production. https://doi.org/10.1016/j.aquaculture.2024.024047
9. Khamrayev, Sh., et al. (2023). Suv tejovchi texnologiyalar: iqtisodiy tahlil. O‘zbekiston qishloq xo‘jaligi jurnali, 45(2), 112–125.
10. Bobojonov, I., & Hassan, A. (2014). Climate change and farm-level adaptation in Central Asia. https://doi.org/10.1016/j.gloenvcha.2014.01.003
11. World Bank. (2025). National Irrigation Modernization Program. https://documents.worldbank.org/
12. EDB (Eurasian Development Bank). (2023). Efficient Irrigation in Central Asia. https://eabr.org/upload/iblock/632/EDB_2023_Report-4_Irrigation_eng.pdf
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 GREEN ECONOMY AND DEVELOPMENT

This work is licensed under a Creative Commons Attribution 4.0 International License.


