MAHALLIY SHAROITDA SUV TEJOVCHI TEXNOLOGIYALARNI KOʻP MEZONLI BAHOLASHNING IQTISODIY YOʻNALTIRILGAN GIBRID MODELI VA UNING AMALIY QOʻLLANILISHI
DOI:
https://doi.org/10.5281/zenodo.18663004Keywords:
suv tejovchi texnologiyalar, fuzzy-AHP–TOPSIS–DEA gibrid modeli, iqtisodiy samaradorlik, tomchilatib sug‘orish, suv unumdorligi, rebound effect, Markaziy Osiyo, O‘zbekiston, stokastik simulyatsiyaAbstract
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.
References
1. FAO. (2024). Global assessment of salt-affected soils. https://www.fao.org/newsroom/detail/fao-launches-first-majorglobal-
assessment-of-salt-affected-soils-in-50-years/en
2. Hajkowicz, S. (2008). A comparison of 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.2023.137890
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/su14148375
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/10.1051/e3sconf/202124402047
9. Khamrayev, Sh., et al. (2023). Suv tejovchi texnologiyalar: iqtisodiy tahlil. O‘zbekiston qishloq xo‘jaligi jurnali, 45(2),
112–125.
10. Bobojonov, I., & Aw-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
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