تأثير تغييرات اقليمي بر صنعت دام و طيور در ايران: تحليل ريسک استاني تنش‌هاي گرمايي و خشکي
محورهای موضوعی : مدیریت منابع آب
هادی رمضانی اعتدالی
1
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سکینه کوهی
2
1 - استاد، گروه علوم و مهندسي آب، دانشکده کشاورزي و منابع طبيعي، دانشگاه بين المللي امام خميني (ره)، قزوين، ايران.
2 - دکتري مهندسي منابع آب، گروه علوم و مهندسي آب، دانشکده کشاورزي و منابع طبيعي، دانشگاه بين¬المللي امام خميني (ره)، قزوين، ايران.
کلید واژه: تنش گرمايي, شاخص دومارتن, دامداري, مرغداري, TOPSIS,
چکیده مقاله :
زمينه و هدف: تغييرات اقليمي با تشديد تنش‌هاي گرمايي و خشکي، چالش‌هاي جدي براي پايداري صنعت دام و طيور در مناطق خشک و نيمه‌خشک مانند ايران ايجاد نموده است. گاوداري‌ها و مرغداري‌ها به دليل وابستگي به منابع آب و خوراک و حساسيت بالاي حيوانات به دماهاي بالا، در برابر اين تنش‌ها به طور ويژه آسيب‌پذير مي‌باشند. اين مطالعه با هدف ارزيابي ريسک هم‌زمان تنش‌هاي گرمايي و خشکي اقليمي بر واحدهاي گاوداري و مرغداري در ۳۱ استان ايران، با استفاده از شاخص خشکي دومارتن (DMI) و روش تصميم‌گيري چندمعياره TOPSIS، تحت سناريوهاي اقليمي CMIP6 انجام شده است تا اطلاعات ارزشمندي براي سياست‌گذاري و توسعه راهکارهاي تطبيقي فراهم آورد.
روش پژوهش: در اين پژوهش، داده‌هاي دما و بارش از ۳۱ ايستگاه سينوپتيک در دوره پايه (۱۹۹۷–۲۰۱۴) و خروجي‌هاي ريزمقياس‌شده مدل‌هاي اقليمي (NEX-GDDP شامل: CNRM-CM6-1، CanESM5، GFDL-ESM4،HadGEM3-GC31-LL و MIROC6) تحت سناريوهاي اقليمي SSP2-4.5 و SSP5-8.5 براي دوره‌هاي آينده (۲۰۲۵–۲۰۴۹، ۲۰۵۰–۲۰۷۴، ۲۰۷۵–۲۰۹۹) مورد استفاده قرار گرفتند. شاخص خشکي دومارتن براي ارزيابي شرايط اقليمي و فراواني وقوع تنش گرمايي براي گاوداري‌ها (دماي بالاي ۲۶ درجه سانتي‌گراد) و مرغداري‌ها (دماي بالاي ۲۴ درجه سانتي‌گراد) محاسبه شد. سپس رتبه‌بندي ريسک استاني با روش TOPSIS و با در نظر گرفتن معيارهاي فراواني تنش گرمايي، شاخص خشکي و تعداد حيوانات در معرض خطر به انجام رسيد. براي ارزيابي دقت مدل‌هاي اقليمي، شاخص‌هاي آماري RMSE، MAE، MBE و CC استفاده شدند.
يافته‌ها: نتايج نشان داد که در دوره پايه، استان‌هاي جنوبي مانند خوزستان، هرمزگان و بوشهر (به‌ترتيب با 0/84، 3/58 و 9/51 درصد فراواني تنش گرمايي در گاوداري‌ها) در معرض ريسک بالاي اقليمي قرار دارند، درحالي‌که استان‌هاي شمالي مانند گيلان و اردبيل کمترين تنش را تجربه مي‌کنند. همچنين مرغداري‌ها به‌دليل حساسيت فيزيولوژيکي بالاتر، با ميانگين ۴٫۶ واحد تنش گرمايي بيشتر نسبت به گاوداري‌ها مواجه مي‌باشند. در دوره‌هاي آينده، به‌ويژه تحت سناريوي SSP5-8.5، فراواني تنش گرمايي در اکثر استان‌ها، حتي مناطق کوهستاني شمال غرب، افزايش چشمگيري خواهد داشت. براساس نتايج مرغداري‌ها نسبت به گاوداري‌ها حساسيت بيشتري نسبت به تغييرات اقليمي دارند، به‌طوري‌که در آينده دور، برخي استان‌ها مانند هرمزگان با تنش گرمايي بيش از 75 درصد مواجه خواهند شد. رتبه‌بندي استان‌ها با استفاده از روش TOPSIS نشان داد که خوزستان، بوشهر و سيستان و بلوچستان در گاوداري‌ها و اصفهان، يزد و خراسان رضوي در مرغداري‌ها در تمام دوره‌ها در گروه‌هاي ريسک بالا و بحراني قرار دارند.
نتايج: اين مطالعه نشان داد که تغييرات اقليمي، به‌ويژه تحت سناريوي SSP5-8.5، ريسک اقليمي را در صنعت دام و طيور ايران به‌طور قابل‌توجهي افزايش خواهد داد؛ بنابراين، کاهش توليد، افزايش تلفات و افت باروري در گاوداري‌ها و مرغداري‌ها دور از انتظار نمي‌باشد.. تمرکز بالاي واحدهاي توليدي در برخي استان‌ها مانند خوزستان و مازندران، همراه با تشديد تنش‌هاي گرمايي و خشکي تهديدي جدي براي امنيت غذايي و پايداري اقتصادي اين بخش‌ها مي‌باشد و ضرورت برنامه‌ريزي منطقه‌اي را در اين زمينه برجسته مي‌کند. پيشنهاد مي‌شود که راهکارهاي سازگاري مانند توسعه نژادهاي مقاوم، بهبود سيستم‌هاي خنک‌کننده و مديريت منابع آب در اولويت مديران و برنامه‌ريزان قرار بگيرد. اين يافته‌ها مي‌توانند به سياست‌گذاران در تدوين استراتژي‌هاي کاهش ريسک و تقويت تاب‌آوري صنعت دام و طيور کمک نمايد.
Background and Objectives: Climate changes, by intensifying heat stress and climatic aridity, pose significant challenges to the sustainability of the livestock and poultry industry in arid and semi-arid regions such as Iran. Dairy farms and poultry units, due to their dependence on water and feed resources and the high sensitivity of animals to elevated temperatures, are particularly vulnerable to these stresses. This study aims to assess the combined risk of heat stress and climatic aridity on dairy and poultry units across 31 provinces of Iran, utilizing the De Martonne Aridity Index (DMI) and the TOPSIS multi-criteria decision-making method, under CMIP6 climate scenarios, to provide valuable insights for policymaking and the development of adaptive strategies.
Methodology: The study utilized temperature and precipitation data from 31 synoptic stations for the baseline period (1997–2014) and downscaled outputs from the NEX-GDDP climate models (CNRM-CM6-1, CanESM5, GFDL-ESM4, HadGEM3-GC31-LL, MIROC6) under SSP2-4.5 and SSP5-8.5 scenarios for future periods (2025–2049, 2050–2074, 2075–2099). The De Martonne aridity index was calculated to assess climatic conditions, and the frequency of heat stress days was determined for dairy farms (temperatures above 26°C) and poultry farms (temperatures above 24°C). Provincial risk was ranked using the TOPSIS multi-criteria decision-making method, considering heat stress frequency, aridity index, and the number of production units. Model accuracy was evaluated using statistical metrics RMSE, MAE, MBE, and CC.
Findings: The results indicated that during the baseline period, southern provinces such as Khuzestan, Hormozgan, and Bushehr (with heat stress frequencies of 84.0%, 58.3%, and 51.9% in dairy farms, respectively) face high climatic risks, while northern provinces like Gilan and Ardabil experience the least stress. Poultry units, due to higher physiological sensitivity, face an average of 4.6 units higher heat stress compared to dairy farms. In future periods, particularly under the SSP5-8.5 scenario, the frequency of heat stress will significantly increase across most provinces, including northwestern mountainous regions. Poultry units exhibit greater sensitivity to climate change compared to dairy farms, with some provinces, such as Hormozgan, projected to experience heat stress exceeding 75% in the distant future. TOPSIS-based ranking revealed that Khuzestan, Bushehr, and Sistan and Baluchestan for dairy farms, and Isfahan, Yazd, and Khorasan Razavi for poultry units, consistently fall into high to critical risk categories across all periods.
Conclusions: This study demonstrates that climate change, particularly under the SSP5-8.5 scenario, significantly increases climatic risks in Iran’s livestock and poultry industry, leading to reduced production, increased mortality, and decreased fertility in dairy and poultry units. The high concentration of production units in provinces like Khuzestan and Mazandaran, coupled with intensified heat stress and aridity, poses a serious threat to food security and economic sustainability in these sectors, highlighting the need for region-specific planning. It is recommended that adaptation strategies, such as developing heat-resistant breeds, improving cooling systems, and managing water resources, be prioritized by managers and policymakers. These findings can assist policymakers in formulating strategies to mitigate risks and enhance the resilience of the livestock and poultry industry.
Ananta, K., & Shrestha, K. B. (2017). Effects of Climate Change on the Livestock Population in Mustang District, Nepal. Asian Journal of Agriculture and Development, 14(1), 37–49.
Ansari Amoli A, Aghighi H, & Lopez-Baeza E. (2022). Drought Risk Evaluation in Iran by Using Geospatial Technologies. Remote Sensing. 14(13(. https://doi.org/10.3390/rs14133096
Armstrong, D. V. (1994). Heat stress interaction with shade and cooling. Journal of Dairy Science, 77(7), 2044–2050.
Attia, Y. A., Aldhalmi, A. K., Youssef, I. M., Bovera, F., Tufarelli, V., Abd El-Hack, M. E., El-Kholy, K. H., & Shukry, M. (2024). Climate change and its effects on poultry industry and sustainability. Discover Sustainability, 5(397). https://doi.org/10.1007/s43621-024-00627-2
Azad, A., & Ahmadi, A. (2024). Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran. Scientific Reports. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-74789-4.
Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056
De Martonne, E. (1926). Aerisme, et Índices d’aridite. Comptes Rendus de L’Academy of Science, 182, 1395–1398.
Farzbood, P. (2021). Effects of managerial factors on performance and economic status of broiler chickens in Gonbad under heat stress conditions. Master's degree. Gorgan University.)In Persian)
Freeman, B. A., & Crapo, J. D. (1982). Biology of disease: Free radicals and tissue injury. Lab Invest, 47, 412–426.
Godde, C., Mason‐D’Croz, D., Mayberry, D., Thornton, P. K., & Herrero, M. (2021). Risk of climate‐related impacts on the livestock sector: A review of the evidence. Global Food Security, 28. https://doi.org/10.1016/j.gfs.2020.100488
Hajati, H., (2022). Heat stress in poultry houses and solutions to deal with it. Journal of Fodder and Animal Feed, 3(1), 64-74. (In Persian).
Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making. Springer Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48318-9
IPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. https://www.ipcc.ch/report/ar6/wg2/.
IPCC. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
Jurado, J., .Valonso, A., & Alenda, R. (1994). Selection response for growth in the Spanish merino flock. Journal of Animal Science, 72(6), 1433–1440. https://doi.org/10.2527/1994.7261433x
Kahraman, C., Cebeci, U., & Ruan, D. (2003). Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management, 16(6), 382–394.
Karimi, V., Karami, E., & Keshavarz, M. (2018). Climate change and agriculture: Impacts and adaptive responses in Iran. Journal of Integrative Agriculture, 17(1), 1–15. https://doi.org/10.1016/S2095-3119(17)61794-5
Lara, L., & Rostagno, M. (2013). Impact of Heat Stress on Poultry Production. Animals (Basel), 3(2), 356–369. https://doi.org/10.3390/ani3020356
Lin, H., Decuypere, E., & Buyse, J. (2006). Acute heat stress induces oxidative stress in broiler chickens. Comparative Biochemistry and Physiology Part A, 144(1), 11–17.
Lpelc, A. (2019). Climate Impacts on Animal Production. USDA National Institute of Food and Agriculture. https://lpelc.org/climate-impacts-on-animal-production/
Mirakbari, M., Mesbahzadeh, T., MohseniSaravi, M., Khosravi, H., & MortezaieFarizhendi, G. (2018). Performance of Series Model CMIP5 in Simulation and Projection of Climatic Variables of Rainfall, Temperature and Wind Speed (Case Study: Yazd). Physical Geography Research Quarterly, 50(3), 593–609. https://doi.org/10.22059/JPHGR.2018.248177.1007156
Mohajerpour, M., Alizadeh, A., Mousavi Baigy, S. M., Naserian, A. A. and Shakeri, M. T. (2018). Determination of the Most Appropriate Temprature-Humidity Index on Daily Milk Production in a Large Industrial Farm in Mashhad. Iranian Journal of Animal Science Research, 9(4), 507-516. doi: 10.22067/ijasr.v9i4.57379. (In Persian).
Mohan, S., & Bhaskaran, P. K. (2019). Evaluation of CMIP5 climate model projections for surface wind speed over the Indian Ocean region. Climate Dynamics, 53(9–10), 5415–5435. https://doi.org/10.1007/s00382-019-04874-2
Nawaz, A., Amoah, K., Leng, Q., Zheng, J., Zhang, W., & Zhang, L. (2021). Poultry Response to Heat Stress: Its Physiological, Metabolic, and Genetic Implications on Meat Production and Quality Including Strategies to Improve Broiler Production in a Warming World. Frontiers in Veterinary Science, 8. https://doi.org/10.3389/fvets.2021.699081
Ramezani Etedali, H., Koohi, S., & Partovi, Z. (2023). Evaluation of Ensemble Climate Model development methods based on CMIP5 to investigate the potential of water harvesting from air humidity. Iranian Journal of Soil and Water Research, 54(11), 1609–1625. https://doi.org/10.22059/ijswr.2023.364087.669553.(In Persian)
Reintjes, E. (2022). Food Systems and Livestock Production Under Climate Change: The IPCC’s Sixth Assessment. Sustainable Aquaculture Engagement: Phase 3 Progress Report. https://www.fairr.org/policy/issue-briefings/food-systems-and-livestock-production-under-climate-change
Rezaei, M., Nohtani, M., Moghaddamnia, A., Abkar, A., & Rezaei, M. (2013). Performance Evaluation of Statistical Downscaling Model (SDSM) in Forecasting Precipitation in two Arid and Hyper arid Regions. Journal of Water and Soil, 28(4), 836–845. https://doi.org/10.22067/JSW.V0I0.23119.(In Persian)
Seyedsharifi R, Ala Noshahr F, Seif Davati J, Hedayat Evrigh N. (2024). The Effect of Heat Stress on the Performance and Genetic Parameters of Milk Production, Reproduction, and Somatic Cell Scores in Iranian Holstein Cows. Research Animal Production, 15(3), 20-29. https://doi.org/10.61186/rap.15.3.20. (In Persian).
Song, J., Jiao, L. F., Xiao, K., Luan, Z. S., Hu, C. H., Shi, B., & Zhan, X. A. (2013). Cello-oligosaccharide ameliorates heat stress-induced impairment of intestinal microflora, morphology and barrier integrity in broilers. Animal Feed Science Technology, 185, 175–181.
Thornton, P., Nelson, G., Mayberry, D., & Herrero, M. (2021). Increases in extreme heat stress in domesticated livestock species during the twenty-first century. Global Change Biology, 27(22), 5762–5772.
Thornton, P., Nelson, G., Mayberry, D., & Herrero, M. (2022). Impacts of heat stress on global cattle production during the 21st century: a modelling study. Lancet Planetary Health, 6(3), 192–201.
West, J. (2003). Effects of heat-stress on production in dairy cattle. Journal of Dairy Science, 86(6), 2131–2144. https://doi.org/10.3168/jds.S0022-0302(03)73803-X
Wilhite, D. A., & Glantz, M. H. (1985). Understanding the drought phenomenon: The role of definitions. Water International, 10(3), 111–120.
Yahav, S. (2000). Acclimation to thermal stress in domestic fowl. Poultry Science, 79(8), 1197–1202.
