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تأثیر ناهنجاری‌های اقلیمی بر عملکرد زعفران مطالعه موردی: کاشمر و تربت حیدریه

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 1. دانشجوی دکتری، آب و هواشناسی، دانشکده جغرافیا، واحد نور، دانشگاه آزاد اسلامی، نور، ایران

2 2. دانشکده جغرافیا، واحد نور، دانشگاه آزاد اسلامی، نور، ایران

3 دانشکده جغرافیا، واحد نور، دانشگاه آزاد اسلامی، نور، ایران

4 استادیار، بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج

5 استادیار، پژوهشگاه هواشناسی و علوم جو، پژوهشکده اقلیم شناسی و تغییر اقلیم، مشهد، ایران

چکیده
زعفران به عنوان یک محصول استراتژیک در اقتصاد ایران، به شدت تحت تأثیر تغییر اقلیم قرار دارد. تحلیل‌ها حاکی از آن است رویدادهای فرین همچون یخبندان طی دوره گلدهی و خشکسالی‌های ممتد از عوامل کلیدی کاهش عملکرد این محصول هستند. هدف از این پژوهش، تحلیل روند نمایه های فرین دما و بارش و مدل سازی تأثیر این نمایه‌ها بر عملکرد زعفران با استفاده از رگرسیون چندمتغیره می باشد. برای این منظور، از داده‌‌های دمای کمینه، دمای بیشینه و بارش روزانه ۳۰ ساله (۱۹۹۰-۲۰۲۰) دو ایستگاه همدید تربت حیدریه و کاشمر برای محاسبه نمایه های فرین دما و بارش تیم تخصصی تشخیص و نمایه‌های تغییر اقلیم (ETCCDI) در نرم افزار Rclimdex استفاده شد. داده های عملکرد محصول زعفران برای این دو منطقه از سازمان جهاد کشاورزی اخذ شد. نتایج نشان داد که بارش سالانه به طور قابل‌توجهی کاهش یافته است. تحلیل روند نمایه‌های فرین دمایی طی دوره آماری نشان داد نمایه های دمایی TNm, TMm, TXm و روزهای گرم (WSDI) و نمایه‌های بارش بسیار سنگین (R99p) در هر دو ایستگاه و بارش سنگین (R95p) در تربت حیدریه کاهش معناداری (p<0.05) داشته‌اند. مدلسازی رگرسیونی نشان داد برای منطقه کاشمر، مدل با ۴ نمایه فرین دمایی و بارشی تدوین شد (R²=0.70، RMSE=0.49، NRMSE=16.4%). متغیرهای مستقل تأثیرگذار بر عملکرد زعفران شامل گرمایش شبانه (TNm)، روزهای یخی (با ضریب ۱۰/۰- قوی‌ترین عامل کاهش‌دهنده عملکرد)، شب های حاره ای و روزهای خشک متوالی (CDD) بودند که نمایه های فرین مذکور دارای تأثیر منفی بر عملکرد زعفران در این منطقه بودند. برای منطقه تربت حیدریه مدل رگرسیونی با مقادیر آماره هایR²=0.83، RMSE=0.43، NRMSE=15.9% توسعه یافت. متغیرهای مستقل مدل شامل کمینه دمای بیشینه (TXn)، R95p، و FD (با تاثیر مثبت) و نمایه روزهای یخی با ضریب ۱۲/۰- به عنوان تنها نمایه کاهش دهنده ی عملکرد زعفران بود. بر اساس نتایج بدست آمده فرین های دمایی تأثیر غالب بر عملکرد زعفران در این مناطق داشته اند. این یافته‌ها می‌توانند به عنوان پایه‌ای برای برنامه‌ریزی‌های اقلیم‌محور کشت زعفران در مناطق مشابه مورد استفاده قرار گیرند.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

The Impact of Climatic Anomalies on Saffron Yield Case Study: Kashmar and Torbat-e Heydariyeh

نویسندگان English

Mahdiyeh Zynal Zadeh, 1
Gholamreza Janbaz Ghobadi 2
Sadroddin Motevalli, 3
Majid Taherian 4
Mansoureh Kouhi 5
1 1- Ph.D. Candidate, Department of Geography, No.C Branch, Islamic Azad University, Noor, Iran
2 Department of Geography, No.C Branch, Islamic Azad University, Noor, Iran
3 Department of Geography, No.C Branch, Islamic Azad University, Noor, Iran
4 Assistant Pro. Horticulture Crop Research Department, Khorasan Razavi Agricultural and Natural Resources Resaerch and Education Center, AREEO, Mashhad, Iran.
5 Assistant Prof., Member of the Risks and Climate Change Research Group, Climatology and Climate Change Research Institute
چکیده English

As a strategic crop in Iran's economy, saffron is highly vulnerable to climate change. Analyses indicate that extreme events such as frost during flowering periods and prolonged droughts are key factors in reducing saffron yield. This study aims to analyze trends in extreme temperature and precipitation indices and model their impact on saffron yield using multivariate regression. Data on daily low and high temperatures, as well as rainfall from 1990 to 2020, were collected from weather stations in Torbat-e Heydariyeh and Kashmar to calculate extreme climate measures using the ETCCDI method, which was applied using RClimDex software. Saffron yield data was obtained from the Agricultural Jihad Organization. Trend analysis indicated that annual precipitation has had a significant decline. Extreme temperature indices (TNm, TMm, TXm, WSDI) and heavy precipitation indices (R99p at both stations and R95p in Torbat-e Heydariyeh) exhibited significant decreasing trends (p < 0.05). For Kashmar, the multivariate regression model incorporated four extreme indices (R² = 0.70, RMSE = 0.49, NRMSE = 16.4%). Key predictors included ID (β = -0.14, the strongest yield-reducing factor), extreme low temperatures or TNn (β = +0.10), tropical nights (TR20), and consecutive dry days (CDD) negatively impacted yield. For Torbat-e Heydariyeh, the model demonstrated higher accuracy (R² = 0.83, RMSE = 0.43, NRMSE = 15.9%). Significant predictors were TXn (β = +0.18) and frost days or ID (β = -0.12) as the most positive and negative drivers. Overall, temperature variables—particularly nighttime temperatures—dominated yield variability. These findings provide a foundation for climate-smart saffron cultivation planning in similar semi-arid regions. Mitigation strategies should prioritize thermal regulation (e.g., altitude selection, mulching) and water management to offset warming-induced stress.

کلیدواژه‌ها English

Extreme Temperature and Precipitation Indices
Multivariate Regression
Kashmar
Torbat-e Heydariyeh
Saffron
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