ارزیابی ریسک خشکسالی بر ذرت
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ارزیابی ریسک خشکسالی بر ذرت

عنوان فارسی مقاله: ارزیابی ریسک خشکسالی بر ذرت بهاره بر اساس مدل چینش APSIM در استان لیائونینگ ، چین
عنوان انگلیسی مقاله: Drought risk assessment of spring maize based on APSIM crop model in Liaoning province, China
مجله/کنفرانس: مجله بین المللی کاهش خطر بلایا – International Journal of Disaster Risk Reduction
رشته های تحصیلی مرتبط: کشاورزی
گرایش های تحصیلی مرتبط: گیاه پزشکی، اکولوژی گیاهان زراعتی
کلمات کلیدی فارسی: ارزیابی ریسک خشکسالی، مدل چینش APSIM، کمبود آب گیاه، از دست دادن محصول به دلیل خشکسالی
کلمات کلیدی انگلیسی: Drought risk assessment; APSIM crop model; Crop water deficit; Yield loss due to drought
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ijdrr.2020.101483
دانشگاه: China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 3.054 در سال 2019
شاخص H_index: 28 در سال 2020
شاخص SJR: 1.346 در سال 2019
شناسه ISSN: ۲۲۱۲-۴۲۰۹
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 20
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14633
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


۱٫ Introduction


۲٫ Materials and methods


۳٫ Results


۴٫ Discussion


۵٫ Conclusion


Declaration of competing interest


Acknowledgements


References

نمونه متن انگلیسی مقاله

Abstract


Drought risk assessment is a vital part of drought risk management, which plays an important role in drought mitigation. Due to its complexity, drought risk is difficult to define and challenging to quantitatively assess, as the drought impacts associate with many social sectors. This contribution method the issue by quantitatively evaluating the yield loss due to drought as a function of the drought severity indicator in Liaoning province, China for spring maize using logarithmic regression. As crop water deficit is essence to identify agricultural drought, it developed a drought severity indicator using the crop water stress coefficient and duration. The Agricultural Production Systems sIMulator (APSIM) crop model was employed to simulate the spring maize growth to obtain daily water deficit during the growth period (May to September) and yield. The relationship between drought severity frequency and yield loss rate due to drought was established to assess the drought risk of spring maize when drought severity frequency is equal to 20%, 10%, 5% and 2%. The results show that Chaoyang and Fuxin have the highest drought risk in four levels of drought severity frequency whilst the lowest drought risk was identified in Tieling. The central Liaoning province has a moderate drought risk. For a specific drought severity frequency, drought risk increases from east to west in Liaoning province whilst it varies in each city at different drought severities. This method can predict yield loss due to drought for drought early warning. Drought risk maps presents spatial characteristics that can help to agricultural drought mitigation and the development of drought preparedness plan in Liaoning province.


Introduction


Drought is slow-onset and one of the most widespread natural hazards. Drought impacts are nonstructural and the occurrence of drought is associated with significant impacts in water resources, environment, energy, and human lives, especially in agricultural production (Wilhite, 2005;Wilhite and Pulwarty, 2018). These characteristics make it particularly challenging to quantify drought risk and capture drought impacts. In China, the average annual yield loss due to drought has increased from 4.35 million tons in the 1950s, to 34.9 million tons in the early twenty-first century (Lv, 2013). Drought affected approximately 60% of the maize sown area during 1990-2007, which resulted in a 20%-30% reduction in production (Jia et al., 2012). The widespread and costly nature of drought has naturally led to an interest in drought risk assessment. Methods to quantify drought risk help decision makers in drought risk management and drought mitigation. It also has a great significance in the theory and practice of quantitative drought risk assessment (Bachmair et al., 2017;Botterill and Hayes, 2012). To date, a number of previous studies have evaluated the drought risk of different regions and climates across the world at different spatial scales, most of which focus on agricultural drought risk (Xie et al., 2016). Agriculture is directly affected by the occurrence of drought as it reliable on precipitation, temperature and evapotranspiration, which can decrease the soil moisture (Sruthi and Aslam, 2015).

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