Predicting dryland winter wheat yield in cold regions of Iran

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Fatemeh Razzaghi, Razieh Ghahramani, Ali Reza Sepaskhah, Shahrokh Zand-Parsa
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引用次数: 0

Abstract

Wheat is a crucial staple worldwide, serving both human and animal needs. In Iran, where climate conditions vary widely, wheat farming faces significant challenges, especially in areas facing freezing winters and unfavorable temperatures during reproductive stages. Unfortunately, existing models often fail to account these extreme and specific climate conditions, leading to inaccurate predictions, notably in cold areas. To address this issue, wheat dryland farming (WDF) model was evaluated in predicting dryland winter wheat yields in five distinct areas including Shahrekord, Borujen, Koohrang, Farsan, Lordegan, and Ardal in Chahar-Mahal and Bakhtiari province, Iran. The results showed that changes in precipitation and temperature significantly impacted dryland wheat production. While higher precipitation generally associates with higher yields, this relationship is not always straightforward due to factors like unfavorable precipitation patterns and types (i.e., rainfall or snow). Likewise, unfavorable temperatures, particularly during crucial growth stages and winter freezes, pose significant challenges to wheat growth and yield modeling. The WDF model's performance was evaluated across various temperature conditions in the study area, and it was more accurate in regions with certain minimum and maximum temperature values above thresholds. However, the model performance was poor in colder areas, where freezing temperatures were occurred in winter duration (Shahrekord, Borujen, Koohrang, and Farsan). In order to improve the model's accuracy, a correction factor based on the minimum and maximum air temperatures was incorporated in the model. The findings emphasized the importance of considering both precipitation and temperature dynamics when modeling winter wheat yields, especially in regions with diverse climates. By refining models like WDF, agricultural planners can better forecast the yield fluctuations and address the impacts of climate variability on food security in Iran and similar regions worldwide.

Abstract Image

伊朗寒冷地区旱地冬小麦产量预测
小麦是全世界重要的主食,满足人类和动物的需求。伊朗的气候条件差异很大,小麦种植面临着巨大挑战,尤其是在冬季严寒和生育期气温不利的地区。遗憾的是,现有模型往往没有考虑到这些极端和特殊的气候条件,导致预测不准确,尤其是在寒冷地区。为解决这一问题,对伊朗 Chahar-Mahal 和 Bakhtiari 省 Shahrekord、Borujen、Koohrang、Farsan、Lordegan 和 Ardal 等五个不同地区的小麦旱地耕作(WDF)模型进行了评估,以预测旱地冬小麦产量。研究结果表明,降水量和温度的变化对旱地小麦的产量影响很大。虽然降水量增加通常与产量增加有关,但由于不利的降水模式和类型(即降雨或降雪)等因素,这种关系并不总是很直接。同样,不利的温度,尤其是关键生长阶段和冬季冰冻期的不利温度,也对小麦生长和产量建模提出了巨大挑战。对 WDF 模型在研究地区各种温度条件下的性能进行了评估,在某些最低和最高温度值高于阈值的地区,该模型的准确性较高。然而,该模型在寒冷地区的性能较差,这些地区在冬季会出现冰冻温度(Shahrekord、Borujen、Koohrang 和 Farsan)。为了提高模型的准确性,模型中加入了基于最低和最高气温的校正因子。研究结果强调了在建立冬小麦产量模型时同时考虑降水和温度动态的重要性,尤其是在气候多样的地区。通过完善 WDF 等模型,农业规划人员可以更好地预测产量波动,并应对气候多变性对伊朗和全球类似地区粮食安全的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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