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Influence of climate factors on Phakopsora euvitis Y. Ono airborne dispersal in grapevine crop

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DataCite Commons2021-03-25 更新2024-07-28 收录
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https://scielo.figshare.com/articles/dataset/Influence_of_climate_factors_on_Phakopsora_euvitis_Y_Ono_airborne_dispersal_in_grapevine_crop/14279245/1
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ABSTRACT Grape rust, one of the most important diseases of Vitis spp., is managed in Brazil according to a calendar-based fungicide spray program. Despite the use of fungicides, the crop and the quality of grapes have shown significant losses due to rust. This disease causes leaf fall, unequal size and color of berries and loss in plant vigor in the following harvest. The aim of this study was to investigate the relationship between airborne urediniospore dispersal and weather parameters to improve the management of this disease. Thus, vineyards of the cultivar ‘Syrah’, susceptible to grape rust, were monitored to determine Phakopsora euvitis airborne dispersal dynamic. Wind vane spore traps were maintained at 0.5 m above the grapevine canopy and samples were collected every 24 h, while slides were changed at 9:00 a.m. A logistic regression model was developed to estimate the urediniospore dispersal probability. Higher urediniospore concentrations were observed between April and August, while in the remaining months the concentration was low or zero. The regression model of the airborne urediniospore concentration relative to the weather parameters was described as logit (Y) = 15.6668 - 0.6333 (temperature) + 0.4291 (wind speed), and a decision threshold of 0.47 was determined for predictive purposes.

摘要 葡萄锈病是葡萄属(Vitis)植物最重要的病害之一,巴西当前采用基于日历的杀菌剂喷施方案对其进行防控。尽管已使用杀菌剂,葡萄产量与品质仍因该病害出现显著损失。该病害可引发落叶、浆果大小与色泽不均,并导致下一季收获时植株活力下降。本研究旨在探究空中夏孢子(urediniospore)的传播与气象参数之间的关联,以优化该病害的防控策略。为此,研究人员对易感葡萄锈病的‘西拉’(Syrah)葡萄品种葡萄园开展监测,以明确葡萄锈病菌(Phakopsora euvitis)的空中夏孢子传播动态。研究中将风向标式孢子捕捉器安装于距葡萄冠层0.5米高处,每24小时采集一次样本,且每日上午9:00更换载玻片。本研究构建了逻辑回归模型以估算夏孢子传播概率。监测结果显示,4月至8月间夏孢子浓度较高,其余月份浓度较低或为零。针对空中夏孢子浓度与气象参数的回归模型表达式为:logit(Y) = 15.6668 - 0.6333×(温度) + 0.4291×(风速),并确定了用于预测的决策阈值0.47。
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SciELO journals
创建时间:
2021-03-24
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