当前位置: X-MOL 学术Agric. For. Meteorol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Measuring and modelling of apple flower stigma temperature as a step towards improved fire blight prediction
Agricultural and Forest Meteorology ( IF 6.2 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.agrformet.2020.108171
Sébastien Rougerie-Durocher , Vincent Philion , David Szalatnay

Abstract In many areas, fire blight (Erwinia amylovora) is a sporadic but potentially devastating disease of apples. Infections occur primarily during bloom when warm weather conducive to bacteria multiplication on the stigma of contaminated flowers is followed by a wetting event, facilitating plant entry. Fire blight prediction models which rely on air temperature for disease forecast can help, but currently produce many false positive and some false negative prognoses. The differences between air and apple flower stigma temperature can explain some of the issues. The present study undertakes an introductory step in resolving this matter by being the first of its kind to document apple stigma temperatures and its departure from air temperature. Thermocouples continuously monitored flower temperature for the blooming seasons of 2018 and 2019 in the orchard of Saint-Bruno-de-Montarville, Quebec, Canada, while a thermal imager measured the temperature of randomly selected flowers in 2019. Flower stigma temperature measured with thermocouples followed the diurnal pattern of air temperature, but stigma temperature was higher/lower than air with maxima/minima at peak hours of the day/night. Temperatures measured with the thermal imager revealed a mean positive difference with the air temperature during the day (1.6 ± 1.3 °C). Stigma to air differences for both instruments had a strong positive relation with solar radiation during daytime. Under high humidity, this difference was significantly reduced. From these findings, regression models for estimating stigma temperature were developed for fire blight forecasting. When validating with thermal imaging data, the best model utilizes air temperature, radiation and relative humidity to estimate stigma temperature with better results (RMSE = 1.04 °C) than air temperature alone (RMSE = 2.05 °C). Although the application of these findings for fire blight prediction models was not tested, there is evidence that models that solely rely on air temperature are at risk of errors.

中文翻译:

苹果花柱头温度的测量和建模作为改进火疫病预测的一步

摘要 在许多地区,火疫病(Erwinia amylovora)是一种散发性但具有潜在破坏性的苹果病害。感染主要发生在开花期间,当时温暖的天气有利于细菌在受污染的花柱上繁殖,随后发生润湿事件,促进植物进入。依靠气温进行疾病预测的火疫病预测模型可以提供帮助,但目前会产生许多假阳性和一些假阴性预测。空气和苹果花柱头温度之间的差异可以解释一些问题。本研究首次记录了苹果柱头温度及其与气温的偏离,从而为解决这一问题迈出了第一步。在加拿大魁北克省 Saint-Bruno-de-Montarville 果园,热电偶持续监测 2018 年和 2019 年开花季节的花朵温度,而热成像仪则测量了 2019 年随机选择的花朵的温度。随后用热电偶测量了花朵柱头温度气温的昼夜规律,但柱头温度高于/低于空气,在白天/晚上的高峰时段有最大值/最小值。使用热像仪测量的温度显示与白天的气温 (1.6 ± 1.3 °C) 之间存在平均正差。两种仪器的柱头与空气差异与白天的太阳辐射有很强的正相关关系。在高湿度下,这种差异显着减小。从这些发现中,用于估计柱头温度的回归模型是为火疫病预测开发的。在使用热成像数据进行验证时,最佳模型利用气温、辐射和相对湿度来估计柱头温度,其结果 (RMSE = 1.04 °C) 比单独的气温 (RMSE = 2.05 °C) 更好。尽管这些发现在火疫病预测模型中的应用未经测试,但有证据表明,仅依赖气温的模型存在出错风险。
更新日期:2020-12-01
down
wechat
bug