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Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states

机译:在美国四个州的草莓病预警系统中实际使用的叶片湿度持续时间模型的评估

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摘要

Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH aeyen90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE < 2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.
机译:叶片潮湿持续时间(LWD)在疾病发展中起着关键作用,经常被用作疾病预警系统的输入。 LWD通常使用数学模型进行估算,因为通过传感器进行的测量很少可用和/或可靠。美国佛罗里达的种植者使用一种称为“草莓咨询系统”(SAS)的草莓疾病预警系统来决定何时喷洒草莓田以控制炭疽病和葡萄孢菌的腐烂。当前,SAS在六个位置部署了可靠的随钻测井传感器。强大的LWD模型将有助于SAS从佛罗里达扩展到其他没有可靠LW传感器的地区。这项研究的目的是评估与现场LWD测量相比,使用数学模型来估计LWD和喷雾建议时间。具体目标是(i)比较模型估计和观察到的LWD以及推荐使用的杀菌剂的时间和数量上的差异,(ii)评估气象站传感器精度对LWD模型性能的影响,以及(iii)比较整个LWD模型性能美国的四个州。评估的LWD模型是分类和回归树(CART),降露点(DPD),相对湿度等于或大于90%(NHRH aeyen90%)的小时数和Penman-Monteith(P-M)。由于P-M模型是基于物理的模型,因此具有可移植性,因此预计误差最低。实际上,在具有高精度传感器的气象站中,PM模型最准确地估计了随钻测距(MAE <2小时),但是当使用相对湿度和估计净辐射(基于太阳辐射和温度)的较低精度传感器时,PM模型的准确性最低( MAE = 3.7 h)。 CART模型是最有效的LWD估算方法,可为炭疽病和葡萄孢菌腐烂控制的农药喷洒时间提供建议,因此,我们建议使用该模型将草莓病预警范围扩大到佛罗里达州以外,并扩展到可能有气象站的其他地方部署了精度较低的传感器,并且没有净辐射观测值。

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