Montone Verona O, Fraisse Clyde W, Peres Natalia A, Sentelhas Paulo C, Gleason Mark, Ellis Michael, Schnabel Guido
Department of Agricultural and Biological Engineering, University of Florida, P.O. Box 110570, Gainesville, FL, USA.
Gulf Coast Research and Research Center, University of Florida, Wimauma, FL, 33598, USA.
Int J Biometeorol. 2016 Nov;60(11):1761-1774. doi: 10.1007/s00484-016-1165-4. Epub 2016 May 14.
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 ≥90 %), 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.
叶片湿润持续时间(LWD)在病害发展中起着关键作用,并且经常被用作病害预警系统的一个输入参数。由于很少有传感器能够进行测量且测量结果也不可靠,因此LWD通常使用数学模型来估算。美国佛罗里达州的种植者使用一种名为“草莓咨询系统”(SAS)的草莓病害预警系统来决定何时对草莓田进行喷洒,以控制炭疽病和灰霉病果实腐烂。目前,SAS在六个部署了可靠LWD传感器的地点实施。一个强大的LWD模型将有助于SAS从佛罗里达州扩展到其他没有可靠LW传感器的地区。本研究的目的是评估使用数学模型来估算LWD以及与现场LWD测量相比的喷雾建议时间。具体目标是:(i)比较模型估算的和观测到的LWD以及杀菌剂喷雾建议在时间和次数上的差异;(ii)评估气象站传感器精度对LWD模型性能的影响;(iii)比较美国四个州的LWD模型性能。所评估的LWD模型包括分类与回归树(CART)、露点降(DPD)、相对湿度等于或大于90%的小时数(NHRH≥90%)以及彭曼-蒙特斯(P-M)模型。预计P-M模型的误差最低,因为它是基于物理原理的,因此具有可移植性。实际上,P-M模型在配备高精度传感器的气象站估算LWD最为准确(平均绝对误差<2小时),但当使用相对湿度和估算净辐射(基于太阳辐射和温度)的精度较低的传感器时,其准确性最低(平均绝对误差=3.7小时)。CART模型在估算LWD以及就炭疽病和灰霉病果实腐烂防治的杀菌剂喷雾时间向种植者提供建议方面最为稳健,因此我们建议使用该模型将草莓病害预警扩展到佛罗里达州以外的其他地区,这些地区可能部署精度较低的传感器且无法获得净辐射观测数据。