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Int J Biometeorol. 2016 Nov;60(11):1761-1774. Epub 2016 May 14.

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

Author information

1
Department of Agricultural and Biological Engineering, University of Florida, P.O. Box 110570, Gainesville, FL, USA. vmontone@ufl.edu.
2
Department of Agricultural and Biological Engineering, University of Florida, P.O. Box 110570, Gainesville, FL, USA. cfraisse@ufl.edu.
3
Gulf Coast Research and Research Center, University of Florida, Wimauma, FL, 33598, USA.
4
Department of Biosystems Engineering, University of São Paulo, P.O. Box 9, Piracicaba, Sao Paulo, Brazil.
5
Department of Plant Pathology and Microbiology, Iowa State University, 313 Bessey, Ames, IA, USA.
6
Department of Plant Pathology, Ohio State University, 224 Selby Hall, Columbus, OH, USA.
7
Department of Agricultural and Environmental Sciences, Clemson University, 105 Collings St./220BRC, Clemson, SC, USA.

Abstract

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.

KEYWORDS:

Anthracnose; Botrytis; Botrytis cinerea; Colletotrichum acutatum; Dew; Disease control; LWD sensors; Rational spray; Strawberry diseases

PMID:
27180263
DOI:
10.1007/s00484-016-1165-4
[Indexed for MEDLINE]

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