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Rethinking the Role of Leaf Wetness Duration in Plant Disease Management Tracy Rowlandson University of Guelph
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Outline Importance of leaf wetness Evolution of leaf wetness sensors So what’s wrong with them? Other options Where to go next: a suggestion
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Bregaglio et al. (2011).
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Importance of Leaf Wetness Knowledge of leaf wetness duration (LWD) is the basis for any disease management programs Sources of leaf wetness: – Intercepted rainfall – Intercepted irrigation – Dew formation: 2 sources of water vapour – Guttation
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Evolution of Leaf Wetness Sensors Static (e.g. Duvdevani Dew Gauge) – Wooden block placed outdoors at sunset – Dew formation examined in the morning and compared to series of photographs – Very poor correlation with LWD
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Evolution of Leaf Wetness Sensors Static (e.g. Duvdevani Dew Gauge) – Wooden block placed outdoors at sunset – Dew formation examined in the morning and compared to series of photographs – Very poor correlation with LWD Mechanical (e.g. DeWit leaf wetness recorder) – Measures changes in the length of string, human hair, or membrane or change in weight of sensor – Typically recorded LWD on a rotating chart
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Evolution of Leaf Wetness Sensors Electronic (e.g. resistance sensors) – Interlaced, gold-plated copper wires – When water bridges the wires, the circuit is complete and resistance drops – Can measure LWD from rainfall, irrigation, and dew formation
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So What’s Wrong With Them? Is the problem with the sensors? No, the problem is how we use them – What does the sensor represent? – Treatment of sensors – Placement of sensors – Calibration of sensors
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So What’s Wrong With Them? There are so many different sensors – Commercially available and those made in-house – Many are sold unpainted – Painting techniques differ between users
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DecagonEnvirondataSpectrumNetsens OnSet Campbell Sci. RainwiseGlobal Water
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Non-paintedPainted Sensor Response (KOhm) Adapted from Lau et al. (2000)
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So What’s Wrong With Them? Sensor placement – Position in canopy can have a large impact on the measured LWD
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Dew amount and duration varies vertically within some crop canopies Jacobs and Nieveen, 1995 Maize Barley
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Leaf Wetness Duration Hours Batzer et al., 2008 Leaf wetness duration in an apple canopy
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So What’s Wrong With Them? Calibration is rarely done – Important especially for multi-seasonal use – Need to determine wetness threshold – Do multiple sensors agree? Maintenance is critical – Birds love them – Debris on surface will change wetness threshold
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Other Options Models: physical, empirical, or combination Physical models tend to be more complex and require more inputs Physical models valid everywhere – Models are either one source (model dewfall only) or two source (model both dewfall and distillation) – Consider the canopy to be a ‘big leaf’ or separate the canopy into layers
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Other Options Models: physical, empirical, or combination Physical models tend to be more complex and require more inputs Physical models valid everywhere Empirical models require fewer inputs Valid only for the region for which they were developed e.g. RH ≥ 90%
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Ames, IA 83%; Elora, ON 85%; Florence, Italy 92%; Piracicaba, Brazil 90% Sentelhas et al. (2008)
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TomCast Model Leaf Wetness Duration (h) required to produce DSVs of Mean T (°C) 01234 13-170-67-1516-2021+- 18-200-34-89-1516-2223+ 21-250-23-56-1213-2021+ 26-290-34-89-1516-2223+
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Other Options How well does RH do in a disease model? – Data from LWD sensor from 2010 compared to RH – RH threshold of 83% found to compare the best For TomCast model LWD sensor indicated 3 sprays (mid-July to end of August); RH indicated 3 sprays RH model indicated spray timing one day later than sensor
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Suggestion Take advantage of the standard measurements of RH already available Develop networks based on currently existing stations (i.e. fill in where needed) Standards are available for installation of RH sensors Calibration conducted by manufacturer There will still be obstacles to overcome
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Weather Stations in North America
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