MWWG Presentation Use of Precipitation Duration Data from Weather Radar in Leaf Wetness Duration Estimates for Plant Disease Management MSc. Thesis, University of Guelph Tracy Rowlandson Iowa State University

MWWG Presentation Outline Introduction Background Study Objectives Use of radar rainfall estimates in disease management schemes Radar indication of daily rainfall occurrence Conclusions and future work

MWWG Presentation Introduction Decisions regarding fungicide spray timings are often based on leaf wetness duration (LWD) and temperature during that period < 1 mm of rain will remain on a leaf Occurrence is more important than quantity Rain events are spatially diverse so data captured by a rain gauge is site-specific

MWWG Presentation Background Common disease management schemes include TomCast and MelCast Require input of LWD and average temperature in order to estimate disease index values Empirical and physical models have been developed to predict LWD Physical models based on the concept of energy budgets

MWWG Presentation Leaf Wetness Sensors Electronic sensors look at changes in electrical resistance Printed circuit board of gold-plated copper contacts Sensors are coated with paint and dried at high temperatures

MWWG Presentation Radar Basics

MWWG Presentation Study Objectives Adapt the Penman-Monteith model to: Simulate the wetness duration on a tipped leaf surface Determine the length of the drying period following the end of a rainfall event Determine if radar is a valuable substitute or complement to tipping bucket rain gauge networks to estimate duration of rainfall events in disease management.

MWWG Presentation Disease Index Estimations Methodology RH, temperature, windspeed, solar radiation, and longwave radiation (at Elora) were measured and averaged over hourly periods Solar radiation was adjusted to represent the amount that was received on the tipped surface of the leaf wetness sensor P-M model was used to indicate the amount of time required for the sensor to dry after a rain event.

MWWG Presentation Disease Index Estimations Methodology Radar Duration = combination of rainfall duration indicated by the radar, and the drying time indicated by the P-M model TBRG Duration = combination of rainfall duration indicated by the TBRG, and the drying time indicated by the P-M model

MWWG Presentation When rain was indicated by the radar, the rain event was given a 0.6mm value in the rain reservoir the hour following the indicated rain The duration of the wetness period was determined Mean temperature during the wetness period was calculated If a break in the wetness period was 2 hours or greater, the wetness event was separated into two events Disease Index Estimations Methodology

MWWG Presentation Disease Index Estimations Methodology If the wetness event was longer than 24 hours, a break in the wetness event was enforced Disease severity values (DSV’s) and Environmental Favorability Indices (EFI’s) were calculated on a daily basis beginning at 1100 and ending 1100 the following day If a wetness event extends beyond 1100, the user can extend the period to 1400 (for a maximum wetness duration of 27 hours)

MWWG Presentation Disease Index Estimations Methodology DSV/EFI accumulations were made using both conventional and Doppler radars At Elora, estimations were made using all 4 radars At Ridgetown, range limitations of the King City Doppler radar prevented it from being used DSV’s/EFI’s were also calculated using rainfall from TBRG at Elora and Ridgetown Leaf wetness sensor was considered to be the best indicator of LWD and was used as the basis for comparison

MWWG Presentation Radar Basics

MWWG Presentation Disease Index Estimations Results June 9-11, Elora Time

MWWG Presentation Disease Index Estimations - Results June 9 Duration (hours) Avg. Temp. TomCast DSV MelCast EFI Sensor Exeter conventional Exeter Doppler King City conventional King City Doppler TBRG DSV and EFI calculations at Elora

MWWG Presentation Disease Index Estimations – Results June 11 Duration (hours) Avg. Temp TomCast DSV MelCast EFI Sensor Exeter conventional Exeter Doppler King City conventional King City Doppler TBRG

MWWG Presentation Disease Index Estimations - Results June 10 Duration (hours) Avg. Temp TomCast DSV MelCast EFI Sensor Exeter conventional Exeter Doppler King City conventional King City Doppler TBRG

MWWG Presentation Disease Index Estimations Results SensorTBRG Exeter conv. Exeter Dop. King City conv. King City Dop. DSVEFIDSVEFIDSVEFIDSVEFIDSVEFIDSVEFI Total Cumulative Error Total DSV and EFI calculations for Elora

MWWG Presentation Disease Index Estimations Results SensorTBRGExeter conv.Exeter Dop. King City conv. DSVEFIDSVEFIDSVEFIDSVEFIDSVEFI Total Cumulative Error Total DSV and EFI calculations for Ridgetown

MWWG Presentation Disease Index Estimations Conclusions Errors due to excessively long wet periods Radar indicates rainfall for too long Overestimation of the length of drying time when radar indicates sporadic rainfall Rating of the radars At Elora, the King City radars were the most capable of mimicking the sensor At Ridgetown, the Exteter conventional and King City conventional radars faired the best

MWWG Presentation Study Conclusions Study objective: Determine if radar is a valuable substitute or complement to tipping bucket rain gauge networks in plant disease management schemes Radar rainfall estimates should be used as a complement The allowable window for error in MelCast is too small for the use of radar rainfall estimates TBRG and P-M model mimicked sensor quite well Use of radar for as a check for interpolation should be done with caution

MWWG Presentation Future Research Advancements User could establish an automated system that tests various threshold values for current weather conditions or daily estimate. Radar rainfall estimates could be used to determine if a TBRG is installed correctly, need calibration or maintenance or has been tampered with. Further investigation into the use of radar rainfall estimates in the timing of fungicides sprays.

MWWG Presentation Questions?