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 RowlandsonIowa State University

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Outline• Introduction• Background• Study Objectives• Use of radar rainfall estimates in disease

management schemes• Radar indication of daily rainfall occurrence• Conclusions and future work

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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

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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

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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

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Radar Basics

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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.

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Disease Index EstimationsMethodology

• 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.

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Disease Index EstimationsMethodology

• 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

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• 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 EstimationsMethodology

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Disease Index EstimationsMethodology

• 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)

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Disease Index EstimationsMethodology

• 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

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Radar Basics

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0 10 20 30 40 50 60 70

TBRG

KC

KD

EC

ED

Sensor

Duration (Hours)

SensorExeter Doppler (ED)Exeter Conventional (EC)King City Doppler (KD)King City Conventional (KC)TBRG

Disease Index EstimationsResults

June 9-11, Elora Time

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Disease Index Estimations - Results

June 9Duration (hours)

Avg. Temp.TomCast

DSV

MelCast

EFI

Sensor 19 17.3 2 4

Exeter conventional

21 17.5 3 6

Exeter Doppler

21 17.5 3 6

King City conventional

22 16.4 3 4

King City Doppler

14 14.3 1 1

TBRG 7 13.1 1 1

DSV and EFI calculations at Elora

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Disease Index Estimations – Results

June 11Duration (hours)

Avg. TempTomCast

DSV

MelCast

EFI

Sensor 10 8.1 0 0

Exeter conventional

5 17.7 1 1

10 8.1 0 0

Exeter Doppler5 17.7 1 1

10 8.1 0 0

King City conventional

13 9.9 0 0

King City Doppler

10 8.1 0 0

TBRG 10 8.1 0 0

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Disease Index Estimations - Results

June 10Duration (hours)

Avg. TempTomCast

DSV

MelCast

EFI

Sensor 11 17.3 2 4

Exeter conventional

24 17.5 3 6

Exeter Doppler 24 17.5 3 6

King City conventional

13 16.4 3 4

King City Doppler

12 14.3 1 1

TBRG3 14.8 0 0

12 10.5 0 0

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Disease Index EstimationsResults

Sensor TBRGExeter conv.

Exeter Dop.

King City conv.

King City Dop.

DSV EFI DSV EFI DSV EFI DSV EFI DSV EFI DSV EFI

Total 27 45 27 51 41 67 40 66 28 54 25 41

Cumulative Error 0 +6 +14 +22 +13 +21 +1 +9 -2 -4

Total DSV and EFI calculations for Elora

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Disease Index EstimationsResults

Sensor TBRG Exeter conv. Exeter Dop.King City

conv.

DSV EFI DSV EFI DSV EFI DSV EFI DSV EFI

Total 39 69 49 90 53 101 56 112 54 104

Cumulative Error +10 +21 +14 +32 +17 +43 +15 +35

Total DSV and EFI calculations for Ridgetown

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Disease Index EstimationsConclusions

• 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

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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

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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.

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Questions?