Cornell University NYSAESGeneva Use of pan evaporation and temperature data in Powdery Mildew forecasting Principles of disease forecasting Diseases Amenable to Meteopathological Prediction Bourke 1970 ID: 342486
Download Presentation The PPT/PDF document "Michelle M. Moyer" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Michelle M. MoyerCornell UniversityNYSAES-Geneva
Use of pan evaporation and temperature data in Powdery Mildew forecastingSlide2
Principles of disease forecastingDiseases Amenable to Meteopathological Prediction (Bourke 1970)Causes economically significant damageVariable seasonal impact (directly/indirectly weather related)
Control measures are availableTo adjust spray timesTo reduce total number of spraysTo optimize timing of spraysProtection, eradicationMaximization of chemistries; resistance managementSlide3
Forecasting the Powdery MildewsGenerally a focus on infection periods and general favorability for severity developmentTemperature (Grape, Apple, Beet, Wheat)Rainfall (Grape, Apple, Beet, Wheat)Humidity (Grape, Apple, Wheat)Vapor Pressure Deficit (Apple)
Leaf Wetness (Grape)Wind (Wheat)Slide4
Forecasting Grape Powdery MildewDevelopment of a risk assessment model for grapevine powdery mildew in the NE.Ran existing models for the regionDevelopment of a new model based on a variety of weather inputs. In NY, powdery mildew severity on clusters is related to temperature the previous fall, and
In-season pan evaporation levelsSlide5
In Season Weather: How “wet” or “dry” has the weather been (based on pan evaporation). What is the upcoming forecast?
In-season weather is conducive. What was the inoculum potential?In-season weather not conducive.100% Chance of
“Mild Year”
High inoculum potential with average to highly conducive weather. Potential realized.
100% Chance of
“Severe Year”
Wet to Average (<6.07 mm)
Dry (>=6.07 mm)
Moderate to low inoculum load.
How conducive is the weather to maximize the impact this load?
Warm previous fall
(>=450 DD)
Cool previous fall
(<450 DD)
Conducive (<5.45 mm)
Average (>=5.45 mm)
Low inoculum load, but weather is extremely favorable to maximize potential.
60% Chance of “Severe Year”
Low inoculum load with just average weather conditions means a poor start for PM.
80% Chance of
“Mild Year”
Estimating Powdery Mildew RiskSlide6
Advantages of using EpanIntegration of multiple weather parametersSingle measurement
Global pan evaporation networks availableConceptually easy to explain to growers“Water Stress”“Clothesline” ExampleSlide7
Epan EtoEvaporation from an open surface
Measured parameterEvaporation from open and vegetative surfacesCalculated parameterEpan
vs. EtoSlide8
Requirements in using Eto/Epan
Epan maintenance crucial for accurate readingsNeed daily to weekly Epan averagesCalculation of Eto is only as good as the input valuesWhile E
pan and Eto are challenges to forecast, use of historical averages can helpSlide9
Year
R
2
Slope (p-value)
x
Intercept (p-value)
y
1985
0.86
1.00 (1.00)
-0.31 (0.09)
1986
0.81
0.95 (0.19)
-0.04 (0.86)
1987
0.78
0.97 (0.55)
-0.02 (0.93)
1988
0.76
1.06 (0.25)
-0.19 (0.51)
1989
0.79
0.9 (0.01)
-0.29 (0.17)
1990
0.78
0.93 (0.07)
-0.27 (0.22)
1991
0.57
0.86 (0.02)
0.46 (0.23)
1992
0.61
0.84 (0.01)-0.003 (0.99) 19930.750.97 (0.48)-0.24 (0.37) 19940.560.76 (<0.01) 0.60 (0.05) 19950.590.93 (0.30) 0.29 (0.44) 19960.570.81 (<0.01) 0.56 (0.07) 19970.450.77 (<0.01) 1.08 (0.01) 19980.490.77 (<0.01) 0.92 (0.02) 19990.580.95 (0.48) 0.24 (0.55) 20000.440.75 (<0.01) 0.99 (0.02) 20010.590.94 (0.35) 0.47 (0.20) 20020.380.50 (<0.01) 2.34 (<0.01) 20030.300.49 (<0.01) 2.18 (<0.01) 20040.040.17 (<0.01) 3.37 (<0.01) 20050.130.45 (<0.01) 2.66 (<0.01) 20060.560.86 (0.04) 0.41 (0.28) 20070.470.80 (0.01) 0.11 (0.02) x Testing that slope is different than 1 (2-tailed). y Testing that the intercept is different than 0 (2-tailed).
Resulting regression parameters from comparing calculated E
to
to actual
E
panSlide10
Annual Epan values decreasing
Average Daily Epan
Historically
(May-Oct,170)=
5.97mm
Average Daily
E
pan
2000-2007
(May-Oct,170)=
4.63mmSlide11
Trends vs. Events
Epan as a general favorability indicator
Temperature most common and easily
accessible weather input
How
to temperature trends vs. specific events influence PM development?Slide12
What we know: What we don’t:Suboptimal temperature effects on existing coloniesSuboptimal effects on epidemic developmentWhat do cold temperatures do?
Temperature and powdery mildewSlide13
Cold-induced resistance
*
Percent in Class (%)
Appressorium
Branched hyphaeSlide14
Cold kills (or at least hurts a little…)
Four-day-old colony grown at 25°C:: Line Sketch of colony footprint, and Same colony visualized with a vital stain
Four-day-old colony exposed to 2°C for 8h at 3dpi:
:
Line Sketch of colony footprint, and
Same colony visualized with a vital stainSlide15
It is true… New York is coldSlide16
Similar cold events occur globally
Site
Climate Type
Average daily minimum (°C
)
Days between
budbreak
and bloom with
min.
temps
<
6°C
Geneva, New York
Cool temperate
7.1
17
Hobart, Tasmania
Maritime
6.0
21
Davis, California
Mediterranean
7.6
16
Bernkastle, Germany
Northern temperate
7.5
18
Loxton, South Australia
Mediterranean
/
desert
8.9
17
Raleigh, North Carolina
Warm temperate
10.2
14Slide17
ConsiderationsEpan, in theory, is a useful environmental parameter to use for disease forecastingIncorporates multiple weather parameters and their interaction on water stress and availability to the pathogen.May also help understand plant stress- useful in obligate biotroph systems.
Temperature in PM forecasting may best be used as an indicator of acute unfavorable events for disease or pathogen developmentSlide18
Questions?Slide19
Air temperature is deceiving Date
Time
x
Air
y
(°C)
Leaf
y
(°C)
12-13 May
7:30pm
5:20am
16.8 (0.6)
3.5 (0.1)
13.0 (1.4)
0.5 (0.2)
17-18 May
7:30pm
5:20am
11.3 (0.0)
4.6 (0.0)
5.7 (1.9)
3.7 (0.5)
18-19 May
7:30pm
5:20am
11.4 (0.4)
1.7 (0.1)
4.8 (2.1)
-1.7 (0.5)
x
Times of measurements were approximately 1 h before sunset and 30 min before
sunrise
y
Temperature as indicated by infrared remote sensing of leaf surface, or a thermocouple located 2 cm above the leaf surface. Values in parenthesis are standard errors of the mean.