Reference and Crop Leaf Wetness Duration: Measurements and Estimates

Paulo Sentelhas

University of São Paulo, Brazil Currently working for Weather Innovations Incorporated

Chatham, ON - Canada

The results that will be shown in this presentation were obtained with the support of the following institutions:

I also would like to thank the colleagues that helped me in all these works: Terry Gillespie (UofG), Mark Gleason (ISU),

Eduardo Santos (USP), José Eduardo Monteiro (USP), Simone Orlandini (UNIFI), Mario Pedro (IAC)

Presentation Outline

a) A brief overview about leaf wetness duration and its importance for plant diseases

b) Determining a standard protocol to measure reference LWD

c) Comparisons among LWD sensors

d) Crop LWD variability and its relation to reference LWD

e) Estimating LWD with empirical and physical models – RH threshold and Penman-Monteith physical approach

A brief overview about leaf wetness duration and its importance for plant diseases

Leaf wetness duration (LWD) is recognized as a very important weather parameter for plant disease epidemiology

LWD affects bacterial diseases (Citrus Canker)

Infection

Esporulation

LWD affects fungal diseases (Maize Antracnosis)

Germination

Penetration

Therefore, together with temperature, LWD is a key component of many plant disease warning systems,...

…which usually reduces number of sprays, costs and environmental threat.

Despite considerable effort to develop accurate electronic sensors to measure LWD, there has been little research attention paid to how measure this variable in a standard

condition (there is no observation standard, for either sensor or exposure conditions) or how its variability relates to

different cropping situations.

However, LWD is a difficult variable to measure or to estimate because it is driven by both atmospheric

conditions and their interactions with the structure and composition of the vegetative community.

Determining a standard protocol to measure reference LWD

Leaf Wetness measurements have been made in many creative ways…

Flat plates are most common method

Cylindrical sensor is also a common method and has been

used by WIN in its network

Site-specific turf “tee sensor” is not very common…

… as well as wire clips on plants.

Problem is… having any of these sensors in the grower’s field may interfere with farm operations.

• Can we put the sensors “near” the field?

• Would a “standard” measurement at an Agmet weather station be worthwhile?

Lab tests: LWD sensors test and calibration

LWD sensors

Data-logger

DPG

High CV (67%) with unpainted sensors

Much lower CV (9%) with painted sensors

Field test - The importance of painting wetness sensors- To increase the sensitivity to microscopic droplets

- To mimic leaf optical and thermal properties

All sensors at the same position 30 cm and 30 degrees

Avg. variability ~ 2.5 %, or about 20 minutes for a 12-hour average wet period.

Field experiment at Elora R.S., Univ. of Guelph

General view of the experiment set up at the grass field where LWD sensors were installed at different heights and angles, and

other weather variables were measured

Field experiment at Elora R.S.

Visual observation of dew on-set and dry-off at the grass and

corn field

Dew on-set on corn leaf

Wetness on corn leaf early in the morning

Field experiment at Elora R.S.

Dew on-set observation on grass

Comparing 30 cm sensors at various angles to visual observations on turf

At dew onset…

At end of wetness…

• Horizontal (0 degrees) wet longer when drying

• Hard to choose “best” from other angles

Effect of height of exposure on LWD

Crop LWD versus LWD measured near screen height over grass.

Crop LWD versus LWD measured at 30 cm height over grass.

Summary remarks…

• Height of exposure of LWD sensors in a weather station has a significant effect on measured wetness duration

• Angles of exposure between 15 and 45 degrees have similar degree of variability in LWD measurements as the variability between individual sensors

• Horizontal exposure is not recommended – leads to prolonged drying period

• Angled sensors at 30 cm above turf grass in a weather station mimicked turfgrass and nearby crop wetness quite well – a possible standard for weather station exposure

Comparisons among sensors

Which sensor is better?

How to assess their performance?

X

Elora, ON - Canada

ME = MAE = 37 min

Piracicaba, SP - Brazil

0o 15o 30o

45o 60o

LW

D_c

ylin

dri

cal

sen

sors

(h

)

LWD_flat plate sensors (h)

No much difference at any angle!!!

Piracicaba, SP - Brazil

Maybe a smaller angle could improve the performance!!!

Summary remarks…

Any LWD sensor can perform very well, since calibrated and tested under lab and field conditions and installed

following specific recommendations:

Angle: 15 – 45o

Height: 30 cm (for reference over turfgrass)Color: white or light green or gray

Orientation: facing N (Northern Hemisphere)

Crop LWD variability and its relation to “reference” LWD

Measurements in several different crops

Soybean

Cotton

Tomato

Maize

Muskmelon

AppleCoffee

Grape

Average leaf wetness duration (LWD, hours) for different positions in apple, coffee, maize, grape, and muskmelon canopies

Cotton

Inside

LW

D_

cott

on

(h

)

Top

LW

D_

cott

on

(h

)

LWD_grass (h) LWD_grass (h)

LW

D_

coff

ee

(h

)

LWD_grass (h)

Coffee

Top Middle Third - NE Lower Third - NE

Middle Third - SW Lower Third - SWInside

Apple

Top Middle Bottom

Top InsideMaize

Top Bottom

Grape

Summary remarks…

• The pattern of LWD variation inside the crops differed according to the characteristics of their canopies. For the majority of the crops assessed, the longest LWD is at the top of the canopy.

• The use of reference LWD measured over turfgrass as an estimator of LWD at the top of the canopies seems to be a very good option for crops that have maximum LWD at this position or have little variation of LWD at different canopy positions.

Estimating LWD with empirical and physical models – RH threshold and Penman-Monteith physical approach

When LWD sensors are not available or when LWD past historical series must be determined for research purposes, LWD can be estimated by different models.

They are easy to apply but require ajustments for each new location and climate. Normally, they use Relative Humidity (RH) or Dew Point Depression (DPD), and thresholds for wetness onset and dry-off must be calibrated locally.

Based on the physical principles of dew formation and dew and/or rain evaporation. They have shown good portability and sufficiently accurate results for operational use. However, the requirement of hourly Rn seems to be a disadvantage.

LWD Empirical Models LWD Physical Models

LWD Empirical Models, based on RH

a) Constant Threshold (RH≥90%)

Wetness presence when RH ≥ 90%

b) Dew point depression (DPD)

DPD = Tair – Tdew

Wetness onset when DPD smaller than 1.8oC Wetness dry off when DPD greater than 2.2oC

c) Extended threshold (EXT_RH)

Wetness presence when RH > 87% Wetness absence when RH < 70% When RH between 70 and 87%, wetness is present if RH increases in a rate greater than 3% per 30 min and it begins to dry when RH decreases in a rate greater than 2% per 30 min

LWD Empirical Models

Evaluation of calibrated

Performance of RH model when locally calibrated, with independet data

Among the physical models used to estimate wetness deposition and evaporation, the one based on the Penman-Monteith equation has some

advantages over those based on the energy balance approach, such as the lack of need for a leaf temperature estimate, which makes it easily applicable using hourly data from a nearby automatic weather station.

LWD Physical Models

The Penman-Monteith approach assumes that air temperature measured at given height above turfgrass at a standard weather station is equivalent to temperature at the same height above the top of a crop canopy, and that adding a resistance item to the model is enough to account for the air layer from measurement height, above the canopy, to the level of the leaves.

LE

+L

E -

Day n Day n+1

Wetness onset

Wetness dry-off

LWD

Using the procedures presented earlier, wetness onset occurs when LE > 0, and wetness dry-off occurs when condensation accumulated by the model is consumed by an equivalent amount of evaporation (LE < 0)

“Reference” LWD

X Crop LWD

Crop LWD versus “reference” LWD measured at 30 cm height over turfgrass

LW

D_

cott

on

(h

)

LWD_grass (h)

Cotton

Grape

Apple

Crop LWD versus “reference” LWD measured at 30 cm height over turfgrass.

The Hypothesis:

A reference LWD, estimated by a Penman-Monteith approach using weather data, from a standard meteorological station, could provide accurate estimates of reference and crop LWD.

Sites, Crops, Datasets and Climates

Penman-Monteith Model to Estimate “Reference” LWD

s = the slope of the saturation vapor pressure curve, esTa = the saturated vapor presure at the WS air temp, ea = the actual air vapor pressure, * = the modified psychrometer constant (= 0.64 kPa K-1 with moisture and heat transfer for both sides of sensor during dew, and = 1.28 kPa K-1 for evaporation from one side of a sensor after rain)

D = the effective dimension of the mock leaf (=0.07m), u = the winf speed at the WS.

Wetness onset: occurs when LE > 0 or rain occurs

Wetness dry-off : occurs when the condensantion and/or rain accumulated by the model is consumed by na equivalent amount of evaporation

Between 13h of day n to 12h of day n+1

Data from Rn sensor or estimated by different models

is the boundary layer resistance forheat transfer

is the aerodynamic resistance

Estimated LWD X Measured LWD

for different heights on turfgrass

190 cm

110 cm

30 cm

Our “reference” LWD

Estimated LWD X Measured LWD for different heights on turfgrass

ME = +0.47 h MAE =

0.76 h

ME = +0.20 h MAE =

0.89 h

ME = +0.78 h MAE =

1.05 h

Estimated LWD X Measured LWD for 30cm on turfgrass at different sites

ME = +1.33 h MAE = 1.55

h

Estimated “Reference” LWD X Measured Crop LWD

X

Estimated “Reference” LWD

Measured “reference” LWD X Measured crop LWD for different sites and crops

Estimated “reference” LWD X Measured crop LWD for different

sites and crops

Estimated Crop LWD X Measured Crop LWD

XW*

Estimated Crop LWD

General wetness coefficient (general W) used to convert

“reference” LWD into crop LWD

Specific wetness coefficient (specific W)

used to convert “reference” LWD into crop LWD

a) Coffee

b) Grape

c) Maize

d) Soybean

e) Tomato

Estimated Crop LWD X Measured Crop LWD (Independent Data)

Specific W General W

ME = +0.10 h MAE =

1.25 h

ME = +0.40 h MAE =

1.31 h

RH-based empirical models for LWD estimation, when not calibrated locally, performed differently at locations with contrasting climates.

When the RH model was locally calibrated, the accuracy of LWD estimates improved substantially, and resulting errors were in average 1.6 h.

A Penman-Monteith approach was able to estimate “reference” LWD over turfgrass at different heights with high accuracy and precision.

When compared to LWD measured in five different crops with different heights and architecture, 30-cm height “reference” LWD estimated by P-M model showed promise as a simple and useful tool to estimate crop LWD.

When an empirical coefficient (W) was applied in a two-step procedure to convert estimated “reference” LWD into crop LWD, a significant improvement in crop LWD estimates was observed, with mean absolute errors of 1.3 h.

Summary remarks…

Any tested, painted and angled sensors at 30 cm above turfgrass in a weather station mimicked turfgrass wetness quite well – a possible standard for weather station exposure.

General remarks

TOP

LWD

_cot

ton

(h)

LWD_grass (h)

For the majority of the crops assessed, the longest LWD occurred at the top of the canopy, which is quite well mimicked by reference LWD sensor.

When sensors are not available for reference LWD measurements, there are missing data, or when LWD historical data are required, this variable can be estimated from weather data by both empirical (RH threshold) and physical (PM) models, since calibrated or adjusted locally. However, the errors are normally greater than those obtained with measurements.

Thank you