walloon agricultural research centre (cra-w ... · validation tools : step 1 data check • human...

Walloon Agricultural Research CentreAgriculture and Natural Environment Department

Farming systems, Territory and Information Technology Unitwww.cra.wallonie.be

Walloon Agricultural Research Centre

(CRA-W)

Meteorological data and lateblight

modeling

Version 25/04/2014

China-Europe Workshop of Potato Sustainable and Efficient Productions

Harbin, 24th of July 2015 Damien Rosillon



CRA-W : Walloon Agricultural Research Centre

• A public agricultural research centre based in Belgium

• Multidisciplinary research institution

• CRA-W it is

• 500 people

• 300 ha of facilities

• 3 sites

• 16 research units

• 4 research departments



CRA-W : Walloon Agricultural Research Centre

Breeding and biodiversity Unit

Farming systems, Territory

and Information

Technology Unit



Architecture of a warning system



CRA-W’s Automatic weather stations network

• 38 automatic weather stations on an area

of 16.844 km²

• 1 weather station for 440 km²

• Average distance between stations : 30 km



Automatic weather stations network



A weather station

7

Raingauge

Ground surface

temperature probe

Wind vane

Air temperature and

relative humidity sensor

Anemometer

Pyranometer

Soil temperature

probe (- 20 cm)




• « An automatic weather station is automatic. After its

installation nothing more to do. »

• Wrong!

• Clogged raingauge






• Wrong!

• Blokked raingauge






• Wrong!

• Dust on relative humidity sensor






• Wrong!

• « Hostile » environment



• Developpement of a model based on Guntz and Divoux

principles

• Based on past weather data

• Helps the expert to interpret meteorological data.

• Are the meteorological conditions favourable to late blight

development?

• It helps to decide if spraying fungicide is useful or not

• Outputs : a potential infection or fructification periods

• Detection of periods favourable to the infection

• Estimate of the infection gravity

• Measurment of the length of the incubation phase

• Detection of the day where the fructification phase can

happen

Late blight modeling



Late blight modeling

Infection and level ofgravity

rainfall

Incubation curve

RH curve

T° curve

Cumulateddaily rainfall



Impact of meteorological data on model outputs• Impact of a slight malfunction of a relative humidity sensor on

the amount of simulated infections

• Scenario 1 : regular situation



Impact of meteorological data on model outputs

• Underestimation of 5% of RH measurements




• Overestimation of 5% on RH measurements




• Model outputs is higlhy dependant on meteorological data

• Wrong meteorological data can lead to wrong decision

• Reliable meteorological data are crucial for a reliable late

blight warning system

• maintenance of weather stations network is crucial

• Maintenance of the site

• Frequent recalibration of the sensors

• …

• meteorological data must be validated



Meteorological data management

• 40 stations

• 6 sensors per station

• 1 record / hour

• 5760 data a day

• 2.102.400 data a year



Validation tools : step 1 data check

• Step 1 : Data check : identification of missing or wrong data

• Automatic procedures :• Check if data every hour

• Check by thresholds

• Impossible to entirely automate validation. Automatic procedures are

completed with « human » procedures

• « Human » procedures :• Visual analysis

• Monthly indicators




• Human procedures – daily maps




• Human procedures – graphic analysis

0

5

10

15

20

25

30

35

40

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241




• Human procedures – monthly indicators

region station indic tsa indic_hra indic_ens_cuminc_plu indic_missing

haute_belgique Libramont -0,2 0% 30% 2% 0%

haute_belgique Michamps -0,1 -3% 40% -22% 0%

haute_belgique Amberloup 0 0% -99% 1% 14%

haute_belgique Bergeval 0,7 0% 19% 9% 0%

haute_belgique Willerzie -0,2 3% 10% 9% 0%

haute_belgique MOYENNE 0 0% 0% 0% 3%

gaume Chassepierre 0,1 2% -5% 22% 0%

gaume Schockville -0,4 0% 6% -16% 0%

gaume Ruette 0,4 -2% 0% -6% 0%

gaume MOYENNE 0 0% 0% 0% 0%

intermediaire Chimay 0 0% 2% 1% 0%

intermediaire Ferrieres -0,5 -4% -14% -4% 0%

intermediaire Haut-le-Wastia 0 3% 16% -7% 0%

intermediaire Jemelle -0,1 2% -1% 2% 0%

intermediaire Seny 1 -5% -14% -19% 0%

intermediaire Jevoumont -0,4 4% 12% 26% 0%

intermediaire MOYENNE 0 0% 0% 0% 0%

moyenne_belgiqueLouvain-la-Neuve 0,3 -4% 13% -34% 0%

moyenne_belgiqueFloriffoux -0,7 2% 1% -16% 0%

moyenne_belgiqueLeuze -0,1 -5% -2% -18% 0%

Indicateurs




• June 2013






• August 2013



Validation tools : step 2 data correction

• Step 2 : Correction

• Integrated tools to correct the data• Linear interpolation

• Spatial interpolation

• Data translation

Validated meteorological data are sent to the model



Next steps

• « Field scale » decision support system• Virtual weather stations network

• Use of grid data

• GIS technics

Source : Racca Paolo et al., « Decision

Support Systems in Agriculture :

Administration of Meteorological Data,

Use of GIS and Validation methods in

crop protection warning service ».

ZEPP Germany

• Weather forecast integration



Conclusions

• What ever the source of the data :• Observation from weather station network

• Grid data

• Weather forecast

• Quality weather data are crucial for a quality decision support

system



• Websites :

• www.pameseb.be (weather station network website)

• www.cra.wallonie.be

• Damien Rosillon : [email protected]

• +32 61231010

Contact

http://www.pameseb.be/

http://www.cra.wallonie.be/

mailto:[email protected]



Thank you for your attention

walloon agricultural research centre (cra-w ... · validation tools : step 1 data check • human...

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