Walloon Agricultural Research Centre (CRA-W) Meteorological data and lateblight modeling

Transcription

1 (CRA-W) Meteorological data and lateblight modeling China-Europe Workshop of Potato Sustainable and Efficient Productions Harbin, 24th of July 2015 Damien Rosillon Version 25/04/2014

2 CRA-W : 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

3 CRA-W : Breeding and biodiversity Unit Farming systems, Territory and Information Technology Unit

4 Architecture of a warning system

5 CRA-W s Automatic weather stations network 38 automatic weather stations on an area of km² 1 weather station for 440 km² Average distance between stations : 30 km

6 Automatic weather stations network

7 A weather station Pyranometer Wind vane Raingauge Air temperature and relative humidity sensor Anemometer Ground surface temperature probe Soil temperature probe (- 20 cm) 7

8 Automatic weather stations network «An automatic weather station is automatic. After its installation nothing more to do.» Wrong! Clogged raingauge

9 Automatic weather stations network «An automatic weather station is automatic. After its installation nothing more to do.» Wrong! Blokked raingauge

10 Automatic weather stations network «An automatic weather station is automatic. After its installation nothing more to do.» Wrong! Dust on relative humidity sensor

11 Automatic weather stations network «An automatic weather station is automatic. After its installation nothing more to do.» Wrong! «Hostile» environment

12 Late blight modeling 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

13 Late blight modeling RH curve T curve Incubation curve Infection and level of gravity Cumulated daily rainfall rainfall

14 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

15 Impact of meteorological data on model outputs Underestimation of 5% of RH measurements

16 Impact of meteorological data on model outputs Overestimation of 5% on RH measurements

17 Impact of meteorological data on model outputs 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

18 Meteorological data management 40 stations 6 sensors per station 1 record / hour 5760 data a day data a year

19 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

20 Validation tools : step 1 data check Human procedures daily maps

21 Validation tools : step 1 data check Human procedures graphic analysis

22 Validation tools : step 1 data check Human procedures monthly indicators Indicateurs region station indic tsa indic_hra indic_ens_inc_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-Wasti 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_belgiqulouvain-la-neu 0,3-4% 13% -34% 0% moyenne_belgiqufloriffoux -0,7 2% 1% -16% 0% moyenne_belgiquleuze -0,1-5% -2% -18% 0%

23 Validation tools : step 1 data check Human procedures monthly indicators June 2013

24 Validation tools : step 1 data check Human procedures monthly indicators August 2013

25 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

26 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

27 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

28 Contact Websites : (weather station network website) Damien Rosillon : d.rosillon@cra.wallonie.be

29 Thank you for your attention