1. Using Weather data in Agriculture insurance Rapeseed in La Meuse Salah DHOUIB Weather and Agriculture Covers

2. 2Titre de la présentation Historical rapeseed yield in La Meuse Identification of the underlying weather causes of a bad yield: lack and/or excess rainfall, heat-wave, etc Using weather data to determine the frequency and period of return

3. 3Titre de la présentation Using Historical yield data Homogeneity Problem due to technical progress in the farming industry Availability problems: electronic data is not always easy to find Homogeneity Problem due to classification changes Target: De-trending data and identifying “bad” years

4. 4Titre de la présentation Rapeseed: Historical and de-trended yield in La Meuse since 1980 3 Low yield years: 83, 92 and 2001

5. 5Titre de la présentation Clear trend up to the nineties due to technical progress 3 catastrophic years: 1983, 1992 and 2001

6. 6Titre de la présentation Underlying weather causes Objective  Identifying key weather factors explaining bad yield How ?  Agronomic knowledge  Analysis of the correlation between extreme historical weather conditions and bad yield

7. 7Titre de la présentation Identifying weather factors: pre-harvest drought Example : Risk type « 2001 » : May June draught in La Meuse Ressources hydriques fin juin Vs moyenne 1950-2006

8. 8Titre de la présentation May June rainfall in Metz: Meteo-France data 2001 pre-harvest draught

9. 9Titre de la présentation May 15th-June 10th rainfall in Metz: Harvest Period “Flood” 1983, 1992 and 1994 harvest « floods »

10. 10Titre de la présentation Weather data / Yield data correlation Very complicated compared to say Energy sector More complicated in Europe where agriculture is less dependent on “Mother Nature” Bigger distance between weather station and the location of the risk: we need airports near farms Agriculture uses non-continuous weather phenomena: frost, rainfall, hail Axis of development: NDVI type of index, Satellite imagery, etc

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