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Utilizing Weather Data for Agricultural Insurance in La Meuse Rapeseed

Explore historical rapeseed yields in La Meuse, identifying underlying weather causes for bad yields, using data to determine frequency of returns, addressing homogeneity issues, and de-trending data to pinpoint "bad" years since 1980.

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Utilizing Weather Data for Agricultural Insurance in La Meuse Rapeseed

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

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

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

  6. 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. 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. May June rainfall in Metz: Meteo-France data 2001 pre-harvest draught

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

  10. 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

  11. Thank you

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