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Agrohacks - Down To Earth ~ a tool alerting farmers about forecasted severe weather conditions

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Presentation of the #SanJuanINSPIREHackathon winning team, showing their project: Bajada a Tierra (Down To Earth) ~ a tool alerting farmers about forecasted severe weather conditions.

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Agrohacks - Down To Earth ~ a tool alerting farmers about forecasted severe weather conditions

  1. 1. Idea for Hackathon Participate in a challenging activity under couching of specialists in the use of climatic and soil data. Test our abilities, skills and contribute with local knowledge on the topics. Sharing experiences and networking.
  2. 2. Lack of data & Acces Lack of knowledge about climate variables in agricultural community Improvement of tools and products for agricultural and government entities Down to ground Where we make available to agricultural producers and government entities a tool that use free sofware, free data and products that can alert producer about weather conditions and complement the servicies of INTA – SMN, others
  3. 3. JANUARY JULY
  4. 4. We select a Grape production for fine wine, to aplly diferrent indexes, i.e. Integral Térmica Activa de Winkler ITW = Σ (Tmd – 10 ºC) x días del mes.1 Sept 31 Mar
  5. 5. We select a Grape production for fine wine, to aplly diferrent indexes, i.e. Coeficiente Hidrométrico de Zuluaga CH= Σ (Tmm x mm lluvia)/PLH1 Sept 31 Mar
  6. 6. We select a Grape production for fine wine, to aplly diferrent indexes, i.e. ITW + CH
  7. 7. Correlation between reanalysis data with sensor data. Errors. R-squared 0.626 BIAS 1.107986813 RMSD 3.923062333 R-squared 0.577 BIAS -0.232598025 RMSD 3.400398529
  8. 8. Air T & H
  9. 9. Air T & H vs. Soil T & Water Potencial
  10. 10. Identify Zonda Wind Events (IZWE)
  11. 11. Data input for Zonda index - T and Td at 850, 700, 500, 400 and 300 hPa (wyoming, GFS or others) Meteoblue products
  12. 12. Foreseen potential - In this hackathon, we identify the convenience to development a Zonda Forcast - Different indixes or coefficients of each type of agricultural production in other place, can be used to generate maps such as those developed in this hackathon - All scripts were create under euxdat
  13. 13. Conclusiones: - High potential for the use of meteorological and soil data to apply to diferente agricultural productions (specific index). - Limitactions of reanalysis models. - The need to develop a common language for data management between diferente research disciplines was identified into our team. - Hackathon is a Good place to motivate students and researchers.
  14. 14. Thanks: - UNSJ - University of West Bohemia - To the rest of the participating teams

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