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Exploitation of Climatic Data in Agriculture

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Karel Jedlicka

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Exploitation of Climatic Data in Agriculture

  1. 1. Exploitation of climatic data in agriculture Use case of Agro-climatic zones Karel Jedlička This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777549 www.EUXDAT.eu European e-Infrastructure for Extreme Data Analytics in Sustainable Development
  2. 2. 2www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Agro-climatic zone refers to a land unit in terms of major climates, suitable for a certain range of crops and cultivars.  Agro-climatic conditions mainly refer to soil types, rainfall, temperature and water availability which influence the type of vegetations. An agro-ecological zone is the land unit carved out of agro-climatic zone superimposed on landform which acts as modifier to climate and length of growing period.
  3. 3. 3www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Current climate zones maps are very generic. These show large areas and display only some differences in topography. Characteristics such as seaside buffer zones, weather divides or South-North differences are usually not accounted.
  4. 4. 4www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Current climate zones maps are very generic showing large areas and display only some differences in topography. Characteristics such as seaside buffer zones, weather divides or South-North differences are usually not accounted.  The idea is to provide local agro-climatic maps by processing Historic weather databases and detailed Earth Observation data for topography and land cover.  Such improvements in the climate zones can support local/within-field management strategies.
  5. 5. 5www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Which of my fields faced freezing on 1st April during last 30 years?  What areas faced temperatures above 35°C in July, August in recent years?  Comparing historical weather data to a crop growth model, can I adjust my planting date to gather more yield?  When should I put nitrogen into my field to fertilize effectively?  When there is a risk of insect pest attacking my field? When should I put pesticide?  all above mentioned use cases have a common ground – to analyze historical/forecasted weather data (together with other data as well, DEM, soil maps, hydrology, …)  Where can I calculate it?
  6. 6. 6www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  EUXDAT e-Infrastructure • Client side: web based coding environment • Jupyter Notebook & Python • Server side (cloud/HPC): • GDAL/OGR • GRASS • Meteoblue API / CDS API • Orfeo Toolbox • …
  7. 7. 7www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Freezing days • Request temperature data for 1st of April (1989 – 2019) • For each place count temperatures above zero and below zero
  8. 8. 8www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Freezing days • Factors influencing temperature • Elevation • Distance from a water source • Slope orientation • Land cover Influence
  9. 9. 9www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Elevation as a factor influencing temperature • Temperature is related to topography • Temperature decreases by 0.65 °C for each 100 m of elevation • Topography models used by global meteo models are sparse
  10. 10. 10www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Elevation as a factor influencing temperature terrain model used for meteorological data (a cell ~ 4x4 km) - source meteoblue API EU-DEM terrain model (a cell ~ 30x30 m)
  11. 11. 11www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Elevation factor • Input ~ temperatures on a sparse surface model (one value per 4x4 km) • Recalculation of temperatures at sea level • Densification of the sparse input data of T0 temperature to one value per 30x30m using interpolation. • Applying the elevation factor to calculate temperatures back on detailed surface Surface Sea level
  12. 12. 13www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Elevation as a factor influencing temperature coarse densified
  13. 13. 14www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Elevation factor
  14. 14. 15www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Iterate for years 1989-2019
  15. 15. 20www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Which of my fields faced freezing on 1st April during last 30 years?  What areas faced temperatures above 35°C in July, August in recent years?  Comparing historical weather data to a crop growth model, can I adjust my planting date to gather more yield?  When should I put nitrogen into my field to fertilize effectively?  When there is a risk of insect pest attacking my field? When should I put pesticide?  all above mentioned use cases have a common ground – to analyze historical/forecasted weather data (together with other data as well, DEM, soil maps, hydrology, …)  Where can I calculate it?
  16. 16. 21www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Search of days with temperature above 35°C for July 2018 28x28kmcell 1x1kmcell
  17. 17. 22www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones  Which of my fields faced freezing on 1st April during last 30 years?  What areas faced temperatures above 35°C in July, August in recent years?  Comparing historical weather data to a crop growth model, can I adjust my planting date to gather more yield?  When should I put nitrogen into my field to fertilize effectively?  When there is a risk of insect pest attacking my field? When should I put pesticide?  the mentioned use cases have a common ground – to analyze historical/forecasted weather data (together with other data as well, DEM, soil maps, hydrology, …)
  18. 18. 23www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones
  19. 19. 24www.euxdat.eu Exploitation of climatic data in agriculture  Agro-climatic zones

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