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TEAM 2: Climatic Services for Africa


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at the Nairobi INSPIRE Hackathon 2019

Published in: Environment
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TEAM 2: Climatic Services for Africa

  1. 1. Climate services for Africa Nairobi INSPIRE Hackaton 2019 TEAM 2 Karel Jedlička
  2. 2. Climate services for Africa • Team • Motivation • Idea • Maize cultivation – an example of a good practise • Discussion & future plans
  3. 3. Team • 83 people from 19 countries interested • 22 people following the team intermediate results
  4. 4. Kizito Odhiambo Arusey Chebet Patrice Mirindi Christoph Ramshorn Karl Gutbrodt Karel Jedlicka Team Chris Prince Udochukwu Njoku Sebastian Schogl
  5. 5. Motivation
  6. 6. Idea • Aim ~ demonstrate options of meteorological data exploitation for the needs of African farmers • Approach • Design a clear use case • Describe processes of the use case • Outline communication channels to end users • Constraints • Short time schedule • Limited knowledge of local conditions • Communication channels to the end user
  7. 7. Climate data for maize cultivation • Use case: a farmer plans to plant and cultivate maize on a field • The farmer can rely on a traditional approach - or - • The farmer can use meteorological data
  8. 8. Climate data for maize cultivation • Use case: a farmer uses meteo data to plan maze planting and cultivation • The farmer sends a field position to the climate service • The service provides: • Growth plan • Nitrogen plan • Insect pests alert What ?
  9. 9. Climate data for maize cultivation • Insect pests alert • Input • Position of the field • Weather forecast (temperatures) • Process • Calculation of cumulation of effective temperatures from weather forecast data • Output • A risk ratio of insect pests attack forecast for upcoming days How ?
  10. 10. Climate data for maize cultivation • Insect pests alert • Process • Connect to Weather Forecast API (meteoblue agriculture API) • Request 3 days (hour by hour) temperature forecast • Calculate how many hours in the upcoming 3 days temperature is above 10°C ~ and add the sum of these temperatures into a EffectiveTemperatureSum variable • If the EffectiveTemperatureSum exceeds a threshold (e.g. a species need 1400°C of cumulative heat to evolve) the system stores a timestamp when that is going to happen How ?
  11. 11. Climate data for maize cultivation • Nitrogen plan • Inputs • Position • (Soil type) • (Planting day) • Weather forecast data (temperature, moisture) • Process • Searching a date in next 14 days, suitable for fertilisation (soil moisture higher than a treshold or rain expectation) • Output • Dates recommended for nitrogen fertilisation How ?
  12. 12. Climate data for maize cultivation • Nitrogen plan • Apply the fertilizer when the soil is (or going to be) moist (asking meteoblue agriculture API. • Apply Di-Ammonium Phosphate (DAP) or NPK at a rate of 50 kg per acre at the planting time. • Once the crop is about 45 cm high use Calcium Ammonium Nitrate (CAN) at a rate of 50 kg per acre in low rainfall areas. • In high rainfall areas, split fertilization in 2 parts. 1. part: 6 weeks after planting, 2. Part: just before maize flowers. How ?
  13. 13. Climate data for maize cultivation • Growth plan • Inputs • Position • (Soil type) • Historic meteorological data • Area specific maize growth model • Process • Analysis of historic meteorologic data matching conditions defined by the growth model • Output • Table relatig planting date to a likely yield How ?
  14. 14. Climate data for maize cultivation • Growth plan • Process • Simulating yield with a crop model (CERES-Maize, DSSAT) with meteoblue historical meteorological data. • Simulation period will be one year by setting manually different “virtually” planting dates. The CERES model output will be yield as a function of the planting date respectively planting period. • This approach can be applied for 30-years historical data provided by meteoblue. • This leads to an optimized planting date/period as a function of meteorological parameters (Temperature sums, precipitation, radiation) • For the actual season, meteorological data of the actual season and the 7-day forecast are taken into account in order to estimate the optimal planting date. How ?
  15. 15. Climate data for maize cultivation • EUXDAT e-Infrastructure • Client side: web based coding environment • Jupyter Notebook & Python • Server side (cloud/HPC): • GDAL/OGR • GRASS • Meteoblue API • Orfeo • … How ?
  16. 16. Climate data for maize cultivation • We can tell farmer • When to plant • When to fertilize • When to put a pesticide How ? But How?
  17. 17. Climate data for maize cultivation • Communication channels • General findings • Get introduced by a trustworthy person. • Cooperate with local agriculture trainers, agriculture coordinators, farmers. • Communicate in local language if possible. • Proposed channels • Web page • mobile app • SMS • GSM call • Personal contact (trainers) (farmers, trainers) (farmers) (farmers) How ? (farmers)
  18. 18. Conclusion • Concept exists, proof of concept ahead • Next actions • ( and StarGate projects • Discussion continues at