at the Nairobi INSPIRE Hackathon 2019

1. Climate services for Africa
Nairobi INSPIRE Hackaton
2019
TEAM 2
Karel Jedlička
geomatics.zcu.cz

2. Climate services for Africa
• Team
• Motivation
• Idea
• Maize cultivation – an example of a good practise
• Discussion & future plans

3. Team
• 83 people from 19 countries
interested
• 22 people following the
team intermediate results

4. Kizito Odhiambo
Arusey Chebet
Patrice Mirindi
Christoph Ramshorn
Karl Gutbrodt
Karel Jedlicka
Team
Chris Prince Udochukwu
Njoku
Sebastian Schogl

5. Motivation

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Climate data for maize cultivation
• We can tell farmer
• When to plant
• When to fertilize
• When to put a pesticide
How ?
But How?

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. Conclusion
• Concept exists, proof of concept ahead
• Next actions
• (euxdat.eu) and StarGate projects
• Discussion continues at bit.ly/Clima4Africa