Below are some brief notes from discussions held during the day (For
information on the workshop content, see the presentations and program, to be
found separately).
Local signs in nature:
One person commented that the signs seen at the beginning of the rainy
season e.g. much fruit on the trees, more calves, etc. might actually be a
reflection on the conditions the year before instead of predictions of the
coming season. More observations and comparisons would need to be made than on
just the two seasons in this study.
For the two years in which the study was held, the signs in nature and
the seasonal forecasts gave the same indications. IK is important to include
because if you respect the knowledge have themselves, they might be also being
open to new technology and knowledge. It is important that all types of knowledge
be gathered and studied in order to increase understanding and use in a
proactive manner.
Empowerment of Extension
Service officers
The project has empowered extension service officers in uploading
wireless sensor data and reading the output graphs and understanding the
modeling-assisted seasonal forecast information. Training is needed as part of
any sensor-based information collection exercises to make the outputs useful on
the ground. Training would be needed in the care and data collection as related
to the sensors, hydrological modeling and analysis of seasonal forecasts as
well as scale issues. Early warning is only one part of the story. There must
be willingness and opportunity from farmers to act on the information they
receive.
How do we tackle the uncertainty issue in
seasonal forecasting?
There are
always uncertainties in predictions. The best way to handle this is by combining
different sources and types of information. Uncertainty is not only related to
the resolution of rainfall forecasting but also about how soil moisture is
different according to soil types, vegetation, etc. There must be systems in
place to improve the accuracy of information. Sensors only measure conditions
at specific points. Much thought must be put into choosing where to put the
sensors. GIS could be used to detect drought prone areas by using the
information from sensors placed at strategic and representative areas in terms
of soil, etc. System thinking is key to create a sustainable and useful
program. Different incidents have been built into the disaster management
database LADMSI.
How to continue the North-South collaboration?
Both sides
will look for funding opportunities related to networking as well as continued
project work to bring the project to the next stage. One possibility is WRC
funding.
