Since many farm management decisions are taken without knowing what the season going to be, advance information about the possible seasonal conditions will help farmers in making more informed decisions.

1. Communicating downscaled, probabilistic
seasonal forecasts and evaluating their impact
on farmers’ management of climate risks:
Examples from Kaffrine (Senegal)
and Wote (Kenya)
Ousmane Ndiaye – ANACIM
K.P.C. Rao – ICRISAT
Jim Hansen – CCAFS, IRI
Arame Tall – CCAFS, ICRISAT

2. Hypothesis
Since many farm management decisions are
taken without knowing what the season going to
be, advance information about the possible
seasonal conditions will help farmers in making
more informed decisions.
Sahel: Annual Precipitation
200
250
300
350
400
450
500
550
600
650
700
1900 1920 1940 1960 1980 2000
Rainfall(mm)
Observed

3. Key constraints addressed
• Lack of awareness about seasonal climate
forecasts and their reliability
• Misperceptions about the climate and its
variability
• Lack of understanding about the probabilistic
nature of forecast information
• Non-availability of information in a format that
can easily be understood by the farmers
• Dialogue between users and producers of
climate information

4. National institutions working on food
security (+ social, dissemination)
Local expert group
Rural radio SMS
Farmers
Face to
face
PRODUCTIONTAILORINGCOMMUNICATION
STEP 1: BUILDING AN INTEGRATED FRAMEWORK:
THE MULTI-DISCIPLINARY WORKING GROUP

5. Seasonal forecast  varieties
Onset forecast  farm
preparation
Nowcasting  flooding saving life (thunder)
Daily forecast  use of fertilizer / pesticide
Decade forecast  weeding, field work
Evaluation
Lessons drawn
Training workshop
Indigenous knowledge
Discussion and meetings
Field Visits
experts meeting each 10 days :
monitoring the season
Decade forecast  optimum
harvesting period
Daily forecast  saving crops
left outside
Before During the Crop season Maturity/end

6. Methods used in Kaffrine (West
Africa) and Wote (East Africa)
• The study was conducted in Kaffrine disctrict
(Senegal) and Wote division, Makueni district,
Eastern province (Kenya) during the 2011 &
2012 rainy seasons
• Study treatments include
– Survey (Control)
– Interpreting and presenting seasonal forecast
information in the form of an agro-advisory
– Training workshop along with advisory
– Evaluation

7. Building on local knowledge:
High humidity and high temperatures
can explain some of their indicators 
“Stronger monsoon”
Doing quite the same thing BUT
Better observing system
More reliable storage capacity
(numbers, maps, computers, …)
« When the wind change
direction to fetch the rain »
=
Wind change from harmatan
to monsoon during onset
STEP 2: BUILDING TRUST
LINKAGE TO INDIGENEOUS KNOWLEDGE

8. team work : farmers, climatologist, World Vision, Agriculture expert, sociologist
“KNOWLEDGE SHOULD PRECEDE ACTION”
Farmer in kaffrine

9. Wote: Observed responses
Treatment
Area cultivated (ha) Investment
(Ksh/ha)
Yield (kg/ha)
PS ES
Control (T1) 1.53 2.06 1797 386.8
Training
workshop (T2)
2.00 1.89 2043 447.3
Agro-advisory
(T3)
2.04 1.62 6092 613.8
Training
workshop and
advisory (T4)
2.10 1.94 3400 441.4

10.  First step : building trust (social dimension : using indigeneous
knowledge)
 Giving not only useful BUT useable forecast (tailored for specific
user needs)
 Long term and multi-stakeholders partnership (each institution
has part of the solution for food security)
 Communicating probabilistic aspect of the forecast (easy to
understand, can translate into action and to evaluate)
 Dynamic process : need to better understand farmers decision
system (long term dynamical partnership)
 The forecast covers a large area : we need forecast at farm level
 Farmers still lack of tools and materials beside climate information
LESSONS AND CHALLENGES

11.  « We were guessing now we have decision tools »
 « The early warning system of an very early rainfall
saved all my crops left outsides»
 « with eminent rainfall forecast through sms
(nowcasting) we can saveguard our cattle, return
from farms to avoid thunder »
 « we woman (soeur unies de Ngodiba) are now
better of and as equipped as men now. »
FARMER TESTIMONIALS (Kaffrine)

12. Demand for climate services (Wote)
Village/treatment
Amount willing to pay (Ksh/season)
Women Men All
Training workshop (T2) 258 357 313
Agro-advisory (T3) 228 204 211
Training workshop and
advisory (T4)
385 364 368
All villages 262 263 261

13. Methods
• In Kaffrine: 300 farmers trained, more than 1000s
received climate services (33% of women)
• In Wote: A total of 117 farmers (61% women)
accessed and used climate agro-advisories
• Farmer use of climate information was assessed
by conducting three surveys
– Before training or providing forecast information
– During the season
– After
the season
ACHIEVEMENTS

14. THANK YOU