Presented by Dr Ousmane Ndiaye (ANACIM, Senegal). Africa Agriculture Science Week 6, 15 July 2013, Accra, Ghana

1. Communica)ng
downscaled,
probabilis)c
seasonal
forecasts
and
evalua)ng
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
informaHon
about
the
possible
seasonal
condiHons
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
• MispercepHons
about
the
climate
and
its
variability
• Lack
of
understanding
about
the
probabilisHc
nature
of
forecast
informaHon
• Non-­‐availability
of
informaHon
in
a
format
that
can
easily
be
understood
by
the
farmers
• Dialogue
between
users
and
producers
of
climate
informaHon

4. NaHonal
insHtuHons
working
on
food
security
(+
social,
disseminaHon)
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
⇒
varie)es
Onset
forecast
⇒
farm
prepara)on
Nowcas)ng
⇒
flooding
saving
life
(thunder)
Daily
forecast
⇒
use
of
fer)lizer
/
pes)cide
Decade
forecast
⇒
weeding,
field
work
Evalua)on
Lessons
drawn
Training
workshop
Indigenous
knowledge
Discussion
and
mee)ngs
Field
Visits
experts
mee)ng
each
10
days
:
monitoring
the
season
Decade
forecast
⇒
op)mum
harves)ng
period
Daily
forecast
⇒
saving
crops
leS
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)
– InterpreHng
and
presenHng
seasonal
forecast
informaHon
in
the
form
of
an
agro-­‐advisory
– Training
workshop
along
with
advisory
– EvaluaHon

7. Building
on
local
knowledge:
High
humidity
and
high
temperatures
can
explain
some
of
their
indicators
è
“Stronger
monsoon”
Doing
quite
the
same
thing
BUT
Beer
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
cul)vated
(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. Expectation for the season
Village/treatment
Women
farmers
Men
farmers
All
No
Yes
No
Yes
No
Yes
Control
(T1)
82
18
82
18
82
18
Training
workshop
(T2)
63
38
54
46
59
41
Agro-­‐advisory
(T3)
53
47
42
58
52
48
Training
workshop
and
advisory
(T4)
27
73
33
67
30
70

11. Ø First
step
:
building
trust
(social
dimension
:
using
indigeneous
knowledge)
Ø Giving
not
only
useful
BUT
useable
forecast
(tailored
for
specific
user
needs)
Ø Long
term
and
mulH-­‐stakeholders
partnership
(each
insHtuHon
has
part
of
the
soluHon
for
food
security)
Ø CommunicaHng
probabilisHc
aspect
of
the
forecast
(easy
to
understand,
can
translate
into
acHon
and
to
evaluate)
Ø Dynamic
process
:
need
to
beer
understand
farmers
decision
system
(long
term
dynamical
partnership)
Ø The
forecast
covers
a
large
area
:
we
need
forecast
at
farm
level
Ø Farmers
sHll
lack
of
tools
and
materials
beside
climate
informaHon
LESSONS AND CHALLENGES

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

13. 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

14. 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
informaHon
was
assessed
by
conducHng
three
surveys
– Before
training
or
providing
forecast
informaHon
– During
the
season
– Aaer
the
season
ACHIEVEMENTS

15. THANK YOU