Communica)ng	
  downscaled,	
  probabilis)c	
  
seasonal	
  forecasts	
  and	
  evalua)ng	
  their	
  impact	
  
on	
  far...
Hypothesis
Since	
   many	
   farm	
   management	
   decisions	
   are	
  
taken	
   without	
   knowing	
   what	
   the...
Key constraints addressed
•  Lack	
  of	
  awareness	
  about	
  seasonal	
  climate	
  
forecasts	
  and	
  their	
  reli...
NaHonal	
  insHtuHons	
  working	
  on	
  food	
  
security	
  (+	
  social,	
  disseminaHon)	
  
Local	
  expert	
  group...
Seasonal	
  forecast	
  ⇒	
  varie)es	
  
Onset	
  forecast	
  ⇒	
  farm	
  	
  
prepara)on	
  
Nowcas)ng	
  ⇒	
  flooding	...
Methods used in Kaffrine (West
Africa) and Wote (East Africa)
•  The	
  study	
  was	
  conducted	
  in	
  Kaffrine	
  disc...
Building	
  on	
  local	
  knowledge:	
  
High	
  humidity	
  and	
  high	
  temperatures	
  
can	
  explain	
  some	
  of...
team work : farmers, climatologist, World Vision, Agriculture expert, sociologist
“KNOWLEDGE SHOULD PRECEDE ACTION”
Farmer...
Wote: Observed responses
Treatment	
  
Area	
  cul)vated	
  (ha)	
   Investment	
  
(Ksh/ha)	
  
Yield	
  (kg/ha)	
  
PS	
...
Expectation for the season
Village/treatment	
  
Women	
  farmers	
   Men	
  farmers	
   All	
  
No	
   Yes	
   No	
   Yes...
Ø  First	
  step	
  :	
  building	
  trust	
  	
  (social	
  dimension	
  :	
  using	
  indigeneous	
  
knowledge)	
  
Ø...
Ø 	
  « We	
  were	
  guessing	
  now	
  we	
  have	
  decision	
  tools	
  »	
  
Ø 	
  « The	
  early	
  warning	
  sys...
Demand for climate services (Wote)
Village/treatment	
  
Amount	
  willing	
  to	
  pay	
  (Ksh/season)	
  
Women	
   Men	...
Methods	
  
•  In	
  Kaffrine:	
  300	
  farmers	
  trained,	
  more	
  than	
  1000s	
  
received	
  climate	
  services	
...
THANK YOU
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Climate Services: Empowering Farmers to confront climate risks at village-level

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

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Climate Services: Empowering Farmers to confront climate risks at village-level

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

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