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

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

Published in: Education, Technology
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  • 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

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