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Engagement to help adapt
Makueni County farming
systems to climate change
and variability
AgMIP –
3rd February 2016
Guided by Makueni County
Integrated Development Plan:
2013-2017 & Kenya vision 2030
County stakeholders include Research
institutions whose role is:
(i) Research on appropriate farming
technologies
(ii) Research on viable crop and livestock
Ongoing CCAFS engagement
• Works with KALRO, ICRISAT, Makueni County
Dept of Agric, Livestock & Fisheries, and
farmers
• Pilot research involving participatory
evaluation and promotion of integrated
sorghum - legume technologies
• Led to the formation of two umbrella
community based organisations
Research engagement informed by
climate information
Research engagement informed by
climate information
Previous and ongoing research
engagement in Makueni
Look at the poster on “Making the best of our
climate: Understanding your climate –Kampi ya
Mawe”
Agricultural Model Intercomparison
and Improvement Project (AgMIP)
Adapting smallholder systems to climate
change and variability through a
stakeholder driven, multi-modeling
integrated assessment approach
Climate in Wote is highly variable
How can farmers deal
with the current
climate variability?
TECHNO
LOGY
Below Normal
seasons
Normal seasons Above Normal
seasons
Sorghum
varieties
KARI MTAMA 1 Serena, seredo,
Gadam, KARI
Mtama 1
Serena, Seredo,
KARI Mtama 1,
Gadama
Climate information
services: SR 2011 - 2013
CCAFS IMPACT Lite
Datasets used. Pilot
Site: Wote, Eastern
Kenya
Farm population from 2012 survey
Sample of 200 households from 10X10 km
area
Presented with a choices, would farmers take
them up?
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Farm population w/base
tech & base indicators
(poverty, sustainability)
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Sub-populations:
non-adopters (base tech & indicators)
adopters (improved tech, indicators)


Adoption
Base system: Improved
system:
What is the adoption rate of the technology?
Impact on Returns to farm
Mean Net farm income
increases by 25%
What is the impact on poverty?
OPP COST
Poverty Rate
for Entire
Population (%)
Poverty Rate
for Farmers in
System 1 (Non-
adopters)
Poverty Rate
for Farmers in
System 2
(Adopters)
-532.9163598 27.26289003 27.68907274 5.977902971
-399.6872699 26.16489055 27.69974175 7.747326501
-266.4581799 23.92081335 27.70372998 10.12546346
-133.22909 21.2686647 27.70418861 13.11977145
0 19.93299478 27.72135482 16.44532703
133.22909 20.49491456 27.77817315 19.45574063
266.4581799 21.71983028 27.88967714 21.49560583
399.6872699 22.49526587 28.06229064 22.45714271
532.9163598 22.76270381 28.29672655 22.7575542
Population poverty rate
decreases by 7%
Is this enough incentive for farmers?
System approach including other
elements??
What is feasible in Wote??
ICRISAT is a member of the CGIAR Consortium

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Engagement to help adapt Makueni County farming systems to climate change and variability

  • 1. Engagement to help adapt Makueni County farming systems to climate change and variability AgMIP – 3rd February 2016
  • 2. Guided by Makueni County Integrated Development Plan: 2013-2017 & Kenya vision 2030 County stakeholders include Research institutions whose role is: (i) Research on appropriate farming technologies (ii) Research on viable crop and livestock
  • 3. Ongoing CCAFS engagement • Works with KALRO, ICRISAT, Makueni County Dept of Agric, Livestock & Fisheries, and farmers • Pilot research involving participatory evaluation and promotion of integrated sorghum - legume technologies • Led to the formation of two umbrella community based organisations
  • 4. Research engagement informed by climate information
  • 5. Research engagement informed by climate information
  • 6. Previous and ongoing research engagement in Makueni Look at the poster on “Making the best of our climate: Understanding your climate –Kampi ya Mawe”
  • 7. Agricultural Model Intercomparison and Improvement Project (AgMIP) Adapting smallholder systems to climate change and variability through a stakeholder driven, multi-modeling integrated assessment approach
  • 8. Climate in Wote is highly variable
  • 9. How can farmers deal with the current climate variability? TECHNO LOGY Below Normal seasons Normal seasons Above Normal seasons Sorghum varieties KARI MTAMA 1 Serena, seredo, Gadam, KARI Mtama 1 Serena, Seredo, KARI Mtama 1, Gadama Climate information services: SR 2011 - 2013
  • 10. CCAFS IMPACT Lite Datasets used. Pilot Site: Wote, Eastern Kenya Farm population from 2012 survey Sample of 200 households from 10X10 km area
  • 11. Presented with a choices, would farmers take them up?                        Farm population w/base tech & base indicators (poverty, sustainability)                        Sub-populations: non-adopters (base tech & indicators) adopters (improved tech, indicators)   Adoption
  • 13. What is the adoption rate of the technology?
  • 14. Impact on Returns to farm Mean Net farm income increases by 25%
  • 15. What is the impact on poverty? OPP COST Poverty Rate for Entire Population (%) Poverty Rate for Farmers in System 1 (Non- adopters) Poverty Rate for Farmers in System 2 (Adopters) -532.9163598 27.26289003 27.68907274 5.977902971 -399.6872699 26.16489055 27.69974175 7.747326501 -266.4581799 23.92081335 27.70372998 10.12546346 -133.22909 21.2686647 27.70418861 13.11977145 0 19.93299478 27.72135482 16.44532703 133.22909 20.49491456 27.77817315 19.45574063 266.4581799 21.71983028 27.88967714 21.49560583 399.6872699 22.49526587 28.06229064 22.45714271 532.9163598 22.76270381 28.29672655 22.7575542 Population poverty rate decreases by 7%
  • 16. Is this enough incentive for farmers? System approach including other elements?? What is feasible in Wote??
  • 17. ICRISAT is a member of the CGIAR Consortium