This document summarizes a workshop presentation on modeling trajectories of change in crop-livestock systems in Kenya. The presentation engaged stakeholders and used various models to understand drivers of change in Kenyan livestock systems and potential choices for producers. Four scenarios were developed: a baseline scenario projecting continuation of past trends, and equitable growth, inequitable growth, and equitable growth with climate change scenarios. Modeling results indicated a likely shift away from subsistence farming toward more intensive food and dairy production under all scenarios except inequitable growth in less favorable areas. The process of developing participatory scenarios and models was found to be very useful for stimulating discussion among stakeholders.

1. Trajectories of change of crop livestock
systems in Kenya: engaging stakeholders
and modeling
Mario Herrero
WCCA Crop-Livestock Systems Modelling Workshop
Brisbane, Australia Sept 2011

2. Background
• Understand drivers of why and where are livestock
systems changing in Kenya and what are the choices
for producers
• Used a range of models
– Land use models (CLUE)
– Spatial econometrics
– Household models
– Livestock and crop models
– Climate change models
• Substantial stakeholder consultation with policy makers
and local institutions
• ILRI, Kenyan Agricultural Research Institute, Ministries
of Agriculture and Livestock, Wageningen University
• From 2001-2006

3. Why are systems changing?
Population growth Market changes
- -
Climate change
Land size change
New opportunities
in urban areas

4. Where are systems changing?
Importance of market
access and climatic
characteristics
Maize as food
and cash crop Cash crops
Cattle in zero-
grazing unit

5. Scenarios: storylines for
potential development paths
• Each scenario is an alternative image of how the
future might unfold.
• Scenarios can be viewed as a linking tool that
integrates
– qualitative narratives about future development pathways
and
– quantitative formulations based on formal modelling, and
available data
• Scenarios can enhance our understanding of how
systems work, behave and evolve, and so can help in
the assessment of future developments.

6. What can a scenario tell us?
Change farm activities
e.g. Reduced
land availability Increase inputs
Stop farming
• Which impacts would this have on farmers’
decisions?
• Under what larger storylines is this change
likely to occur?

7. Four possible development
paths
• This presentation presents four possible but
simplistic development paths for agriculture in
the Kenyan Highlands over the next 15 to 20
years:
• Baseline scenario
• Equitable growth scenario (ERS)
• In-equitable growth scenario
• Equitable growth scenario with climate change

8. Baseline scenario
• Key features: continuation of development
pathways seen in Kenya in 1980s and 90s
• Poorly functioning public institutions for supporting agriculture,
education and market development
• Market barriers internally and externally, and poor market infrastructure
• Policy environment that stifles enterprise and innovation in both rural
and urban economies
• Result: poor economic growth, continued urban-rural migration, little ag
productivity growth, continued high population growth and land
fragmentation

9. Demand
• Change in demand for commodities
– Maize
– Beans
– Tea
– Milk
• Driving factors
– Population growth
– Income (with commodity specific elasticities)
– Export

10. Aggregated demand
Change in demand for export cash crops with limited dairy activities
Relative change
27.0
26.9
26.8
26.7
26.6
26.5
26.4
26.3
26.2
26.1
26.0
2004 2009 2014 2019 2024
Baseline Equitable In-equitable
Years

11. Site targeting Participatory modelling
Policy-making Ecoregion + spatial modeling
• Systems’ classification
Farms
A B C • Selection of farms
Dissemination & • Longitudinal data
implementation Case studies • Participatory methods
• Key informants
Range of interventions to • Participatory appraisals
test for each system • Recommendation domains
(filtering) • Toolboxes of interventions
• Farmers / NARS
Testing Scenario formulation • IMPACT & Household
options in the (Farm and policy level) model
field • Sensitivity analyses
Selection of a fewer • Stakeholder workshops
range of options • Participatory appraisals
(Herrero, 1999)

12. Spatial patterns

13. Spatial patterns over time

14. Spatial patterns over time

15. Spatial patterns over time

16. Spatial patterns over time
In-equitable
Equitable

17. Intensification/extensification over time
In-equitable

18. Aggregated change in farming systems
60
40
20
0
Baseline Equitable In-equitable, In-equitable,
-20
no large large scale
-40 scale farms farms
Subsistence farmers with limited dairy activities
Farmers with major dairy activities
Intensified crop farmers with limited dairy activities
Export cash crop farmers with limited dairy activities
Export cash crop farmers with major dairy activities
Non-agricultural households

19. Household model: baseline scenario
Farmers with major dairy, baseline
scenario
period
Observed Optimal 2005-2009 2010-2014 2015-2019 2020-2024
data base
Food crops Maize = maize = maize = maize = maize = maize
0.03 ha 0.03 ha 0.03 ha 0.03 ha 0.03 ha 0.03 ha
Food/cash crops Maize, Maize, Maize, = Maize, = Maize,
beans beans Maize, be beans beans beans
0.4 ha 0.5 ha ans 0.4 ha 0.4 ha 0.4 ha
Under baseline scenario of low growth,
0.48 ha
Cash crops dairy activity in -this example farm declines
- - - - -
Grassland
between 2005 and 2024
0.1 ha = = = = =
0.1 ha 0.1 ha 0.1 ha 0.1 ha 0.1 ha
Cut and carry 1.93 ha
1.83 ha 1.40 ha 1.12 ha 0.83 ha 0.6 ha
Milk orientation 8 cows: 10 8 cows: 7 cows: 5 cows: 4 cows:
4 milking cows: 4 milking 3.5 milking 2.5 milking 2 milking
5 milking
Hired labour 477 34.3 4.9 0 =0 =0
(46.9%)
Dependency on 31% food = cut/ cut/ carry cut/ carry cut/ carry
purchased food/ feed carry pasture pasture pasture

20. Contrast: equitable growth scenario
Farmers with major dairy, equitable
scenario
period
Observed Optimal 2005-2009 2010-2014 2015-2019 2020-2024
data base
Under equitable scenario of higher growth
Food crops Maize = maize = maize = maize = maize = maize
and landha
0.03 ha 0.03 consolidation, pasture and grass for
0.03 ha 0.03 ha 0.03 ha 0.03 ha
Food/cash crops dairy in this example farm increases Maize,
Maize, Maize, Maize, Maize, Maize, =
beans beans beans beans beans beans
betweenha
0.4 ha 0.5 2005 1.3 ha 2024ha
and 1.65 1.73 ha 1.73 ha
Cash crops - - - - - -
Grassland 0.1 ha = 0.1 ha = 0.1 ha = 0.1 ha 0.48 ha 0.93 ha
Cut and carry 1.93 ha 1.83 ha 1.39 ha 1.45 ha 1.48 ha 1.59 ha
Milk orientation 8 cows: 10 8 cows: = 8 cows: = 8 cows: = 8 cows:
4 milking cows: 4 milking 4 milking 4 milking 4 milking
5 milking
Hired labour 477 34.3 83 131 142 125
(46.9%)
Dependency on 31% food = cut/ = cut/ carry = cut/ carry = cut/ carry
purchased food/ feed carry pasture pasture pasture
pasture

21. Summary of results
• Subsistence farming is likely to decrease in Kenya, even under
the less optimistic baseline scenario, shift to more intensive
food crops and dairy production
• In all scenarios there is likely to be a shift away from farming to
non-agricultural households.
• Only increase in subsistence farming could occur in inequitable
scenario, in the less favoured areas.
• Unlike perhaps other parts of Kenya, the highlands of Kenya
may not be significantly impacted by climate change.
• These results are only indicative of potential changes under
rather simplistic scenarios, and so should not be seen as
definitive.
• Their main purpose is to stimulate interest and further
development in these types of analytical methods by national
institutions.

22. Lessons learnt
• Discussion tools
• Time-consuming
• Process more important than models
• Policy steering group: Significant interest from policy makers
• Useful to show results along the process, even if partial, not at
the end
• Socio-economic impacts as, or more, important than the bio-
physical ones

23. Thank you