Trajectories of change of crop livestock systems in Kenya:

engaging stakeholders and modeling

Mario Herrero

WCCA Crop-Livestock Systems Modelling WorkshopBrisbane, Australia Sept 2011

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

- -

Climate change

Population growth

Land size change

Market changes

New opportunities in urban areas

Why are systems changing?

Importance of market access and climatic characteristics

Cash crops

Maize as food and cash crop

Cattle in zero- grazing unit

Where are systems changing?

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.

What can a scenario tell us?

• Which impacts would this have on farmers’ decisions?

• Under what larger storylines is this change likely to occur?

e.g. Reduced land availability

Change farm activities

Increase inputs

Stop farming

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

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

Demand

• Change in demand for commodities– Maize– Beans– Tea– Milk

• Driving factors– Population growth– Income (with commodity specific elasticities)– Export

26.0

26.1

26.2

26.3

26.4

26.5

26.6

26.7

26.8

26.9

27.0

2004 2009 2014 2019 2024

Baseline Equitable In-equitable

Rel

ativ

e ch

ang

e

Years

Aggregated demand

Change in demand for export cash crops with limited dairy activities

• Longitudinal data• Participatory

methods• Key informants

• Systems’ classification

• Selection of farms

• IMPACT & Household model

• Sensitivity analyses

• Participatory appraisals• Recommendation

domains• Toolboxes of

interventions• Farmers / NARS

• Stakeholder workshops• Participatory

appraisals

Participatory modelling

Ecoregion + spatial modeling

Farms

CBA

Case studies

Range of interventions to test

for each system (filtering)

Scenario formulation(Farm and policy

level)

Selection of a fewer range of

options

Site targeting

(Herrero, 1999)

Dissemination &

implementation

Policy-making

Testing options in the field

Spatial patterns

Spatial patterns over time

Spatial patterns over time

Spatial patterns over time

Spatial patterns over time

Equitable

In-equitable

Intensification/extensification over time

In-equitable

Aggregated change in farming systems

-40

-20

0

20

40

60

Baseline Equitable In-equitable,no large

scale farms

In-equitable,large scale

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

Household model: baseline scenario

period

Observed data

Optimal base

2005-2009

2010-2014 2015-2019 2020-2024

Food crops Maize0.03 ha

= maize0.03 ha

= maize0.03 ha

= maize0.03 ha

= maize0.03 ha

= maize0.03 ha

Food/cash crops Maize, beans0.4 ha

Maize, beans0.5 ha

Maize, beans

0.48 ha

Maize, beans0.4 ha

= Maize, beans0.4 ha

= Maize, beans0.4 ha

Cash crops - - - - - -

Grassland 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: 4 milking

10 cows: 5 milking

8 cows: 4 milking

7 cows: 3.5 milking

5 cows: 2.5 milking

4 cows: 2 milking

Hired labour 477 (46.9%)

34.3 4.9 0 = 0 = 0

Dependency on purchased food/ feed

31% food = cut/ carry

pasture

cut/ carry pasture

cut/ carry pasture

cut/ carry pasture

Under baseline scenario of low growth,dairy activity in this example farm declinesbetween 2005 and 2024

Farmers with major dairy, baseline scenario

Contrast: equitable growth scenario

period

Observed data

Optimal base

2005-2009

2010-2014 2015-2019 2020-2024

Food crops Maize0.03 ha

= maize0.03 ha

= maize0.03 ha

= maize0.03 ha

= maize0.03 ha

= maize0.03 ha

Food/cash crops Maize, beans0.4 ha

Maize, beans0.5 ha

Maize, beans1.3 ha

Maize, beans

1.65 ha

Maize, beans

1.73 ha

= Maize, beans

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: 4 milking

10 cows: 5 milking

8 cows: 4 milking

= 8 cows: 4 milking

= 8 cows: 4 milking

= 8 cows: 4 milking

Hired labour 477 (46.9%)

34.3 83 131 142 125

Dependency on purchased food/ feed

31% food = cut/ carry pasture

= cut/ carry pasture

= cut/ carry pasture

= cut/ carry pasture

Farmers with major dairy, equitable scenario

Under equitable scenario of higher growthand land consolidation, pasture and grass fordairy in this example farm increasesbetween 2005 and 2024

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.

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

Thank you