Trajectories of Change: Farming System Futures in the highlands of

Kenya

Collaborative project of:- International Livestock Research Institute- Kenya Agricultural Research Institute- Wageningen University, The NetherlandsSponsored by:- Ecoregional Methodology Fund (The Netherlands)Data from - Smallholder Dairy Project (SDP), DFID

Trajectories of Change in Crop-Livestock Systems

• Identify patterns of evolution and trajectories of change in crop-ruminant systems, and explain the main driving forces

• Model the relationships between driving factors and change in Kenya and predict systems evolution under key scenarios

• Identify planning and policy interventions that enhance opportunities for, and sustainability of, smallholder producers

Project objectives

• Work with national partners, policy advisors to design questions and analyses

• Develop new analytical methods that combine GIS, survey techniques and models to predict the evolution of crop-livestock systems, and produce various decision-support tools that apply them

Approach

Approach overview

Government publicationsDatabases

Research reports

Household dataSpatial data

Drivers of change

Models

Agricultural System Changes

Maps

ScenariosExperts

Research team

Study area

• 34 districts in Kenya highlands• High potential area with bulk of Kenyan

agriculture• Crop-livestock systems• Influence from external environment

Map of study area

• 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

Scenarios

• The driving forces are:- Human population density- Access to primary education- Use of extension services- Employment off-farm- Road infrastructure- HIV/AIDS- Climate change

• The rate of change vary by district• Using yearly rate of change, we predict change in

the driving forces up to 2024 (from 2004)

Scenario parameters

Four possible development paths

• This study used four possible, but simplistic development paths for agriculture in the Kenyan Highlands over the next 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

Equitable growth scenario

• Key features: mimics the plans laid out in the GoK’s Economic Recovery Strategy (ERS)

• Public investment and functioning institutions for supporting agriculture and education

• Physical and policy barrier to markets reduced, and infrastructure improved, both internally and externally

• Policy environment that stimulate entrepreneurship and innovation in both rural and urban economies

• Result: high economic growth, lower population growth, some land consolidation, improved ag productivity, reduced transactions costs in markets

In-equitable growth scenario

• Key features: Imbalanced investment and public attention, with bias towards high potential areas and larger enterprises

• Poor functioning institutions for supporting smallholder agriculture, with those that function biased towards larger scale players

• Market barriers reduced and infrastructure improved in high potential areas, particularly where large scale production and export markets

• Policy environment that facilitates innovation in large scale rural production

• Result: moderate economic growth, but continued stagnation in some smallholder areas, increased wage employment in large scale production, moderate population growth, and mixed land fragmentation

Equitable scenario with climate change

• Key features: same as Equitable growth scenario, but now add predicted climate change

• Well functioning institutions, market barriers reduced, infrastructure improved, and improved policy environment

• Kenya climate no longer assumed to retain same current patterns: Effects of greenhouse bases, global warming causes climatic change.

• Categorizing and mapping crop-livestock production systems

• Modeling general trajectories of change under different development scenarios developed with stakeholders

• Use household model to test policy interventions, and fine-tune scenario outcomes

Modeling approach

Farming system characterization

Assessment of spatial spread of farming

systems

SDP survey of 3000 households

Household dataSpatial data

Spatial data & estimated coverage of each farming

system

Determinants of farming system choice

Categorizing production systems

Farming systems characterization

• Different ways of classifying agricultural households were tested:– Using factor and cluster analysis (statistical

methods) but results did not prove to represent well farming systems

– Using “experts’ opinion” with a logical “tree” classification

Farming systems characterization

Food crops onlyor cash crops for

Domestic market only Cash crops for export

No or lowExternal inputs

High externalinputs

No/Low dairy

Dairy

No dairy14.1%

Dairy16.9%

Agricultural households

Non-Agriculturalhouseholds

No dairy23.0%

19.0% 13.9%

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

13.1%

Non-agricultural households

Characterization production systems

Farming systems

SDP WMS

Nr. % Nr. % Area(Ha)

Subsistence farmers with no dairy 465 14.1 782 14.3 20.4

Farmers with dairy activities 555 16.9 1,167 21.3 17.3

Intensified farmers with no dairy 753 23.0 995 18.2 24.2

Export-oriented farmers with no dairy

622 19.0 1,089 19.9 27.5

Export-oriented farmers with dairy activities

457 13.9 715 13.1 10.6

Income comparisons between farming systems

Farming systemAverage income

(Ksh/ month)

Subsistence farmers with limited dairy activities 5,000Very poor

Farmers with major dairy activities 7,600Less poor

Intensified crop farmers with limited dairy activities 5,900Poor

Export cash crop farmers with limited dairy activities

6,600Poor

Export cash crop farmers with major dairy activities 7,800Less poor

Households with non agricultural activities 8,200Less poor

Determinants of farming system choice• The driving forces are:

• Human population density• Access to primary education• Use of extension services• Employment off-farm• Road infrastructure• HIV/AIDS• Climate change

• The rate of change vary by district (except for extension)• Using yearly rate of change, we predict change in the driving

forces up to 2024 (from 2004)• The models are then used to predict farming systems change over

time and over space, using the change in the driving forces

Summary of Logit results

Distribution of farming systems

2004

Baseline scenario

Location factorsBiophysical conditions

Off-farmincome

Population growth

Initial land use Scenario conditions

Average Average ur Average

Baseline scenario1 2.4 10.27 0.706

Equitable growth2 1.3 10.27 -0.629

In-equitable growth 1.85 10.27 0.038

Equitable growth 1.3 10.27 -0.629

INPUT

Spatial modeling

2004 2014 2024

OUTPUT

Land use (farming system) change

Spatial modeling

Aggregated demand

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

• Driving factors– Population growth– Income (with commodity specific elasticities)– Exports– HIV/AIDS

26

28

30

32

34

2004 2009 2014 2019 2024

Baseline Equitable Inequitable

Aggregated demand

Rel

ativ

e ch

ang

e

Years

Change in demand for export cash crops with limited dairy activities

Aggregated demand

12

14

16

18

20

2004 2009 2014 2019 2024

Baseline Equitable Inequitable

Change in demand for subsistence farming

Rel

ativ

e ch

ang

e

Years

Change in occurrence of farming systemsScenario

Change Baseline Equitable In-equitable

No change 80.6 74.8 97.01 92.42

Less intensified farming systems 8.9 4.7 22.4 13.4

More intensified farming systems 27.0 34.1 4.2 26.0

New agricultural households 8.1 8.0 11.4 4.6

New non- agricultural households 0.4 0.1 5.3 20.7

1 Zone without large-scale farming2 Zone with large-scale farming

Spatial patterns

2004

Baseline scenario

20042006

Spatial patterns over time

200820102012201420162018202020222024

Baseline scenario

Spatial patterns – areas with change

Baseline scenario

Baseline scenario

Spatial patterns – areas with change

Baseline scenario

Spatial patterns – areas with change

Equitable

In-equitable

Spatial patterns – inequitable

Aggregated change in farming systems

-40

-20

0

20

40

Baseline Equitable Inequitable,no large-

scale farms

Inequitable,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

• Results almost identical to equitable growth scenario

• Climate change impact relative small within considered time frame (20 years)

• Suggests that Kenya highlands not likely to be significantly affected by climate change during this period

Climate change scenario

Poor people per farming system

Subsistence farmers withlimited dairy activities

Farmers with major dairyactivities

Intensified crop farmerswith limited dairy activities

Export cash crop farmerswith limited dairy activities

Export cash crop farmerswith major dairy activities

Non-agriculturalhouseholds

Current situation

Baseline Equitable Inequitable Equitable*

Non-agriculturalhouseholds

Export cash crop farmerswith major dairy activities

Export cash crop farmerswith limited dairy activities

Intensified crop farmerswith limited dairy activities

Farmers with major dairyactivities

Subsistence farmers withlimited dairy activities

Poor people per farming systemP

erce

nta

ge

of

rura

l p

oo

r

Farming system

Baseline Equitable0

100

50

In-equitable Equitable*

Change in number of poor people

Baseline

Equitable In-equitable

Household model

Observed data OptimalBase

Period2005-2009

Period2010-2014

Period2015-2020

Period2020-2024

Food crops Maize0.03 ha

↓ Maize0.02 ha

= Maize0.02 ha

= Maize0.02 ha

= Maize0.02 ha

= Maize0.02 ha

Food/cash crops Maize, Beans0.4 ha

↑ Maize, beans

0.55 ha

↓Maize, beans

0.26 ha

↓ Maize, beans

0.16 ha

↓ Maize, beans

0.07 ha

↓ Maize, beans0.03 ha

Cash crops - - - - - -

Grassland 0.1 ha =0.1 ha ↓ 0.08 ha ↓ 0.07 ha ↓ 0.06 ha ↓ 0.05 ha

Cut and carry 1.93ha ↓↓ 1.79 ha ↓ 1.65 ha ↓ 1.40 ha ↓ 1.21 ha ↓ 1.02 ha

Milk Orientation 8 Cows:4 Milking

↑9 Cows:4.5 Milking

↓ 8 Cows:4 Milking

↓ 7 Cows:3.5 Milking

↓6 Cows:3 Milking

↓5 Cows:2.5 Milking

Hired labour 477(100%) ↓41.2 ↓4.7 ↓0% 0 0

Dependency on purchased food/feed

31% food ↑Cut/carry pasture

↓ Cut/carry pasture

↓ Cut/carry pasture

↓ cut/carry pasture

↓ cut/carry pasture

Land allocated (ha) 2.46 2.46 2.01 1.65 1.36 1.13

Results household modelFarmers with major dairy, baseline scenario

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

Results household model

Observed data

Optimal Base

Period 2005-2009

Period 2010-2014

Period 2015-2020

Period 2020-2024

Food crops Maize0.03 ha

↓ Maize0.02 ha

↑ Maize0.08 ha

↑ Maize0.08 ha

↓↓ Maize0.0 ha

↓↓ Maize0.0 ha

Food/cash crops Maize, Beans0.4 ha

↑ Maize, beans0.55 ha

↑ Maize, beans2.1 ha

↑ Maize, beans

2.53 ha

↑ Maize, beans

2.65 ha

↓ Maize, beans2.62 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 1.93ha ↓↓ 1.79 ha ↓↓ 0.52 ha ↓↓ 0.52 ha ↑ 0.96 ha ↑ 1.55 ha

Milk Orientation 8 Cows:4 Milking

↑9 Cows:4.5 Milking

↓↓ 3 Cows:1.5 Milking

↓↓ 3 Cows:1.5 Milking

↑ 5 Cows:2.5 Milking

↑ 8 Cows:4 Milking

Hired labour 477(100%) ↓41.2 ↑ 57.6 ↑ 117.5 ↑ 191.2 ↑ 304.8

Dependency on purchased food/feed

31% food ↑Cut/carry pasture

↓↓ Cut/carry pasture

= Cut/carry pasture

↑ Cut/carry pasture

↑↑ Cut/carry pasture

Land allocated (ha) 2.46 2.46 2.81 3.53 3.71 4.27

Farmers with major dairy, equitable scenario

Under equitable scenario of higher growthand land consolidation, grass for dairy in this example farm increases between 2005 and 2024

• 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.

Summary

• 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

• Future plan – apply the same approach to data from Western India (Gujarat). Relevant for agricultural change research in a number of settings.

Summary

How to enhance institutionalization of ecoregional research?

Survey of recent Ecoregional projects found:– Engage with policy-makers & drafters, and

other users early and frequently– Address real problems of interest– Develop user-friendly materials, including user

manuals, web materials, policy briefs– Promote capacity building with partners,

including formal training and short courses– Seek additional resources for continued

development and use

Engage with policy-makers

• Various meeting

7 August 2003 - Panel of experts

25 February 2004 - Stakeholders

23 November 2004 - Local experts

29 April 2005 - Panel of experts

22 June 2005 – Stakeholders

• To address real problems of interest – link to ERS

Documentation of Ecoregional tools

Peer-reviewed report published in theIGBP/IHDP LUCC report series (no. 7)

Review report of statistical methods for eco-regional research

User’s manuals of IMPACT and CLUE

www.trajectories.org

Documentation of Ecoregional tools

Promote capacity building with partners

• The TOC project organised a 5-day training June 13-17 to familiarise 6 “trainees” with the approach on farming system change and the two modelling approaches used within the project

• During the last day, the trainees brainstorm on possible future projects. Three concept notes are currently under development, to be finalized in workshop end Sept.

Promote capacity building with partners

Mosoti Andama Institute of Policy analysis and research

Stanley Omucheni University of Nairobi, Survey Department

Dr. Peter Kamoni Kenya Agricultural Research Institute

Dr. Margaret Ngigi Egerton University

Isaac Komo TEGEMEO institute

Stanley Kagera Ministry of Planning and National Development

Course participants:

Acknowledgements

Alberto BernuesCarlos QuirosEdmund GithoroErnesto Gonzalez-EstradaIsabelle BaltenweckJeannette van de SteegMario HerreroMichael WaithakaNienke Schulp

Nol WittePamela OchungoPeter Ngigi NdunguPeter VerburgPhilip ThorntonStanley KaranjaStella MakokhaSteve StaalTom Onyango

Thank you