trajectories of change: farming system futures in the highlands of kenya
TRANSCRIPT
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