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Trajectories of change of crop livestock systems in Kenya: engaging stakeholders and modeling. Mario Herrero
 

Trajectories of change of crop livestock systems in Kenya: engaging stakeholders and modeling. Mario Herrero

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A presentation made at the WCCA 2011 event in Brisbane, Australia.

A presentation made at the WCCA 2011 event in Brisbane, Australia.

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    Trajectories of change of crop livestock systems in Kenya: engaging stakeholders and modeling. Mario Herrero Trajectories of change of crop livestock systems in Kenya: engaging stakeholders and modeling. Mario Herrero Presentation Transcript

    • Trajectories of change of crop livestocksystems in Kenya: engaging stakeholders and modeling Mario Herrero WCCA Crop-Livestock Systems Modelling Workshop Brisbane, 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
    • Why are systems changing?Population growth Market changes - - Climate changeLand size change New opportunities in urban areas
    • Where are systems changing? Importance of market access and climatic characteristicsMaize as foodand cash crop Cash crops Cattle in zero- grazing unit
    • 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? Change farm activitiese.g. Reducedland availability Increase inputs Stop farming • Which impacts would this have on farmers’ decisions? • Under what larger storylines is this change likely to occur?
    • 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
    • Aggregated demand Change in demand for export cash crops with limited dairy activitiesRelative 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
    • Site targeting Participatory modellingPolicy-making Ecoregion + spatial modeling • Systems’ classification Farms A B C • Selection of farmsDissemination & • Longitudinal dataimplementation 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)
    • Spatial patterns
    • Spatial patterns over time
    • Spatial patterns over time
    • Spatial patterns over time
    • Spatial patterns over time In-equitableEquitable
    • Intensification/extensification over time In-equitable
    • 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
    • Household model: baseline scenarioFarmers with major dairy, baselinescenario period Observed Optimal 2005-2009 2010-2014 2015-2019 2020-2024 data baseFood crops Maize = maize = maize = maize = maize = maize 0.03 ha 0.03 ha 0.03 ha 0.03 ha 0.03 ha 0.03 haFood/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 haCash 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 haCut and carry 1.93 ha 1.83 ha 1.40 ha 1.12 ha 0.83 ha 0.6 haMilk 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 milkingHired labour 477 34.3 4.9 0 =0 =0 (46.9%)Dependency on 31% food = cut/ cut/ carry cut/ carry cut/ carrypurchased food/ feed carry pasture pasture pasture
    • Contrast: equitable growth scenarioFarmers with major dairy, equitablescenario period Observed Optimal 2005-2009 2010-2014 2015-2019 2020-2024 data base Under equitable scenario of higher growthFood 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 haFood/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 haCash crops - - - - - -Grassland 0.1 ha = 0.1 ha = 0.1 ha = 0.1 ha 0.48 ha 0.93 haCut and carry 1.93 ha 1.83 ha 1.39 ha 1.45 ha 1.48 ha 1.59 haMilk orientation 8 cows: 10 8 cows: = 8 cows: = 8 cows: = 8 cows: 4 milking cows: 4 milking 4 milking 4 milking 4 milking 5 milkingHired labour 477 34.3 83 131 142 125 (46.9%)Dependency on 31% food = cut/ = cut/ carry = cut/ carry = cut/ carrypurchased food/ feed carry pasture pasture pasture pasture
    • 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