Smallholders Competitiveness Team
2013-2014 Highlights
Isabelle Baltenweck
Livelihoods, Gender, Impact and Innovations Annual Retreat,
Nairobi, 21 November 2013

Staff
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Lucy Lapar- Hanoi
James Rao- Nairobi
Emily Ouma- Kampala
Eunice Kariuki- Nairobi
Pamela Ochungo- Nairobi
Emmanuel Kinuthia- Nairobi
Aziz Karamov, Hanoi from 2014, Jan 1
• New staff
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TBC, Delhi
1 RT on MoreMilkIt
Scientist 1 EADD, Nairobi
RT EADD Uganda and Tanzania

Projects
• Managed by LGI
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Value Chain Development Component of L&F
East Africa Dairy Development Project (East Africa)
MoreMilkIt (Tanzania)
REVALTER (Vietnam)
Upcoming pig VC project in Uganda
• Managed by other programs
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Pig Value Chain Project in Uganda (ASSP): Emily
Pig and Food safety in Vietnam: Lucy
Dairy Genetics: James
Humid Tropics: James, (Isabelle)
A4NH: Lucy
• Insufficient links with PIM and PVCT

Research
• We focus on smallholder farmers as key value chain
actors
• With the increased demand for livestock and livestock
products, smallholders have the opportunity to access
these market but they often face several constraints.
• This Team aims at identifying, testing and evaluating
technical, institutional and organizational mechanisms
that would benefit the smallholder farmers.
• We are leading, or contributing to, various research in
development projects, conducting on -farm and
household level research, linked to the ASSP
Programme on the technical side

Projects Snapshot- EADD

SnapshotMoreMilkIt

Snapshot- Pig VC Uganda

Snapshot- Dairy
Genetics
Adoption and Extent of Use of Artificial Insemination Technology – A Comparative
Analysis of Smallholder Dairy Farmer in Kenya and Uganda
Abstract
Despite the envisaged productivity gains from adoption of improved dairy breeds and breeding
technologies, uptake remains low across many African countries. This article undertakes a
comparative analysis of demand for AI technology in Kenya and Uganda. Based on a crosssectional survey of 926 smallholder dairy farmers across the two countries and using analytical
approaches that recognizes the inherent structure of adoption data, we find 78% risk of non-AI
adoption in Uganda compared to 68% in Kenya. We also find higher use rate of AI technology in
Kenya. Adoption and use of AI services in Kenya is uniquely and significantly influenced by
extension services. Infrastructure, institutional limitations and milk marketing also affect
adoption and use of AI in both countries, but in varying and sometimes in contrasting ways, thus
Characterizing smallholder dairy farms in Kenya
providing opportunities for cross-country learning.
Abstract
Classification of farm households is useful in identifying homogenous groups of households for
which targeted development interventions can be recommended. In this article, cluster analysis
was applied on a sample of 696 farm households in order to classify smallholder dairy farmers in
Rift Valley and Western regions of Kenya. Results indicate four groups of smallholder dairy
households, which we have further profiled in order to recommend appropriate actions for
smallholder dairy improvements. Our analysis reveal diversity in breeding constraints and
breeding practice, which calls for diversified institutional innovations for improved access and
utilization of herd upgrading technologies. Diversity in feeding regimes also underscores the
need for diversified feed interventions in addressing feed-based productivity constraints