ILRI Smallholders Competitiveness Team: 2013-2014 highlights

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Presented by Isabelle Baltenweck at the Livelihoods, Gender, Impact and Innovations Annual Retreat, Nairobi, 21 November 2013


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  • ILRI Smallholders Competitiveness Team: 2013-2014 highlights

    1. 1. Smallholders Competitiveness Team 2013-2014 Highlights Isabelle Baltenweck Livelihoods, Gender, Impact and Innovations Annual Retreat, Nairobi, 21 November 2013
    2. 2. Staff • • • • • • • 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 • • • • TBC, Delhi 1 RT on MoreMilkIt Scientist 1 EADD, Nairobi RT EADD Uganda and Tanzania
    3. 3. Projects • Managed by LGI – – – – – 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 – – – – – 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
    4. 4. 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
    5. 5. Projects Snapshot- EADD
    6. 6. SnapshotMoreMilkIt
    7. 7. Snapshot- Pig VC Uganda
    8. 8. 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

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