Smallholders Competitiveness Team2013-2014 Highlights

Isabelle Baltenweck Livelihoods, Gender, Impact and Innovations Annual Retreat,

Nairobi, 21 November 2013

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

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

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

Snapshot- MoreMilkIt

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 cross-sectional 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 providing opportunities for cross-country learning. Characterizing smallholder dairy farms in Kenya

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