Presented by John P. Gibson, Ed Rege, Okeyo Mwai, Julie Ojango at the Dairy Genetics East Africa (DGEA) Project 2013 Grand Challenges Meeting, Rio de Janeiro, Brazil, 28-30 October 2013

1. Overview of Dairy Genetics East Africa
(DGEA)
John P. Gibson (on behalf of the Dairy Genetics
East Africa project team)

2. The Partners
John Gibson, Cedric Gondro, Gilbert
Jeyaruban, Shalanee Weerasinghe
Ed Rege, Robert Ouma, Jurgen Hagmann
Okeyo Mwai, Isabelle Baltenweck, Denis
Mujibi, James Rao, Julie Ojango, et al.

3. Principal Funding.
Bill and Melinda Gates Foundation
Supporting Agencies
AusAID Endeavour
Illumina
Geneseek
CGIAR CRP 3.7 (ILRI)
Sheep Cooperative Research Centre
The Centre for Genetic Analysis and Application
(UNE)

4. Dairy Genetics East Africa

5. Dairy Genetics East Africa
The Problem
• Smallholder dairy has dramatically improved
livelihoods of >5m smallholder farmers in the
region and continues to expand
• There is no genetic strategy
• Only inflow of genetics is high yield exotics
(Holstein Ferraris on dirt roads)
• AI reaches only a small proportion of
smallholders
• Reports of problems in heifer supply

6. Dairy Genetics East Africa
WHAT?
• What is the optimum breed composition for
smallholder dairy farmers in different production
environments?
HOW?
• How can the most appropriate genotypes be
delivered to smallholder farmers? (And then facilitate
the development of sustainable delivery businesses).

7. What is the optimum breed composition?
Traditional approach
• Breed cattle of range of genotypes
• Rear to production age
• Record for several lactations (usually in research
or other controlled herds)
Time to answer: high
Cost: high
Accuracy: low
Risk of not completing: high
Relevance of results: often questionable

8. What is the optimum breed composition?
New approach
• Work with crossbred cattle owned by smallholders
• Record in situ
• Use snp to determine breed composition
Time to answer: low
Cost: moderate to high
Accuracy: higher
Risk of not completing: low
Relevance of results: high

9. Which genotype(s) performs best?
• 7 sites in Kenya + Uganda
• working with random sample of 900 farmers and
2000 cows
• milk yields, health and reproduction events + farmlevel information
• crossbred and indigenous cows genotyped with 770k
Illumina snp assay
Then estimate breed proportions and determine which
breed proportions work best in which environments

10. PC1 vs PC2 DGEA all data + Hapmap; 566k snp
Africa Bos taurus
reference breed
Crossbred cows
Local indigenous
breeds
Bos indicus reference breed
European dairy
breeds

11. Key results
• Dairy composition of individual animals can be accurately
estimated.
• Much wider variation in breed proportions than expected
• Farmer prediction of breed composition, R2 only 0.16. (i.e.
What you see is not what you get.)
• Average yields approx 5kg/day (less than 1500kg/annum):
much lower than generally expected.
• >50% farmers have access to AI
• 10% of breedings are by AI

12. Implications
• Low yields and wides variation in breed composition mean
that the value of breeding optimisation is higher than
originally thought.
• Local bulls are likely to be much more variable than thought
(selection for indigenous traits in a dairy shell?)
• Need to understand and target bull genotypes
• While access to AI is a problem, poor service quality and
lack of needed (crossbred) genotypes is a bigger problem.

13. How can appropriate germplasm be
delivered to smallholder farmers?
• An innovation platform approach jointly across
Kenya + Uganda
• Facilitating key actors to identify and then find
sustainable solutions to germplasm supply

14. Outcomes: Key findings
(what we did not know that has changed our
approach)
1.
2.
3.
4.
5.
Heifer rearing and distribution is a major problem
Regional demand is creating major market distortions in
heifer supply
“Large P policy” was thought a major obstacle but in
depth analysis revealed it was not. (This emboldened
new business to move ahead)
Sustainable financing options for businesses, large and
small, are limited.
Investor conference showed that financial organisations
are keen to find win-win solutions

15. Example outcomes: emerging businesses
1.
Private semen production and delivery:
two new businesses
2.
Heifer production and delivery:
a)
b)
c)
d)
e)
3.
Broker/supplier models (already operational)
‘Wombs for hire’
Large scale operations, incl. SIFET
Calf nursery models
Small scale, service oriented production models
ICT-based platforms for linking
germplasm demand and supply:

16. Example outcomes: needs with no business
model (yet)
4.
5.
Bundled services to smallholders: single
businesses (vet, AI, feed etc) not profitable on their own.
One-stop shop approach to farmer services appears
profitable. Possible models include franchise,
cooperative, dairy chilling hubs (farmer owned).
Rwanda: socio-economic-political & farming system
completely different to Kenya and Uganda. Will require
completely different solutions

17. Where to from here
1.
2.
3.
4.
Expand research and intervention development to
anchor countries, Ethiopia and Tanzania
Results on appropriate breeds from current DGEA
are not transferable to major systems in Tanzania
& Ethiopia (different environments and systems).
Baseline farm surveys and genotyping in Tanzania
and Ethiopia to determine best-bet interventions +
missing R&D needs.
Innovation platform approach being tested in each
country to assess levels of investment (time,
process, funding) required to drive sustainable
germplasm delivery.

18. Genetic
research
for a better
future