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Overview of Dairy Genetics East Africa
(DGEA)
John P. Gibson (on behalf of the Dairy Genetics
East Africa project team)
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.
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)
Dairy Genetics East Africa
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
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).
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
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
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
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
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
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.
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
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
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:
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
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.
Genetic
research
for a better
future

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Overview of the Dairy Genetics East Africa (DGEA) project

  • 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)
  • 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.