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

Ok...
Principal Funding.

Bill and Melinda Gates Foundation

Supporting Agencies

AusAID Endeavour
Illumina
Geneseek
CGIAR CRP 3...
Dairy Genetics East Africa
Dairy Genetics East Africa
The Problem
• Smallholder dairy has dramatically improved
livelihoods of >5m smallholder farmer...
Dairy Genetics East Africa
WHAT?
• What is the optimum breed composition for
smallholder dairy farmers in different produc...
What is the optimum breed composition?
Traditional approach
• Breed cattle of range of genotypes
• Rear to production age
...
What is the optimum breed composition?
New approach
• Work with crossbred cattle owned by smallholders
• Record in situ
• ...
Which genotype(s) performs best?
• 7 sites in Kenya + Uganda
• working with random sample of 900 farmers and
2000 cows
• m...
PC1 vs PC2 DGEA all data + Hapmap; 566k snp
Africa Bos taurus
reference breed

Crossbred cows
Local indigenous
breeds

Bos...
Key results
• Dairy composition of individual animals can be accurately
estimated.
• Much wider variation in breed proport...
Implications
• Low yields and wides variation in breed composition mean
that the value of breeding optimisation is higher ...
How can appropriate germplasm be
delivered to smallholder farmers?
• An innovation platform approach jointly across
Kenya ...
Outcomes: Key findings
(what we did not know that has changed our
approach)
1.
2.

3.

4.
5.

Heifer rearing and distribut...
Example outcomes: emerging businesses
1.

Private semen production and delivery:
two new businesses

2.

Heifer production...
Example outcomes: needs with no business
model (yet)
4.

5.

Bundled services to smallholders: single
businesses (vet, AI,...
Where to from here
1.
2.
3.
4.

Expand research and intervention development to
anchor countries, Ethiopia and Tanzania
Re...
Genetic
research
for a better
future
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Overview of the Dairy Genetics East Africa (DGEA) project

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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

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Overview of the Dairy Genetics East Africa (DGEA) project

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

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