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Innovative use of conventional and new technologies
to unravel breed options for smallholder dairy
production in africa
J....
Africa occupies a large area of the World
West Africa East and Central
Africa
Southern Africa
Countries: Gambia,
Sierra Le...
 Africa hosts 310 million head of cattle
(20.9% of the world cattle population)
 Africa produces 5.4% of the global milk...
Smallholder dairy production systems
• Less than 10 head of cattle
reared
• land sizes less than 0.5 of an
acre to 10 acres
Animals are the products of their genes, their
environments and their gene-environment interactions
P = G + E + GE
P is th...
Policies
Animals are also influenced by
markets, institutions and policies
P = G + E + GE
P is the phenotype The animal we...
What happened? Exotic genotypes were introduced
into harsh production environments
0
1000
2000
3000
4000
5000
Harsh Poor G...
0
1000
2000
3000
4000
5000
Harsh Poor Good
Production environment
Yield(l)
Indigenous
X-bred
Exotic
Improve management a l...
0
1000
2000
3000
4000
5000
Harsh Poor Good
Production environment
Yield(l)
Indigenous
X-bred
Exotic
Good management and di...
 Production systems are mainly
small scale or pastoral,
transaction costs are high
 Climate change!
 Limited resources,...
“You never change things by fighting the
existing reality, to change something, build a
new model that makes the existing ...
Questions of interest in adapting genetic
technologies
1. What genotypes perform well in
smallholder systems
2. What deliv...
A random sample of 2000 animals from 900
small holder farmers were selected from 7
sites in Kenya and Uganda
Selected an...
Results
Breed composition from SNP assays
• Breed types identified in the populations
were
– Exotic breeds: Holstein-Fries...
Principal component analysis results based
on 566k chip
Estimated proportions of exotic dairy breed
alleles from SNP were used to categorize
animals into 5 groups termed “% dairy...
Breed groups derived from SNP analyses- Kenya
Genotype combination for various % dairyness
Breed type >87.5% 61-87.5% 36-6...
Breed groups derived from SNP analyses-
Uganda
Genotype combination for various % dairyness
Breed type >87.5% 61-87.5% 36-...
• Milk yields were generally low, averaging 5.39±3.32
in Kenya and 5.62±3.45 in Uganda, with long
lactations > 400 days
Daily milk production for different dairy groups
of animals within countries
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5
DailyMilky...
Milk production by animals with different proportions of
exotic genotypes (%dairyness)
0
2
4
6
8
10
12
Level-1 Level-2 Lev...
There exists a huge yield gap in production by the same breed
of dairy cattle in the different farming systems
0
5
10
15
2...
 The lower than expected milk yields in the
smallholder farming systems have profound
implications for dairy extension an...
Use of Technologies to effect change in Africa
o Digital platforms for on-farm
performance tracking
o Decision-support and...
Concluding remarks
Genetic improvements have resulted in huge economic
returns: - Meat and Livestock Australia reported fr...
This work is financed by The Bill and Melinda Gates
Foundation, and AU-IBAR
It is implemented in a partnership with UNE, S...
This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
better lives throu...
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Innovative use of conventional and new technologies to unravel breed options for smallholder dairy production in Africa

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Presented by J.M.K. Ojango, R. Mrode and A.M. Okeyo at the 1st World Congress on Innovations for Livestock Development: Fostering Innovations for the Livestock Industry, Nakuru, Kenya, 26–30 June 2016

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Innovative use of conventional and new technologies to unravel breed options for smallholder dairy production in Africa

  1. 1. Innovative use of conventional and new technologies to unravel breed options for smallholder dairy production in africa J.M.K. Ojango, R. Mrode and A.M. Okeyo 1st World Congress on Innovations for Livestock Development: Fostering Innovations for the Livestock Industry 26 – 30 June 2016; Nakuru, Kenya
  2. 2. Africa occupies a large area of the World West Africa East and Central Africa Southern Africa Countries: Gambia, Sierra Leone, Togo, Guinea, Mali, Senegal, Nigeria, Ghana, Niger, Cameroon, Gabon, Ivory Coast, Senegal, Burkina Faso, Guinea Bissau & Central African republic Countries: Kenya, Tanzania, Uganda, Ethiopia, Rwanda, Burundi, DR Congo, Sudan, Somalia, Eritrea & Djibouti Countries: Zambia, Malawi, Mozambique, Zimbabwe, Botswana, Namibia, Angola, Swaziland, Madagascar, Lesotho, Mauritius & South Africa
  3. 3.  Africa hosts 310 million head of cattle (20.9% of the world cattle population)  Africa produces 5.4% of the global milk from cattle (FAOSTAT, 2016)  Up to 80% of the milk produced in Africa is by small-holder farmers Cattle in Africa - 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000 140,000,000 160,000,000 180,000,000 2000 2002 2004 2006 2008 2010 2012 2014 Quantityofmilkproduced(Tons) Year Eastern Africa Southern Africa Western Africa
  4. 4. Smallholder dairy production systems • Less than 10 head of cattle reared • land sizes less than 0.5 of an acre to 10 acres
  5. 5. Animals are the products of their genes, their environments and their gene-environment interactions P = G + E + GE P is the phenotype The animal we see, its production etc. G is the genotype The genetic make up of the animal E is the environment All factors (ambient conditions, health, nutrition, husbandry) except the genes of the animal GE is the interaction Between the genes and the environment
  6. 6. Policies Animals are also influenced by markets, institutions and policies P = G + E + GE P is the phenotype The animal we see, its production etc. G is the genotype The genetic make up of the animal E is the environment All factors (ambient conditions, health, nutrition, husbandry) except the genes of the animal GE is the interaction Between the genes and the environment
  7. 7. What happened? Exotic genotypes were introduced into harsh production environments 0 1000 2000 3000 4000 5000 Harsh Poor Good Milkyield(l) Production environment Indigenous X-bred Exotic
  8. 8. 0 1000 2000 3000 4000 5000 Harsh Poor Good Production environment Yield(l) Indigenous X-bred Exotic Improve management a little and change the genotype
  9. 9. 0 1000 2000 3000 4000 5000 Harsh Poor Good Production environment Yield(l) Indigenous X-bred Exotic Good management and different genotype
  10. 10.  Production systems are mainly small scale or pastoral, transaction costs are high  Climate change!  Limited resources, poverty, available feeds  Endemic diseases  Local Markets, skewed prices  Poor Infrastructure  Lack of feedback systems to inform management decisions  Weak institutions Why is change a challenge in Africa
  11. 11. “You never change things by fighting the existing reality, to change something, build a new model that makes the existing model obsolete” • Buckminister Fuller
  12. 12. Questions of interest in adapting genetic technologies 1. What genotypes perform well in smallholder systems 2. What delivery system(s) would best suit the identified genotype(s) 3. What Partnership(s) would be required to deliver the genotype(s) 4. Is there a business model and plan for delivery – ready to implement
  13. 13. A random sample of 2000 animals from 900 small holder farmers were selected from 7 sites in Kenya and Uganda Selected animals: − Were genotyped using high density SNP technology to determine their breed composition − Their productivity was monitored over 2 years (March 2011 to March 2013) Field and SNP data was combined to determine which breed combinations perform best under different conditions.
  14. 14. Results Breed composition from SNP assays • Breed types identified in the populations were – Exotic breeds: Holstein-Friesian, Ayrshire, Guernsey, Jersey – Indigenous breeds: Zebu, Ankole, Nganda • Animals were highly admixed with exotic breed composition ranging from 0% to 99%
  15. 15. Principal component analysis results based on 566k chip
  16. 16. Estimated proportions of exotic dairy breed alleles from SNP were used to categorize animals into 5 groups termed “% dairyness “; 0-20%, 21-35%, 36-60%, 61-87.5% and >87.5% exotic.
  17. 17. Breed groups derived from SNP analyses- Kenya Genotype combination for various % dairyness Breed type >87.5% 61-87.5% 36-60% 21-35% <20% Ayrshire AAAA, AAA, AAZZ, AZZ, AAZ -- -- *Friesian FFFF, FHHH, HHHF FFF, FHH, FFZ, FF FFZZ, FZ, FZZ, FZZZ -- Guernsey-Jersey GGG GGZ, GG,JJ GGZZ, GZ, JZ, GZZ -- -- Ayrshire-Friesian AAFF, AAHH, AAAF, AAF, AF, AH, FFAZ, AAFZ -- -- -- Ayrshire-Guernsey AAAG, AAGG AG,GGAZ, AAJ - -- -- Friesian-Guernsey GGGF, FFFG, FFGG GGF, FFG, FFJ, FGZ, FFGZ -- -- -- Ayrshire-Friesian- Guernsey AAFG, FAGG, FFAG, FAGJ FAG, FAJ, AAFZ - -- -- Mixed -- MMM MMZZ MZZZ -- Zebu -- -- -- -- ZZZZ, ZZZ
  18. 18. Breed groups derived from SNP analyses- Uganda Genotype combination for various % dairyness Breed type >87.5% 61-87.5% 36-60% 21-35% <20% Friesian FFF FFFZ, FFZ FFZZ, FF FZZZ, FZZ -- Holstein -- HHZ HZZ, HHZZ -- -- Holstein-Friesian HHHF, FFHH, FFFH HHF,FHHZ, FFH FHZZ, FHZ -- -- Zebu -- -- -- ZZZ ZZZZ
  19. 19. • Milk yields were generally low, averaging 5.39±3.32 in Kenya and 5.62±3.45 in Uganda, with long lactations > 400 days
  20. 20. Daily milk production for different dairy groups of animals within countries 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 DailyMilkyield(l/day) Lactation Stage (100-day intervals) Kenya Class 2 Class 3 Class 4 Class 5 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 Dailymilkyield(l/day) Lactation Stage (100-day intervals) Uganda Class-1 Class-2 Class-3 Class-4 Class-5 Lactation curves for animals were generally flat with no evidence of a peak in early lactation
  21. 21. Milk production by animals with different proportions of exotic genotypes (%dairyness) 0 2 4 6 8 10 12 Level-1 Level-2 Level-3 Dailymilkyield(l/day) Herd environment level Kenya 21-35% 36-60% 61-87.5% >87.5% 0 2 4 6 8 10 12 Level-1 Level-2 Level-3 Dailymilkyield(l/day) Herd environment level Uganda 21-35% 36-60% 61-87.5% >87.5% High grade cattle only showed substantially better milk yields than other grades in the highest production environment
  22. 22. There exists a huge yield gap in production by the same breed of dairy cattle in the different farming systems 0 5 10 15 20 25 30 35 40 0 1 2 3 4 5 6 7 8 9 10 Kgofmilkperday Months in Milk Figure 1: Realized lactation curves of improved (crossbred or higher) dairy cows achieved by different farmer types in Kenya Commercial/Intensive dairy farmers – ~6,500 kg/lactation --- ~2% of farmers Best smallholder farmers - ~2,500 kg/lactation --- ~5% of farmers Average smallholder farmers --- ~1,400 kg/lactation --- >90% of farmers The gaps to be filled
  23. 23.  The lower than expected milk yields in the smallholder farming systems have profound implications for dairy extension and development programs and for businesses providing services to these farmers  Given the larger size and maintenance requirements of high grade exotic cattle, lower grade exotics will be the most economically productive animals in the low and medium herd production levels
  24. 24. Use of Technologies to effect change in Africa o Digital platforms for on-farm performance tracking o Decision-support and Farmer-to- Farmer performance benchmarking o Smart use records & genomics tools for selection and better AI service delivery Accelerate on-farm genetic gains o Targeting of appropriate genotypes to the optimum agro- ecology o Use of young bulls with a focus on production & adaptation o Local feed/fodder resource use efficiency Genotype adaptation to local agro-ecology o “Africa needs to create dairy breeds that are best suited to local & emerging ecological conditions Economically Relevant Traits • Milk Yield/density • Adaptability Indices • Reproductive Performance • Heat tolerance • Survival rates • Lactation persistency • Mastitis incidences Development of synthetic breeds
  25. 25. Concluding remarks Genetic improvements have resulted in huge economic returns: - Meat and Livestock Australia reported from 1963-2001, investment in genetic selection and crossbreeding resulted in net gain about $861 million Undergirding these improvements is the accurate evaluation of animals on which selection is based Do we have enabling policies and appropriate policy frameworks in place to allow biotechnology and information technologies to effectively solve Africa’s food scarcity & safety problems?
  26. 26. This work is financed by The Bill and Melinda Gates Foundation, and AU-IBAR It is implemented in a partnership with UNE, SRUC, PicoTeam, Smallholder Farmers in East Africa Acknowledgements
  27. 27. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ilri.org ILRI thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system Thank you

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