Presented by Raphael Mrode, ILRI, at the 7 All Africa Conference on Animal Agriculture (AACAA), Accra, Ghana, 29 July–2 August 2019

1. Lessons from the past: How performance data
availability and quality has led to genetic and
economic gains in different breeds
Raphael Mrode, ILRI
7 All Africa Conference on Animal Agriculture (AACAA), Accra,
Ghana, 29 July–2 August 2019

2. Outline
• Foundational role of data in genetic gain and
economic returns
• Importance of data for sustainable selective
breeding
– Dairy cattle, Poultry , beef cattle
• Obvious trends from the past lessons
• Conclusion
2

3. Foundational role of data in genetic
gains and economic returns
• Genetic improvement programs have delivered huge
economic returns
• In the UK dairy industry benefits of genetic
improvement estimated to be between £2.2 billion and
£2.4 billion from 1980 to 2011.
• Rate of genetical change -typically 1 - 3 % of mean per
annum
• Cumulative from one generation to the other
• Foundational to these improvements are
– Efficient performance data collection and storage systems
– Analytical system for the computation of genetic merit
3

4. 4
Foundational role of Data
• Examine the breeder equations
ΔG = ih2σp / L
• Fundamental parameters that drive ΔG are directly related
to the availability and quality of data.
• For example, estimates of h2 are function of depth and
quality of data.
Estimates of heritability for milk yield in dairy cattle over time
< 1980’s 1991 2000’s
0.25 0.30-0.33 0.35 – 0.50
ANOVA Animal Model Test day models
• Indirect predictions of phenotypes is a major research area
in order to reduce L (Mid-infra red spectrum, pedometers
etc)

5. 5
Importance of data in addressing the
consequences of selective breeding for only
productivity
• Dairy Cattle
• Dominant breed worldwide for milk production is
the Holstein breed.
• Until to 1990’s, the breeding program focused on
selection for milk productivity. Consequences:
– a decline in fitness such fertility over time resulting in
poor concept rate
– increased lameness and reduced longevity.
– On average, cows milked for about 3 lactations (alive for
about 5.5 years).

6. 6
Importance of data in addressing the
challenges in the dairy industry
• In 1999, Lifespan predicted from type information plus direct measure based on
number of parities completed was introduced in PLI
• In 2003, somatic cell count (SCC—indirect measure of mastitis) and lameness( via
locomotion was introduced in PLI
• In 2006, fertility was introduced into PLI
• Notice initial decline but improvements after incorporating data on traits in the
index

7. 7
Importance of data of addressing challenges of
selective breeding for growth rate in the poultry
• Broilers
• Growth rates increased over 300% over 50 years
till the 2000’s
•
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2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Heritabilityovertime
BWT@35days
Aviagen - Genetic Trend BWT (g) - 35d

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1957 Broiler 1977 Broiler 2005 Broiler
Source: Renema et al, 2006

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Importance of data in addressing the
consequences of selective breeding for only
productivity
• Major problems of rapid improvement in body
weight in the poultry were associated with leg
issues
• Sample of 51,000 birds in 176 flocks
– At a mean age of 40 days, over 27.6% of birds in
showed poor locomotion
– 3.3% were almost unable to walk
– The above was after culling birds with severe
lameness
– Toby et al, 2001. Leg Disorders in Broiler Chickens: Prevalence, Risk Factors and Prevention PLoS One.
2008; 3(2): e1545.

10. 10
Broader selection (based on data) in
Poultry- overcoming leg problems
• Broilers:
– Broader Selection index include growth traits and leg
health traits (deformities of the long bones, crooked toes,
tibial dyschondroplasia and hock burn)
• Turkeys
– Growth traits plus gait score as an overall measure of
leg health, footpad dermatitis, and 2 skeletal leg health
traits, namely, valgus and varus deformities and tibial
dyschondroplasia.
– Kapell et al 2012. Poultry Science, 91:3032–3043
– Kapell et al 2017. Poultry Science, 96:1553–1562

11. 11
Independent report Canadian Food
inspection agency on the incidence of
some leg problems

12. 12
FCR Testing in Groups
• Commercial like environment
– Birds housed in groups
– In open pens
– Capture individual intake data
• Allow testing of more birds
• Evaluate feeding behaviour
– Individual & Group
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TotalEaten(g)
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eaten/bird
eaten/meal
meals/bird

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Long-term improvements in Biological
Efficiency
-38grs Feed/year
-0.14% Mortality/year
From http://www.nationalchickencouncil.org/about-the-industry/statistics/u-s-broiler-performance/

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Impact of relevant data enabling BLUP
on Beef value in the UK

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British Cattle Movement Service
• COMMERCIAL animals
• Information
– Dam
– Breed
– Date of birth
– Date of death
– Movement
– Sire (not compulsory)

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Benefits of industry data
• ‘Super-pedigree’
– Most complete pedigree in the UK including all bovine
– BCMS
– Pedigree (beef and dairy)
– Milk recording records
• Super-pedigree to create linkage between
commercial phenotypes, which are often crossbred
and registered pedigree animals

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Super-Pedigree –Evaluate Carcass cuts
• Evaluation of carcass cuts for both purebreds, non-pedigree
and crosses using Video Image analysis (VIA)
– VIA technology allows us to better assess carcass yields for the
valuable primal cuts
• Some registered pedigree are genotyped and therefore
genomic prediction (Single-step)

18. 18
Genomic prediction - huge gain in
accuracy --- Striploin accuracy
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gebv EBV 0-2yr EBV 6yr EBV own
carcass record
EBV 5+
progeny
breedingvalueestiamte
Striploin accuracy
+55% +51% +6% -4%
From Birth
Average age =
9yrs (5-15)

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Obvious trends from the past lessons
• Data is critical for make informed decision
– Identification of the consequences of selective breeding
were identified only by data analysis
– Steps to implement sustainable improvement were
based on acquiring relevant data
• Application of appropriate models to drive genetic
progress is dependent on good quality data
• Design of relevant breeding programs and
feedback to farmers are dependent on data

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Obvious trends from the past lessons
• This is the trend in developed countries, private
companies and Africa is no exception
• So how are we doing?
– Good in collecting DNA samples
– Focus on breed characterization
• Lots of international collaborations focused
– Breed characterization, signatures of selection,
domestication history and gene editing
• These are goods steps but we have often ignored the
step of collecting fundamental data - the basis for
sustainable breed development for our farmers and
countries.

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Obvious trends from the past lessons
• We can not continue in this same trend ; we need a
fundamental change in thinking
• The design any project must include – how can we collect
production data – first priority
• Feasibility of collecting phenotypes demonstrated by
– ADGG/ACCG – uses of digital tool
– CBBP –uses of mobile app
• There are many other less complicated Apps to collect data out
there in the digital world
• Implement a simple animal identification system
• Include a budget for data collection at onset of any project design
and any international collaboration
• Issues? –sustainability? Let us begin first – just walk and we
might be able to learn to run with time!

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Repository of available Data collecting
tools
• Proposition -- is to set a website with information on the
following
– Available, cheap and digital tools for data collection
– Information of rules that govern animal identification and available
tools
– Information on free data base
– Analytical tools and algorithm for data interrogation and analysis
– Genetic software
• Want it up and running by March 2020 and fully functional
by December 2020
• Send information to africadatatools2019@gmail.com

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Conclusion
• Obvious trends on the crucial role of data for
genetic gains and economic returns have been
outlined
• This leads to one conclusion
• Even in the era of genomics
• #PHENOTYPE IS KING!

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Acknowledgements