This document discusses challenges and opportunities for adoption of modern breeding tools in developing countries. It covers several topics: human expertise and training needs, infrastructure requirements, access to information and technology, data management challenges, availability of analytical tools and support services, importance of effective partnerships, and changing mindsets. Supporting adoption will require a coordinated, multi-pronged approach including training, infrastructure investments, improved data management practices, access to analytical tools and genotyping services, and strong support networks.

1. Jean-Marcel Ribaut
4th International Workshop on Next Generation Genomics
and Integrated Breeding for Crop Improvement
February 21, 2014
ICRISAT, Patancheru, India
Adoption of modern breeding tools in
developing countries:
Challenges and Opportunities

2. Our Discussion Today:
♦ The human expertise
♦ Infrastructure
♦ Access to information and technology
♦ Data Management
♦ Labs and genomic resources
♦ Analytical tools and pipeline
♦ Support tools
♦ Social network
♦ Mindset and change in behavior
♦ Importance of support services
♦ Effective partnership
♦ Conclusion and perspectives

3. The Human Expertise

4. ♦ Integrated in research projects
♦ Formal training postgraduate programmes
♦ CB à la carte
♦ Training on technologies and related tools
♦ Academic/theory (curriculum, e-learning)
♦ Applications: How to use the tools
♦ Whom to train?
♦ Scientists (balance in expertise and experience)
♦ Training of technicians (translation of the tools)
♦ Training of the station managers (essential!)
♦ Awareness creation programme
♦ Focused workshop for a small group of breeders, plus
international conferences and regional meetings
 Training of future trainers in the regions
How to organise CB activities

5. YEAR 1
MB project Initiation
Intro to some MB approaches
Field data analysis
Mgnt of breeding data
Field trial Mgnt system
Trainees community
YEAR 2
Updates on tools of year1
Adv molecular analysis
Genotypic data Mgnt system
Marker–trait associations
Tools for MTA
Trainees community
YEAR 3
Updates on tools of year1&2
Configurable workflow
Genetic diversity analysis
Association mapping
Partner specific projects
Trainees community
Eastern & Southern Africa – West and Central Africa – South & Southeast Asia
WUR team, DM CoP &
other technical trainers
Specific training:
Local level
Specific Language
Different level:
Technicians
Integrated Breeding Multiyear Course
(IB–MYC)
ESA WCA SSEA

6. It is required that trainees successfully complete the following assignments:
♦ Data management: The trainees must be able to properly identify and
curate their own germplasm in the database, generate a fieldbook, save
the phenotyping data to the database and use the tablet for data
collection (field or lab)
♦ Statistics: The trainees choose a completed experiment for which they
have data, and write a report including details such as the design used,
physical layout, type of analysis applied and the conclusions
♦ Molecular breeding: The trainees write a brief plan of their project. If the
work is in progress, they must include what steps have been completed
and the planned next steps. Use the IBP tools to carry out their MB
projects
Trainee assignment to stay on board

7. IB–MYC trainees by region & gender
Region Country Female Male Total
WCA 9 8 46 54
WCA % 14.8 85.2
ESA 10 11 51 62
ESA % 17.7 82.3
NA 3 4 1 5
NA % 80 20
SSEA 9 14 35 49
SSEA
%
28.6 71.4
Total 31 37 133 170
Total % 21.8 78.2
Region Country Female Male Total
WCA 8 8 41 49
WCA % 16.3 83.7
ESA 8 9 38 47
ESA % 19.1 80.9
NA 3 4 1 5
NA % 80 20
SSEA 7 13 32 45
NA % 28.9 71.1
Total 26 34 112 146
Total % 23.3 76.7
Year 1 Year 2
ESA = Eastern and Southern Africa; NA = Northern Africa
SSEA = South and Southeast Asia; WCA = West and Central Africa

8. Dissemination of CB knowledge
♦ Decentralized effort, the only affordable and sustainable approach
♦ Develop a self-contained manual and tutorials for the tools
♦ Should the tutorial be embedded in the tools? Each step linked to video,
related e-learning material, quiz, study cases
♦ Provide adequate learning material that will serve several purposes:
♦ Working with e-learning specialists to format the workshop support material
in a learning format (PBTN – UNL http://passel.unl.edu/communities/ibp)
♦ Create ready-for-use modules with related training material and quiz,
customisable depending on the audience
♦ Identify partner(s) for dissemination in the target regions (Universities)
♦ Train future trainers
♦ Centralised support service (troubleshooting)
♦ Social network (question–answer, advise forum)

9. Infrastructure

10. ♦ Field infrastructure
♦ A must-have
♦ No fancy equipment needed, but very good baseline
♦ Providing the money is not sufficient (critical needs assessment and
follow-up)
♦ Access to phenomics facilities?
♦ IT support
Critical but less of an issue nowadays
♦ Increasing performance of personal computers
♦ Cheap and transportable UPS
♦ Move to cloud computing
♦ Cell phone technology
♦ Access to good internet connection (at least every few days)
♦ Laboratories: not a limitation anymore
Critical for implementation

11. Field support issues
Goal: Ensure proper and reliable phenotypic data
♦ Several NARS sites did/do not have optimal field station
management practices and support personnel to ensure proper
use and maintenance of their equipment and field plots
♦ Field areas invaded by weeds and shrubs (even small trees!)
♦ No field rotation schedule
♦ Poor drainage of access roads, or lack of water channel
maintenance, leads to field erosion and lack of plot uniformity
♦ Field equipment (tractors, ploughs, etc.) stored in open-area ‘junk
yards’ with little care, maintenance, calibration, etc.
♦ Encroachment by neighbouring farmers on station grounds
♦ For the most deficient cases, we made our infrastructure
investments contingent on assurance from the station manager
that proper field support systems would be established

12. Field Infrastructure Improvements in Africa
(Support from 2010–2011)
Burkina Faso
INERA Farako-ba Rice Weather station $5,000
Banfora Rice Weather station, irrigation, fencing, plot rehab $91,000
Ghana
SARI Tamale Cassava Weather station, irrigation, fencing, plot rehab $104,000
CSIR-CRI Kumasi Cassava Weather station $5,000
Pokuase Cassava Weather station $5,000
Kenya
Egerton University Main campus Chickpea Rain-out shelter $8,000
Koibatek (FTC) Chickpea Weather station, irrigation, fencing, plot rehab $47,000
Kerio Valley Chickpea Weather station $5,000
Moi University Chepkoilel Maize, sorghum Weather station, irrigation, fencing, greenhouse $48,000
Sega Maize, sorghum Weather station, irrigation, fencing $25,000
Mali
IER Sotuba Sorghum Weather station, irrigation, fencing, plot rehab $84,000
Cinzana Sorghum Weather station, irrigation, plot rehab $25,000
Farako Sorghum Weather station $5,000
Longorola Rice Weather station, irrigation, fencing, plot rehab $106,000
Nigeria
IAR Kano Cassava Weather station, irrigation, fencing, plot rehab $70,000
NRCRI Umudike Cassava Weather station $5,000
NCRI Badeggi Rice Weather station, irrigation, fencing, plot rehab $129,000
Tanzania
ARI-Naliendele Naliendele Groundnut, cassava Weather station, irrigation, fencing, plot rehab $124,000
Other espenses
Engineering support $220,000
Shipment, admin fees $63,000
Training courses $160,000
TOTAL $1,334,000

15. Access to information and
technology

16. Challenges:
♦ Most of the breeders in the developing world capture their data by hand
and store them in hard copy (book)
♦ In general, protective and proprietary attitude prevents data sharing
♦ Not a top priority, no clear resources allocation, data still in the hands of
the individual scientists
DM: One of the major challenges in collaborative effort
Implementation:
♦ Clear DM policy in place at the Institution level
♦ Quality and documentation improved thanks to :
♦ Adoption of new tools with predefined data-capture templates (eg,
electronic FB)
♦ Suitable API to ensure systems and DB interoperability
♦ Proper budget allocation including support staff
♦ Part of the staff evaluation process
♦ Donor requirement beforehand
Quality control must start at the scientist level
Data management

17. Crop ontology: key to DM
Courtesy Elizabeth Arnaud

18. Access to service laboratories
♦ Not really an issue anymore
♦ Large number of genotyping service providers
♦ Not only to generate genotypic data but also to help analyse it
♦ Accessible through the IBP at:
https://www.integratedbreeding.net/genotyping-services
♦ Strongly discourage partners to invest in routine and/or
largescale genotyping technologies
♦ Technologies are evolving too fast
♦ Burden on the staff
♦ More focus on data analysis
♦ Reduce local task as much as possible (send leaf/seed samples)
But:
♦ Requires more planning
♦ Analysis turn-over might still be an issue

19. Availability of SNP markers at GCP for genotyping
Crops SNP source No. of SNPs Status
Maize Cornell University 1,250 Available for genotyping
Cowpeas
University of California,
Riverside
1,122 Available for genotyping
Chickpeas ICRISAT 2,068 Available for genotyping
Pigeonpeas ICRISAT 1,616 Available for genotyping
Rice Cornell University 2,015 Available for genotyping
Cassava IITA, University of Maryland 1,740 Available for genotyping
Sorghum Cornell University 1,503 Available for genotyping
Common
beans
USDA–ARS (USA) 1,497 Available for genotyping
Wheat Kansas State University 1,864 Available for genotyping
Soya beans USDA–ARS (USA) 1,082 Available for genotyping
Groundnut ICRISAT 91 Available for genotyping
Genotyping at: LGC Genomics
 Maize: 278 projects, about 25m data-points
 Other crops: 241 projects, about 3m data-points
 Primer mix distribution

20. Analytical tools and pipeline
♦ Difference between research and applied tools
♦ Difference between stand-alone and integrated tools
♦ A lot of very good tools are available
♦ Analytical Pipeline in the private sector:
♦ AGROBASE http://www.agronomix.com/GenII/PDM
♦ Doriane http://www.doriane.com
♦ KDDart http://www.diversityarrays.com
♦ Prism http://www.graphpad.com/scientific-software/prism/
♦ Virtual lab for plant breeding http://www.vlpb.nl
♦ Analytical pipeline initiatives in the public sector
♦ Integrated Breeding Platform https://www.integratedbreeding.net
♦ IRRI’s trait pipeline http://irri.org/rice-today/the-pipeline-grows-stronger
♦ Seeds of Discovery http://seedsofdiscovery.org/seed/about/
♦ Tassel http://www.maizegenetics.net
♦ Pipeline for high-throughput sequence analysis is in progress
♦ Allelic mining pipeline, still some way to go

21. Support or ‘peripheral’ tools
♦ What are we talking about?
♦ Handheld computers or tablets
 Harness, anti-reflectance filters for field data capture
♦ Printers with special format
♦ Barcode readers, etc
♦ Most often forgotten……
♦ Must be included in the planning and deployment
♦ Can have significant impact on the pipeline efficiency
♦ Might represent additional cost
♦ Specificities clearly define:
♦ Challenge, as it can be quite country-specific
♦ Language issue
♦ Availability
♦ Maintenance

22. Training at NRCRI (Yemi)

23. None of this is new, but it
often happens informally
and/or inefficiently
They seek help
from each other
when stuck
They draw
lessons
together from
their
experiences
They tip and
alert each other
They explore
topics together
They share
approaches
that have
worked for
them
They record
what they learn
together
How do
communities
share &
create
knowledge?
Social network: the concept of CoPs
Potentially strong opportunity through social media!!

24. Mindset and change in behavior

25. ♦ Most people are reluctant or resistant to change
♦ Even clear benefits for change are not sufficient incentive
♦ Most changes can be implemented only by:
♦ Strong bottom-up demand
♦ Mandatory top-down decision
♦ Need to be ready to:
♦ Change the way you do business
♦ Dedicate time to learn new things
♦ Dedicate time to things that might not benefit your work directly
♦ Share results/methods in an open manner
♦ Adopt a corporative spirit
♦ Enforcement and implementation
♦ Big difference between the private and public sector
To change people’s behavior:
A real challenge!

26. The importance of support
services

27. To be successful in enhancing plant-breeding efficiency in
developing countries, much more than the simple release of an
analytical pipeline is required!
♦ The technology development part is the easiest part
♦ Need for:
♦ Good support strategy that combines a centralised team for overall
coordination, and a network of local hubs for daily operations
♦ Clear procedure: whom do I call or contact for what?
♦ Reliable, quick, local and adapted service to the user profile
♦ Different users different services:
 CB needs
 Breeding support
 Customisation of the tools and pipeline
♦ Support service quality a presentation card of the entire Platform
♦ Poor support is a sure killer
The needs and the context

28. The IBP support services
Considering the nature of IBP, and the great diversity of potential users
of the BMS, it is critical to provide a top-quality support services to
ensure sustainable adoption
♦ Professional Support to be provided in three ways:
♦ Client-oriented breeding support primarily targeting developing-
country breeders
♦ Capacity building support to provide professional and
comprehensive training in orientation to, and using, the tools
♦ Interaction with peers through social networks and CoPs
♦ Technical Support to be provided at two levels to all users:
♦ Level 1: installation technical support
 To overcome any difficulties in downloading, installing and getting
started with the BMS and related tools
♦ Level 2: operational technical support
 for users that might encounter problems in day-to-day use of the BMS
and related tools

29. Central Support Team:
Managers plus ad hoc
specialists
Promotion and commercialisation Team
Users from Tier 2
Latin America Africa Asia
(Europe, USA, Canada)
DB/DM
BMS CB
Breeding
Coordinate and facilitate
access to IBP tools and
services
User feedback to impact on
BMS development and
implementation
Organisation of IBP support services

30. Effective Partnerships

31. Indicators
♦ Money allocation to partners
♦ Significant in-kind contribution from partners
♦ Project teams find money outside GCP
♦ Partners continue to work together after GCP project ends
♦ Free exchange of information
♦ Partners not necessarily attracted (purely) by money, but to be part
of a network
♦ Critical but indispensable intangibles – trust and goodwill
Evolution of roles and responsibilities
♦ A switch: Leaders become mentors
♦ Knowledge applied & transferred: Trainees become doers & leaders
♦ Today, more than half of our PIs are from developing countries and
more than half the grants go directly to National Programmes
It takes time and resources to nurture and implement true
partnership!
True partnership

32. From the GCP External Review (2008)
The panel noted that GCP community is one
of the Programme’s crucial assets:
“Perhaps the most important value of GCP
thus far, is the opportunities it has provided
for people of diverse backgrounds to think
collectively about solutions to complex
problems, and, in the process, to learn from
one another.”
The power of grouping forces

33. Linking upstream with applied science
The sorghum case: From Cornell to African farmers’ fields with a
stopover in Brazil: a ten-year effort
♦ Step 1: Competitive Project (initiated 2004)
♦ Led by Cornell Univ, in collaboration with EMBRAPA
♦ Plantlets screened under hydroponics – Alt1 Gene cloned
Magalhaes et al. 2007, Nature Genetics, 39: 1156–1151
♦ Step 2: Competitive Project (initiated 2007)
♦ Led by EMBRAPA in collaboration with Cornell
♦ Favourable alleles identified – Improved germplasm for
Brazil
Caniato et al. 2011, PLoS One 6, e20830
♦ Step 3: Commissioned work (initiated 2009)
♦ Led by Moi University in collaboration with EMBRAPA
♦ Introgression of favourable alleles – Improved germplasm
for Kenya and Niger
The power of pooling expertise

34. The Power of working across Countries
The Cassava CoP
An active community to empower National Programmes to
access and use new germplasm and technologies
♦ Component 1: Access to new alleles
♦ Germplasm exchange across South America and East Africa
(IITA, a key partner here)
♦ Component 2: Strengthening the research community in
Africa
♦ Countries involved: Nigeria (leader), Ghana, Tanzania, Uganda
♦ Another 9 countries added in 2012
♦ Component 3: Visibility at international scene
♦ Eg, Nigeria’s National Root Crops Research Institute (NRCRI)
now a key partner in the Bill & Melinda Gates Foundation
breeding projects, resulting from GCP project
♦ Participate in marker development and sequencing effort
♦ Component 4: Government support
♦ Attracting federal funds to enhance infrastructure at NRCRI

35. ♦ Be strategic in partnership development
♦ Much more than simply numbers, no universal ‘template’:
 Different kinds of partnerships for different needs
 Different kinds of partnership for the same need
♦ Be selective, and cautious
♦ Can easily get out of hand, can be a distraction
♦ Plan for it, and do not underestimate effort needed:
♦ managing true partnerships takes time and resources!!!
♦ But, if managed well:
♦ One of the most efficient way to do business
♦ One of the most rewarding components of the work
♦ Creates a special group dynamic
♦ Critical to bring new ideas
♦ The best way to promote your work
 others speak well of you
 cultivates public trust, resultant positive public image without any PR effort
Important to keep in mind

36. Conclusions and Perspectives

37. ♦ Access to suitable tools and analytical pipeline is not a key
limitation anymore
♦ Capacity on most target countries is increasing significantly
♦ Human capacity: fast progresses
♦ Infrastructure: still slow
♦ Major challenge remains in the area of adoption and support
♦ We must have the buy-in of upper management of user institutions
♦ Must apply a proactive promotion with kick-off meeting at user institutions
♦ Stepwise approach starting populating the DB
♦ Support must be: reliable, quick, local and adapted to the user profile
♦ One size doesn’t fit all!
♦ Time is right (and ripe!) to make a change:
♦ Major public investments
♦ The technology is ready
♦ Solid international networking (e.g. CRPs, Gates’ Initiatives, GCP)
♦ Open access policy adopted and implemented
Conclusions and Perspectives

38. The genomics impact on breeding
Crop diversity
Improved
crops
Genomics