How can we increase productivity and effectiveness? Consolidate breeding activities and complimentary disciplines in one place, Establish Regional Crop Improvement Hubs (RCIH)

1. Modernising ICRISAT
Crop Improvement
Adapting Industry-Proven Processes
for Public Institutions
Dr. Jan Debaene
Global Head – Breeding

2. Perspective…

3. Genetic Gain
Δ𝐺 𝑦𝑒𝑎𝑟 =
i 𝑟𝐴𝐼 σ 𝐴
𝐿
𝐢 = Selection intensity
𝒓 𝑨𝑰 = Accuracy
𝝈 𝑨 = Genetic variance
𝑳 = Generation interval
Time:
• Reducing generation interval =
increases slope of the line
• Denominator = Largest effect
Traitvalue
0 1 4
Existing
value
Target
value
2 3
Trait value
Trait value
Trait value
Trait value
Trait value
Time

4. Breeding is a Quantitative Science
Traitvalue
0 1 4
Existing
value
Target
value
2 3
Trait value
Trait value
Trait value
Trait
value
Trait
value
Time
Most traits are controlled by many genes.
Recombining them in new configurations, contributes
to finding a few superior offspring.
Reliable data is key to estimate several of these genetic
parameters, such as estimating genetic heritability.
“
”

5. Data is key to increase genetic gain
Data-Driven Processes
Data
Data systems:
Data input & access – BMS shared DB
Data integrity – validation & backup
Data-driven decisions – visualization
System training & updates
Digital data recording

6. Data is key to increase genetic gain
Data-Driven Processes
Data
Quantity:
Multi-location testing
Target environments
More trials
Datasystems:
Data-drivendecisions–visualization
Dataintegrity–real-timebackup
Dataaccess–BMSsharedDB
Systemtraining&updates
Digitaldatarecording

7. Data is key to increase genetic gain
Data-Driven Processes
Data
Quantity:
Multi-locationtesting
Targetenvironments
Moretrials
Quality:
Accuracy – experimental design
Metrics – statistics
Context – where, when, how
Documentation & accountability
Datasystems:
Data-drivendecisions–visualization
Dataintegrity–real-timebackup
Dataaccess–BMSsharedDB
Systemtraining&updates
Digitaldatarecording

8. Data Quality: Standardization
Validating database (DB) uploads
 DB do not spellcheck
 DB record characters as they
are entered
 DB only stores information
Smriti
Smruti
AK 12- 24
AK 12-24
DONT KNOW
DONTKNOW
vs.
KH_IMP_VAR

9. Data Upload Compliance: Current Status

10. Data Upload Compliance: Current Status
0
10
20
30
40
50
60
70
80
90
100
India Ethiopia Kenya Malawi Mali Niger India Ethiopia Kenya Malawi Mali Niger Nigeria
Asia ESA WCA Asia ESA WCA
2017 2018
% Compliance Chickpea
Fingermillet
Groundnut
Pearlmillet
PigeonPea
Sorghum

11. Breach of Trust
Data Upload Non-Compliance

12. Action ICRISAT NARS
Collection format?
How?
When?
Data Upload to BMS or GOBii
Data Upload Compliance: Solution Activity

13. Having all data in a common database is an essential component
of Breeding Modernization
Solutions:
1. Centralization of data recording and upload:
a. One of the specialization teams in the Crop Improvement Operations
Team is focused on collection of phenotypic data and upload.
b. Make data available to the breeders or genomicists only through BMS
2. Annual breeding advancement meetings (by crop) based on data from BMS:
a. Implementation of Stages and Gateways (EiB module 1)
3. Any future ICRISAT grant proposals should include a mandatory section on
data management and as part of the requirement all data generated by or
for ICRISAT should be standardized and:
a. Field experiments uploaded to BMS,
b. Genomics data uploaded to GOBii
Compliance with Data Upload

14. Fixing (F2 –> Fx) & Line Increase
Genotypic Selection (MAS)
Phenotypic Selection (traits)
Yield Trial Testing – R&D trials
On-farm Yield Trial Testing
Selection of Final
Varieties (OVT)
Selection
Based on
CoGs
Release
Phenotypic
Genotypic
Evaluation
Tested
Untested
P1 x P2 – Px x Py
Recombination
𝐢 = Selection intensity
𝒓 𝑨𝑰 = Accuracy
𝝈 𝑨 = Genetic variance
𝑳 = Generation interval
Δ𝐺 𝑦𝑒𝑎𝑟 =
i 𝑟𝐴𝐼 σ 𝐴
𝐿

16. Consolidate breeding activities and complimentary
disciplines in one place:
• Establish Regional Crop Improvement Hubs (RCIH)
 Samanko, Mali
 Matopos, Zimbabwe?
 Patancheru, India
How can we increase productivity and effectiveness?

17. RCIHs to Increase Productivity and Effectiveness
Upgrading and implementing of modern technologies
• Mechanization of key activities such as planting,
harvesting and seed processing
• High-throughput phenotyping
• Digitization of data collection and transfer
• Centralized data management and analyses
• Rapid generation turnover cycling capabilities and
capacity

18. RCIHs to Increase Effectiveness and Productivity
All early-generation breeding activities are managed at
the RCIH for all crops
• Crop Improvement Operations Team (CIOT) shared
by all breeders
• Regional Breeding Lead to guide and mentor
breeding team and direct the CIOT
• Close interaction with the complementary
disciplines, sharing the hub
• Provide training grounds for partners and trainees

19. Change our viewpoint…
“
”

20. The change we want to implement….
Characteristics of high performing programs
Percentage of organizations that employ these characteristics

21. Breeding programs will be driven by Product Profiles (PPs)
• PPs will be designed with input from NARS, market experts,
climatologists, socio-economic scientists, gender specialists,
nutritionists and breeders.
• PPs will be based on realistic prioritized objectives, with
quantifiable trait targets, with the aim to replace the
predominant variety in the market or introduce new product.
• Once initial PPs are generated, a feedback system using stages
and gateways to ensure agile response to customers needs with
the greatest potential impact on livelihoods.
• Team meetings by breeders with complementary disciplines, i.e.
plant pathologists, physiologists, genomic scientists, etc., to
strategize on how to execute on effective delivery of the PPs.
Develop Product Profiles for Each Crop/Geography

22. Design of Product Profiles
•Input from multidisciplinary team
Target
Product
Profile
Future
market
demand
Climate
change
effects
Nutrient
needs
Social &
economic
aspects
Pest &
pathogen
evolution

23. Five fundamentals of high performing teams
Commitment
Results
Accountability
Conflict
Trust
Open Dialogue
Characteristics of High Performing Teams
• Are comfortable asking for help, admitting mistakes
and limitations and take risks offering feedback
• Tap into one another's skills and experiences
• Avoid wasting time talking about the wrong issues
and revisiting the same topics over and over again
because of lack of buy-in
• Make higher quality decisions and accomplish more
in less time and fewer resources
• Put critical topics on the table and have lively
meetings
• Align the team around common objectives
• Empower star employees, and recognize them so
they feel valued

24. Breeding Program (PP: achievable objectives defined by
multidisciplinary team)
Product
Profile A
60%
Product
Profile B
30%
Product
Profile C
10%
Product Profile Strategy Team
Breeder
Genomic Scientist
Plant Pathologist
Bioinformatics
Plant Physiologist
Crop Improvement Operations Team
Crossing Team
Field Trial
Team
Seed
processing
Team
Phenotyping
and data
collection team
Breeding Structure
Team-based Approach
Etc.
Etc.

25. New role: Product Placement Leads (PPL) (SMG or IRS)
• Coordinate closely with NARS or seed companies
• Place varieties with partners and monitor trials
• Provide feedback to breeders and adjust PPs
• Alleviate breeders’ public time demands; provide increased
focus on product development by breeder
• Plan and coordinate experimental variety and breeders seed
productions
Regional Organizational Structure

26. Global Head of
Breeding
WCA Regional
Breeding Lead
Asia Regional
Breeding Lead
Crop
Improvement
Operations Lead
Sorghum
Breeder &
Associate*
Pearl Millet
Breeder &
Associate*
Finger Millet
Breeder &
Associate*
Chick Pea
Breeder &
Associate*
Pigeon Pea
Breeder &
Associate*
Ground Nut
Breeder &
Associate*
ESA Regional
Breeding Lead
Proposed Breeding Structure
Pathology
Plant Physiology
Entomology
Bio-Informatics
CEGSB
Wide
Hybridization
Molec. Biology
Genebank
Genomics &
Trait Developmt
% time dedicated to each of the programs will
be demand (project) driven.
Regional Product
Placement Lead
Regional Product
Placement Lead
*Associate can be a Scientific Officer, Junior Breeder, Visiting Scientist or Consultant

27. Crop Improvement Operations Team
Senior Mentors
Rapid Generation
Advancement Team
Data Collection &
Phenotyping Team
Cereal Crossing
Team
Legume Crossing
Team
Greenhouse
Operations Team Field Trial
Operations Team
Seed Processing
Team
Seed Inventory
Storage Team
Sampling Team
Crop Improvement Operations Structure
Technical staff will move between teams, depending on seasonal demand.
• Each staff member will be assigned to a primary team for administrative reporting purposes.
• Each team will have at least one primary and secondary team lead.
• Each team will have a scientific advisor/consultant (Ph.D. level expert scientist).
CIOT Lead

28. • Team Leads and deputies for each Specialty Team at HQ were selected by
the breeders from existing staff (24-01-19: needs review and ratification)
• Candidates preferences to take a primary or deputy team lead role were
considered based on a questionnaire filled out by them.
• Other factors considered from the questionnaire:
• Qualifications:
• Skillset
• Experience
• Academic
• Involvement
• How can they improve the processes for the team they join
• How will they expand their knowledgebase
• How can they contribute to technology development and implementation
• Interpersonal and leadership skills
• How will they contribute to building team spirit and cooperation
• How can they ensure good verbal and non-verbal communication skills
• How can they apply receptiveness to feedback and conflict resolution
• How will they show respect for others
Implementation: Organizing the Crop
Improvement Operations Team

29. Are we* too involved in the operational minutiae of our breeding
programs to see the big picture…
*scientists

30. • By removing the responsibility of operational and logistical
execution of all routine activities from individual scientists’
programs, breeders can focus on breeding methodology,
strategy and resource mobilization.
• By centralizing operations, we can develop specialization teams
with better skillsets.
• Greater continuity and potential to make it more attractive to
good talent and offer a career path.
• Standardization in processes, methodology and delivery of
results.
Implementation:
Why a CIOT?

31. Personal Effect of Change

32. August 2018 visit, report given in October
•Agronomic Practice
•Seed Processing
•Planting / Harvesting
•Phenotyping
•Continuous Improvement & HSE
ICRISAT-HQ Research Station Assessment
by Excellence in Breeding

33. Visits - August 16-24, 2018
1) Data management overview (BMS) – Abhishek Rathore
2) Overview of Global Breeding – Jan Debaene and Sobhan Sajja
3) Seed Processing - Jan Debaene, Sobhan Sajja, Janila Pasupuleti, Gaur Pooran
4) Meeting with DDG – Kiran Sharma
5) Farm & Engineering Service overview – Suresh Pillay, Mr. Chandrasekhar, Girish
Panchariya, Pankaj Maknwar, M. Sreekanth
6) Machinery shed - M. Sreekanth, Girish Panchariya, Pankaj Maknwar
7) Overview of Research Program (Genetic Gains) – Rajeev Varshney
8) Meeting with RP – GG – Manish K Pandey, Rakesh K, Rajeev Gupta, Rajeev Varshney
9) Pathology – Mamta Sharma
10) Field tour - MM Sharma
11) Phenotyping - Jana Kholova
12) Entomology – Jaba Jagdish
13) Greenhouses/ Growth chamber facilities – Suresh Pillay, Mr. Chandrasekhar
14) Workshop (seed quality analysis) led by Vincent Vadez (EiB Module IV)
15) Corteva visit – Babu Raman, Suresh Pillay, Sobhan Sajja
16) GIS and remote sensing team – Gumma Krishna, Mohammed Ahmed
17) Groundnut Breeding overview – Janila Pasupuleti
18) HarvestMaster interaction – Allen Wilson

34. EiB Module IV - Priorities
• Approaches to increase plot throughput/reduce costs through
mechanization, automation.
• Approaches to increase plot throughput/reduce costs through HT
phenotyping (Qualitative / Quantitative)
• Streamlined processes with lab providers for physico-chemical
composition and nutritional properties
• Inventory of NIRS uses and joint calibration efforts.
• GxExM methods
Mechanization and automation report
Quality Analysis Workshop report
Both
Pending

35. Suggested Action Plan from EiB Visit
• Category
• Sub-Category
• Current Status
• What is needed?
• Why?
• Action number
• action to address
• task number
• task
• Who is responsible (RACI)
• Impact (1 - 10)
• Duration (months) Status
• Cost Estimate (USD) OPEX
• Total Estimated operational Cost: 2019, 2020, 2021, 2022, 2023
• Cost Estimate (USD) Capital
• Total Estimated Capital Cost: 2019, 2020, 2021, 2022, 2023

36. GOOD PRACTICE Opportunities
Irrigation and weather data
Broader range of equipment
Established infrastructure
Use of weather data
Irrigation software
Automate valves and use
more controlled systems;
measure water volume
applied
More use of soil moisture
sensors
More use of soil water
capacity to manage irrigation
Better weather data
resolution; actual evaporative
demand

37. GOOD PRACTICE Opportunities
Farm Management System
Online service request and
field allocation system
Historical data
Current system is being updated.
System could have features
for:
- Crop Scouting (could be
integrated with BMS)
- GIS mapping
- Data exchange
- Field analysis
- Historical data of chemical
application
Could be integrated with
tractor telemetry system

38. GOOD PRACTICE Opportunities
Greenhouses and Controlled environment
Monitoring plant
growth capacity
Growth Chamber
Capacity for
specific projects
Good glass
house capacity
Automation system current
in use could be updated
(traceability, reports, etc..)
Better environment
characterization (sensor
types and distribution)
Depending on the demand for rapid generation
advancement it would certainly need investment
in irrigation, environment control.
Capacity vs demand analysis to minimize
negative impact of idle capacity.

39. GOOD PRACTICE Opportunities
Seed Processing Infrastructure
Proper space
(dedicated shed) for
seed processing
Some good pieces of
equipment:
- Shellers
- Blowers
- Seed Dryers
Stop working in silos.
Establish seed
processing workflows
and safety protocols
Cleaning and organizing
the seed processing
area is critical. Risk of
mixture / losses / HSE

40. GOOD PRACTICE Opportunities
Conditioning, Packaging and Treating
The process to organize
cold storage has been
initiated
Good equipment has
already been acquired
Automated labeling and
printing
Remove operational
silos. Difficulty to share
equipment and monitor
efficiency
- Access control system is recommended
to be installed in cold storage rooms
- Seed treatment area should be defined
- Trial fulfillment area should be defined

41. GOOD PRACTICE Opportunities
Planters / Planting Solution
Planters are well-
maintained
Good amount of plot
planters
Adopt GPS planter
- Standardize row spacing across crops
would reduce the number of planters
significantly.

42. GOOD PRACTICE Opportunities
Plot Combines / Harvesting solutions
Harvesters are well-
maintained Improve combine
headers
- Standardize row spacing across crops
would reduce the number of combines
significantly.
Use of weighing system
(embedded or not in
harvesters)
Use of NIR or other
technologies

43. GOOD PRACTICE Opportunities
Phenotyping
Some teams are
using data
collectors
It is already integrated with BMS
Engaging more teams
to use electronic data
collectors
Removing operational
silos. Difficulty to share
equipment and monitor
efficiency
• Establishing a continuous improvement
culture (phenotyping committee)
• Standardizing hardware, software and
consumables

44. GOOD PRACTICE Opportunities
Continuous Improvement / HSE
• Reducing potential
for injuries
• Healthier work
environment
• Increased capacity and reduced
operational costs
• Employee engagement and job
satisfaction
• Some equipment
has proper guarding
• Some procedures
being adopted /
Visual

45. Management
ICRISAT: visited Hyderabad during August
• Current state assessment > 90% completed
• Definition of future requirements (Gap Analysis) requires M1 and M2 input
desired future state
(M1, M2 + programs)
current state
(M4 + programs)
Gap
Analysis
Define priorities for
improvement planning
(capabilities & capacity)
Oct-18 Nov-18 Dec-18 Jan-19 Feb-19 Mar-19
presented to team Contributors Mtg. Gap Analysis
Product profiles – M1
Breeding schemas – M2
Action Plans!
Necessary:
• Tools must be available
• Client and EiB engagement
• Timely completion of Product Profile,
Breeding Schemas, and Gap Analysis

46. Seed Processing and Inventory
Process equipment
Cold Chambers
Seed Driers
Seed Processing
Conditioning,
packaging,
treatment and
Seed Quality
assurance
Seed Process
Infrastructure
Conditioning,
packaging
and Treating

47. Shortcomings of present inventory system
SOP – not in place
Sample size – not standardized, space crunch
Hand-written labels – prone to mistakes
Inventory in Excel sheets – no real-time
update on availability
Old material – no information on viability
Regeneration – no protocol in place

48. Respecting the fundamentals of breeding
Seed Management (mix-up of seed)
Cold room status
Take-home
messages
Focus on the basics:
• Make sure you execute well
Seed Inventory and Preservation of Identity
(Avoiding Seed Mixtures)

49. Medium term storage – At present
34862
7204
17992
27300
7343
778
3000
19834
17646
11200
1000
22171
773
2655
3275
3619
6250
0 5000 10000 15000 20000 25000 30000 35000 40000
Groundnut Breeding
Chickpea Breeding
Pigeonpea Breeding
Pearl millet Breeding
Sorghum Breeding
Finger millet breeding
Chickpea Molecular Breeding
Pigeonpea Molecular Breeding
Pearl millet Molecular Breeding
Sorghum Molecular Breeding
Finger millet Molecular Breeding
Pre-Breeding
Chickpea & Pigeonpea Pathology
Pathology (Pearl millet)
Pathology (Sorghum)
Entomology
Crop Physiology

50. Scope for improvement with Seed
Inventory Management System
SIMS
SOP for
inventorying
Standard
sample
size
Bar-coded
label
Transparent
& online
Real-time
status
Regeneration
in time

51. Improved design for medium-term cold store
(Groundnut)

52. Seed processing
Regeneration nursery
Seed inventory management
system
Long term
store (100
seeds)
Medium
term store
(1000 seeds)
Trials/nurseries
Short term store
Finished lines with a
new IC name
Seed Inventory Management System (SIMS)
– Process Flow

53. Seed processing - Volume of samples
Crop/Season Rainy Post-rainy Summer Total
Chickpea 7,000 27,000 35,000
Groundnut 12,000 12,000 24,000
Pigeonpea 8,000 8,000
Pearl millet 45,000 45,000 90,000
Sorghum 25,000 40,000 30,000
Finger millet 2,000 2,000 1,000 5,000
84,000 114,000 28,000 192,000

54. Scope for improvement with Seed Processing Line
SPL
Minimal
workforce
Fast
Mix-free
Improved
quality
Quick
turnover
time
Economical

55. In
Threshing Blowing Grading(Optional)
Seed counting + weighing
Bar code label printing
Out
Proposed Seed Processing Line
Cold stores
Trials

56. •Hire Regional Breeding Theme Leads (IRS): (2nd
QTR’19) Asia, WCA & (3rd QTR’19) ESA
•Hire CIOT Lead (SMG): (1st QTR’19) Asia, (2nd QTR’19)
WCA & (3rd QTR’19) ESA
•Launch CIOT
•Reassign current scientists or hire Product
Placement Leads (SMG or IRS): 2 each for (2nd
QTR’19) Asia, WCA and (3rd QTR’19) ESA
•Hire Project Manager (temporary consultant): (1st
QTR’19) HQ
Next First Priority Implementation Steps:

57. 2019
Today
Jan Feb Mar Apr May Jun Jul Aug
2019
Feb 10 - May 31 Hire Regional Breeding Theme Lead (IRS): Asia
Feb 15 - May 31 Hire Regional Breeding Theme Lead (IRS): WCA
Apr 1 - Jul 15
Hire Regional Breeding Theme Lead
(IRS): ESA
Feb 10 - Mar 31 Hire CIOT Lead (SMG): Asia
Apr 1 - Jun 30 Hire CIOT Lead (SMG): WCA
Jun 1 - Aug 15
Hire CIOT Lead (SMG):
ESA
Apr 1 - Aug 31
Launch CIOT:
HQ
Jan 1 - Mar 1
Hire Project Manager: HQ
Apr 1 - Jun 30
Reassign current scientists or hire Product
Placement Leads (SMG or IRS): Asia
Apr 1 - Jun 30
Reassign current scientists or hire Product
Placement Leads (SMG or IRS): WCA
Jun 1 - Aug 15
Reassign current
scientists or hire Product
Placement Leads (SMG
or IRS): ESA
Hiring Timeline

58. Change is a Process, Not an Event
We are Committed to Help You Grow through Change

59. In Summary:
Δ𝐺 𝑦𝑒𝑎𝑟 =
i 𝑟𝐴𝐼 σ 𝐴
𝐿
The components of the genetic
gains equation will drive the
breeding modernization initiative

60. Questions?

61. Thank You