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ICRISAT Global Planning Meeting 2019: Modernising ICRISAT Crop Improvement & Adapting Industry-Proven Processes for Public Institutions by Jan Debaene

  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
  15. 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?
  16. 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
  17. 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
  18. Change our viewpoint… “ ”
  19. The change we want to implement…. Characteristics of high performing programs Percentage of organizations that employ these characteristics
  20. 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
  21. 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
  22. 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
  23. 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.
  24. 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
  25. 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
  26. 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
  27. • 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
  28. Are we* too involved in the operational minutiae of our breeding programs to see the big picture… *scientists
  29. • 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?
  30. Personal Effect of Change
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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.
  38. 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
  39. 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
  40. 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.
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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)
  48. 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
  49. 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
  50. Improved design for medium-term cold store (Groundnut)
  51. 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
  52. 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
  53. Scope for improvement with Seed Processing Line SPL Minimal workforce Fast Mix-free Improved quality Quick turnover time Economical
  54. In Threshing Blowing Grading(Optional) Seed counting + weighing Bar code label printing Out Proposed Seed Processing Line Cold stores Trials
  55. •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:
  56. 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
  57. Change is a Process, Not an Event We are Committed to Help You Grow through Change
  58. In Summary: Δ𝐺 𝑦𝑒𝑎𝑟 = i 𝑟𝐴𝐼 σ 𝐴 𝐿 The components of the genetic gains equation will drive the breeding modernization initiative
  59. Questions?
  60. Thank You
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