Excellence in Breeding

Excellence in Breeding

The CGIAR Excellence in Breeding Platform (EiB) contributes to the modernization of crop breeding programs that target the Global South.

In 2020, the Platform made important progress toward enabling transformational change across CGIAR and national agricultural research system (NARS) breeding, realizing the full potential of investment in Crops to End Hunger (CtEH), a project that aims to enhance the performance of crop breeding programs through crop-specific and crosscutting improvement plans.

To support this work, the EiB team underwent significant expansion, increasing both direct engagement with breeding programs and taking on a strategic planning role for cross-CGIAR investment in centralized breeding capacities and services.

Key performance indicators were developed and adopted to track breeding program performance across CGIAR. Direct collaboration with breeding programs led to the identification of 320 unique subregional market segments across 26 crops. Each identified market segment reflects a unique combination of grower and consumer needs, and all are gender inclusive. Together with the CGIAR Research Program on Roots, Tubers and Bananas’ Breeding Community of Practice, a hackathon was convened bringing together breeders, social scientists, gender specialists and food scientists to evaluate and improve product profiles by analyzing the processes to define market segments and to develop associated product profiles. Across CGIAR, 120 breeding pipelines were identified and characterized — these are subsets of breeding programs that are aligned with well-defined market segments.

With greater alignment between breeding pipelines, market segments and product profiles, it will be possible for investment to be directly targeted to improve food security and livelihoods in priority areas.

In 2020, EiB worked directly with breeding programs to standardize the documentation of breeding schemes and identify areas for improvement. Breeding program simulations were used to predict results and support adoption by 11 CGIAR breeding programs.

Optimizing breeding programs across CGIAR

In 2020, EiB made important progress toward the goal of optimizing breeding schemes to improve rates of genetic gain.

There was growth in direct engagement with breeding teams, with methodologies used to document breeding schemes and identify areas for improvement. This was used to simulate possible solutions and execute plans to fill identified gaps. A library of these simulation results and discussion of the implications for individual breeding programs is available in the EiB Toolbox.

Through this engagement, important action areas were identified to optimize CGIAR breeding programs:

1. Options to reduce cycle time.
2. Adoption of selection indices to summarize multitrait data.
3. Optimal program size (number of parents, crosses and progeny crosses).
4. Adoption of reciprocal recurrent selection in hybrid programs.
5. Proper implementation of genomic selection.
6. The use of modern experimental designs for field trials to evaluate variety performance.

The simulated results of these choices have led to adaptations in IITA-Cassava, IITA-Banana, IITA-Yam, CIAT-Forages, CIAT-Beans, CIMMYT-Maize, CIMMYT-Wheat, CIP-Potato, CIP-Sweetpotato, IRRI-Rice, and AfricaRice breeding programs.

In addition, four manuals and guidelines to optimize breeding schemes were published in the EiB Toolbox in 2020:

Guidelines for germplasm and trait introgression
Estimating surrogates of genetic value
Selection intensity
Genetic gain as a high-level key performance indicator

Shared genotyping services advanced with the launch of a mid-density service, while the existing low-density service avoided disruptions during the COVID-19 pandemic and continued to meet business volume growth goals. These genotyping service platforms, which refer to the marker density spanning a crop genome, enable the validation of the unique character of a selected parent and confirmation of successful crosses among the progeny, with the mid-density service facilitating the move toward genomic selection.

In 2020, EiB provided technical and managerial support to major phenotyping, operations, and infrastructure upgrades across breeding programs in Africa. Targeted technical interventions supported by EiB in CtEH priority crop breeding programs included improved irrigation design, design and CtEH funding of seed drying facilities, design and CtEH funding of biotic and abiotic stress screening facilities, design of seed processing infrastructure, soil management recommendations, nutritional trait phenotyping recommendations, and GPS recommendations.

A shared high-throughput phenotyping service offering digital image analysis was initiated, and a breeding program costing process began rollout. Three continuous improvement projects were started with CGIAR breeding teams at the International Rice Research Institute, the International Maize and Wheat Improvement Center lab, and EiB, which covered topics such as addressing crop health through sustainable fields, seed and chemical inventories, and coordination and workflow management at EiB. Further training in continuous improvement and the Lean Methodology (a management approach) was provided to 75 CGIAR staff members and 120 NARS staff members.

A new model for CGIAR-NARS collaboration was introduced that provides clearer definitions of roles and responsibilities in breeding, along with a network of regionally based specialists that will directly support the modernization of NARS breeding.

Development of the Enterprise Breeding System (EBS) was fully incorporated into EiB in 2020, which has a direct role in implementing integrated data management systems, biometrics, and bioinformatics.

New webinar series and working groups were convened, which overcame disruptions caused by the global COVID-19 outbreak and facilitated improved collaboration that will support the transition to One CGIAR. A new model for CGIAR-NARS collaboration was introduced that provides clearer definitions of roles and responsibilities in breeding, along with a network of regionally based specialists that will directly support the modernization of NARS breeding.

Cross-cutting Platforms:
Big Data in Agriculture
Cross-cutting Platforms: