How can I optimize my breeding scheme?
New series of manuals developed by EiB is a practical guide to designing crossing, evaluation and selection strategies, and developing performance indicators such as genetic gain and heritability.
Breeding scheme optimization
Computer simulations are helping CGIAR breeders develop better varieties faster
In the past, plant breeding has helped avert entire famines by changing a handful of genes in key crop varieties. But today's breeders must meet similar challenges with consistent excellence, making the right decisions each season to refine natural genetic diversity into a multitude of hard-working food crops.
Webinar: Costing for breeding programs
The “Breeding Costing Tool” is a powerful solution for allowing users to estimate the cost of crop breeding and its associated research activities and to help breeders make decisions about resource allocation. The costing tool is designed to calculate the cost of running a crop breeding activity, or an entire breeding pipeline, using the prices, costs and salaries from a single year. The software is freely available from the Queensland Alliance for Agriculture and Food Innovation and the University of Queensland in Australia.
AlphaSimR
AlphaSimR is a GitLab repositorium with R code addressing multiple simulation questions for designing breeding programs. This includes proper program size, experimental designs, surrogates of genetic merit, reduction of cycle time, among others (this is only accessible to a small number of users on request).
Link: https://gitlab.com/excellenceinbreeding/module2
Contact: g.covarrubias@cgiar.org
Selection index tool
A tool to explore the trade offs among multiple traits using selection indices. Final weights for selection can be used using this tool.
Link: https://bkinghor.une.edu.au/desire.htm
Contact: bkinghor@une.edu.au
Multi-environment trial (MET) analysis tool
A tool to perform multi environment trial analysis and obtain adjusted means for genotypes that can be used in recycling, selection decisions and genetic gain calculation.
Link: apariciojohan.github.io/MrBeanApp/
Contact: j.aparicio@cgiar.org
Optimal selection methods
What is the best selection method?
Selection methods for multiple traits aim to find the balance to increase the population means for multiple traits.
These simulation reports demonstrate the differences between classical methods such independent culling and selection indices, to support the practical implementation of indices.
Contact: g.covarrubias@cgiar.org
Optimal program sizes
What is an optimal program size?
The size of the breeding program is something that can increase the genetic gain by taking advantage of factors such as selection intensity or the among- and within-family variance.
These simulation reports demonstrate the trade-offs between of number of parents, crosses and progeny per cross, and provide practical advice on how to set the level of each.
Contact: d.gemenet@cgiar.org
Choosing the right number of testers
What is the right number of testers?
Hybrid breeding aims to keep and increase the non-additive interactions in the final products while increasing the additive genetic value in a recurrent selection program.
The following simulation reports and retrospective analyses explore questions such as how many testers should be used to capture the general combining ability (GCA) that maximizes additive and non-additive effects.
Contact: d.gemenet@cgiar.org
Implementing hybrid breeding
Should we move to hybrid breeding?
Hybrid breeding aims to keep and increase the non-additive interactions in the final products while increasing the additive genetic value in a recurrent selection program.
The following simulation reports show how the hybrid genetic model applies to non-inbred crops and different ploidies. In addition, we provide practical advice for related questions related to testers, 3-way crosses, etc.
Contact: m.r.labroo@cgiar.org