More bang for your buck: potential gains through optimizing maize breeding schemes in sub-Saharan Africa

Increasing the rate of genetic gain in breeding programs is a critical component of crop genetic improvement strategies to increase yields in smallholder farmers' fields. While a growing array of technologies and tools are being deployed within breeding programs, optimizing resource allocation could...

Descripción completa

Detalles Bibliográficos
Autores principales: Chaingeni, Davison, Mukaro, Ronica, Sneller, Clay, Cairns, Jill, Musundire, Lennin, Das, Biswanath, Odiyo, Olivia, Madahana, Sammy Larry, Mazibuko, Purity, Mubvereki, Washington, Boddupalli, Prasanna, Kutywayo, Dumisani
Formato: Journal Article
Lenguaje:Inglés
Publicado: Frontiers Media 2025
Materias:
Acceso en línea:https://hdl.handle.net/10568/179253
Descripción
Sumario:Increasing the rate of genetic gain in breeding programs is a critical component of crop genetic improvement strategies to increase yields in smallholder farmers' fields. While a growing array of technologies and tools are being deployed within breeding programs, optimizing resource allocation could provide a simple yet effective way to increase genetic gain, particularly within resource-constrained breeding programs. The objective of this study was to demonstrate that an easy-to-use deterministic model and a breeding costing tool could identify key modifications to improve the efficiency of breeding within the Zimbabwean national maize breeding program. The current program uses pedigree inbreeding, with a 4-1-1 tester scheme, and relatively low selection intensity. The method of inbreeding, test-crossing schemes, and selection intensity were modified within the current program budget. A combination of using doubled haploid lines, a 2-2-1 tester plan, and increased selection intensity improved gain per cycle by 42.8%, gain per year by 161.8%, gain per dollar by 43.1%, and decreased cost of one unit of genetic gain by 28.5% without a change in budget. Our results highlight how a simple deterministic model can identify steps to greatly improve breeding efficiency within resource-constrained breeding programs.