TY  - JA
AU  - Chaingeni,D.
AU  - Ronica Mukaro
AU  - Sneller,C.
AU  - Cairns,J.E.
AU  - Musundire,L.
AU  - Das,B.
AU  - Odiyo,O.
AU  - Madahana,S.L.
AU  - Purity Mazibuko
AU  - Mubvereki ,W.
AU  - Prasanna,B.M.
AU  - Dumisani Kutywayo
TI  - More bang for your buck : : potential gains through optimizing maize breeding schemes in sub-Saharan Africa
SN  - 1664-462X
PY  - 2025///
CY  - Switzerland
PB  - Frontiers Media, 
KW  - Improvement
KW  - AGROVOC
KW  - Deterministic models
KW  - Genetic gain
KW  - Breeding
KW  - Efficiency
KW  - Maize
KW  - Africa South of Sahara
N1  - Peer review; Open Access
N2  - 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
UR  - https://hdl.handle.net/10883/35753
DO  - https://doi.org/10.3389/fpls.2025.1553272
T2  - Frontiers in Plant Science
ER  -