| Sumario: | The optimization of sweetpotato breeding pipelines in
Sub-Saharan Africa (SSA) is crucial for improving genetic gains
while ensuring efficiency in resource allocation. Using the
deterministic model, the outcomes to modifications to the
national sweetpotato breeding program at NaCRRI-NARO were
simulated. Heritability assessments revealed that genotype-by
-environment interactions account for just 16% of total
phenotypic variation, with moderate heritability observed in
stage-1 (0.26) and stage-2 (0.56) trials.
The simulations revealed that selection efficiency will be
greatly improved by making changes breeding pipeline
including 1) reducing plot sizes and replicates in stage-3 trials,
2)reallocating resources from National Performance Trials (NPT)
to earlier-stage trials, 3) conducting stage-2 and stage-3 trials
in a single season instead of two and 4 expanding the total
number of clones tested per year from 3,561 to 6,682 (+87.6%).
This translates to doubling the number of clones at stage-1
from 3,000 to 6,000 (+100%) and increasing the number of
clones at stage-2 clones from 500 to 650 (+30%). This larger
breeding population enhances the probability of identifying
superior lines across multiple trait targets. However, early-stage
phenotypic selection, due to lower heritability, limits overall
genetic gains.
To counteract this, genomic selection (GS) is proposed as a
complementary strategy, improving early-stage selection
accuracy and efficiency. Growing the stage-2 and 3 trials for just
one season has the potential to increase the genetic gain per
year (Gy) by 9%. By integrating optimized resource allocation,
expanded selection populations, and genomic selection
approaches, NaCRRI-NARO’s sweetpotato breeding program
demonstrates a model for maximizing genetic gains while
maintaining an efficient and scalable breeding framework in
SSA.
|
|---|