Improving Maize Grain Yield under Drought Stress and Non‐stress Environments in Sub‐Saharan Africa using Marker‐Assisted Recurrent Selection
Bibliographic record
Abstract
ABSTRACT In marker‐assisted recurrent selection (MARS), a subset of molecular markers significantly associated with target traits of interest are used to predict the breeding value of individual plants, followed by rapid recombination and selfing. This study estimated genetic gains in grain yield (GY) using MARS in 10 biparental tropical maize (Zea may L.) populations. In each population, 148 to 184 F2:3 (defined as C0) progenies were derived, crossed with a single‐cross tester, and evaluated under water‐stressed (WS) and well‐watered (WW) environments in sub‐Saharan Africa (SSA). The C0 populations were genotyped with 190 to 225 single‐nucleotide polymorphism (SNP) markers. A selection index based on marker data and phenotypic data was used for selecting the best C0 families for recombination. Individual plants from selected families were genotyped using 55 to 87 SNPs tagging specific quantitative trait loci (QTL), and the best individuals from each cycle were either intercrossed (to form C1) or selfed (to form C1S1 and C1S2). A genetic gain study was conducted using test crosses of lines from the different cycles F1 and founder parents. Test crosses, along with five commercial hybrid checks were evaluated under four WS and four WW environments. The overall gain for GY using MARS across the 10 populations was 105 kg ha−1 yr−1 under WW and 51 kg ha−1 yr−1 under WS. Across WW environments, GY of C1S2–derived hybrids were 8.7, 5.9, and 16.2% significantly greater than those of C0, founder parents, and commercial checks, respectively. Results demonstrate the potential of MARS for increasing genetic gain under both drought and optimum environments in SSA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".