The Contribution of Exotic Varieties to Maize Genetic Improvement
Bibliographic record
Abstract
Maize ( Zea mays L.) is one of the most important staple crops globally, providing essential food and energy resources for millions of people. The genetic improvement of maize has been a focal point of agricultural research, aiming to enhance yield, resilience, and adaptability to various environmental conditions. The introduction of exotic maize lines into adapted germplasm has shown significant potential in increasing genetic variability and improving agronomic traits. For instance, testcrosses of backcross-derived lines exhibited substantial yield improvements, with some lines producing up to 1 056 kg/ha more grain than the control. RNA-sequencing of diverse maize lines revealed extensive genetic and transcriptomic diversity, identifying novel transcripts that contribute to heterosis. Screening of elite exotic inbreds demonstrated that certain tropical lines performed well in temperate environments, suggesting their utility in broadening the genetic base of U.S. maize. Additionally, molecular marker studies confirmed high levels of polymorphism and genetic diversity in tropical maize germplasm. Adaptation efforts have successfully integrated tropical germplasm into temperate breeding programs, enhancing genetic gains. The findings underscore the value of exotic germplasm in maize breeding programs. The integration of exotic alleles has not only expanded the genetic base but also led to the development of high-yielding hybrids with improved agronomic traits. These results highlight the importance of utilizing diverse genetic resources to achieve sustainable genetic improvement in maize.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".