Mixing trait-based corn (Zea mays L.) cultivars increases yield through pollination synchronization and increased cross-fertilization
Bibliographic record
Abstract
Abiotic stress such as high temperature at flowering is one of many conditions reducing yield of corn (Zea mays L.). Mixing corn cultivars with diverse functional traits increases within-crop diversity and provides a potential means of mitigating yield losses under stress conditions. We conducted a three-year field study to investigate the effects of cultivar mixtures on kernel setting rate, pollen sources, and yield. This study consisted of six treatments, including two high temperature-tolerant (HTT) monocrops of WK702 and DH701, two high temperature-sensitive (HTS) monocrops of DH605 and DH662, and two HTT–HTS mixtures of WK702-DH605 and DH701-DH662. The anthesis–silking interval (ASI) was 0.9–1.6 days shorter in mixtures than in monocrops. Kernel setting rate was increased in mixtures (86.4%–88.7%) compared with those in monocrops (74.7%–84.1%) as a result of synchrony and complementarity of pollination. Grain yields of the HTT–HTS mixtures increased by 13.3%–18.7%, equivalent to 1169 to 1605 kg ha−1, in comparison with HTS corn monocrops. The results of SSR markers showed that cross-fertilization percentage in corn cultivar mixtures ranged from 29.3% to 47.8%, partially explaining yield improvement. Land equivalent ratio (LER) was 1.12 for corn mixtures and the partial land equivalent ratio (e.g., > 0.5) showed the complementary benefits in corn mixtures. The results indicated that mixing corn cultivars with diverse flowering and drought-tolerance traits increased yields via pollination synchrony.
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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) | 0.001 | 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".