Ear Position, Leaf Area, and Contribution of Individual Leaves to Grain Yield in Conventional and Leafy Maize Hybrids
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Bibliographic record
Abstract
The contribution of individual leaves and the extra leaves above the ear to dry matter (DM) accumulation and grain yield in Leafy maize ( Zea mays L.) is not well documented. A field experiment was conducted for two growing seasons (2003 and 2004) at Ottawa, Canada, to determine whether additional leaves above the ear in a Leafy hybrid contribute more to grain yield than in a conventional hybrid and to assess the importance of individual leaves above and below the ear. At silking, 10 defoliation treatments were imposed in a conventional (Pioneer 3893) and a Leafy (Maizex LF850‐RR) hybrid. Total number of leaves per plant, position of the primary ear height, and the area and DM of each removed leaf were measured at silk stage. At physiological maturity, number of kernels per plant, kernel DM, and whole plant DM were determined. Despite the Leafy hybrid having a 25% greater number of leaves, 26 to 40% more green leaf area, and 16 to 41% more total plant DM at silking than the conventional hybrid, there was no difference in total DM and grain yield at physiological maturity, indicating a situation of sink limitation in the Leafy hybrid. Removal of all leaves below the earleaf and earleaf alone in the conventional hybrid caused 19 to 26% and 17 to 25% reduction in grain yield, respectively, while there was no any notable effect of these treatments in the Leafy hybrid. When all leaves above the earleaf were removed, kernel number and kernel DM were reduced by 84 to 94% in the Leafy hybrid compared with a 40 to 50% reduction in the conventional hybrid. We conclude that the large number of leaves in the Leafy maize gave no additional advantage in terms of grain yield and total DM production and the earleaf and leaves below the ear‐node were of less importance in the Leafy hybrids than in the conventional hybrid.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it