Response of Corn Grain Yield to Spatial and Temporal Variability in Emergence
Bibliographic record
Abstract
Potential yield benefits from improving within‐row plant spacing variability and plant emergence variability in corn (Zea mays L.) production are often questioned by growers. Research was conducted at two locations in south‐central Ontario during a 2‐yr period to quantify the effects and interactions of plant spacing variability and plant emergence variability on growth and grain yield of corn. Nine treatments were established by hand planting corn rows with repeating six‐plant sequences consisting of uniform and nonuniform spacing, even and uneven emergence, and their combinations. Spacing treatments consisted of (i) uniform within‐row plant spacing of 20 cm; (ii) one 40‐cm gap associated with a double; and (iii) one 60‐cm gap associated with a triple in each six‐plant sequence. Emergence treatments included uniform early emergence, a two‐leaf stage delay, and a four‐leaf stage delay for one plant in each six‐plant sequence. Only plant emergence variability significantly affected plant height, leaf area index (LAI), dry matter accumulation, and grain yield. Compared with the uniformly early emerged plants, one out of six plants with a two‐leaf stage delay in emergence reduced yield by 4%, and one out of six plants with a four‐leaf stage delay reduced yield by 8%. Whereas corn plants next to a gap demonstrated compensatory growth, plants adjacent to a late emerging corn plant did not exhibit compensatory growth. These results indicate that corn is more responsive to plant emergence variability than plant spacing variability. Variation in plant emergence reduced yield, whereas variation in within‐row spacing did not affect yield, and interactions between the two factors were not significant.
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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 | 0.001 |
| 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".