Impact of Planter Type, Planting Speed, and Tillage on Stand Uniformity and Yield of Corn
Bibliographic record
Abstract
Planter type, maintenance, and operation play an important role in uniform stand establishment in corn (Zea mays L.). Research was conducted to determine if planter type affects corn yield by altering plant spacing and emergence variability and to determine if planting speed and tillage influence these effects. This experiment was performed at two locations in south‐central Ontario during a 2‐yr period. Treatments were established with conventional tillage (CT) and no‐tillage (NT) as main plots, three planter types (vacuum meter, finger‐pickup, and air seeder) with differing mechanisms including varied seed‐singulating mechanisms as subplots, and two planting speeds of 7.2 and 11.3 km per hour (kph) as sub‐subplots. Planter type affected stand uniformity with mean standard deviation (SD) of within‐row plant spacing of 7.9, 10.3, and 19.9 cm for vacuum meter, finger pickup, and air seeder, respectively. A higher SD was observed in NT for finger pickup and air seeder but remained the same for vacuum meter. For all planters, SD increased at faster planting speeds. The number of days required to achieve 50% emergence was similar between the vacuum meter and finger pickup but was greater for the air seeder, especially when planting speed was increased and NT was used. Final plant population was unaffected by planter and planting speed treatments. Overall, grain yields decreased 35.9 kg ha−1 for each centimeter increase in within‐row plant spacing SD and 292.8 kg ha−1 per day of delay in emergence. Results suggest that grower’s attention to corn planter mechanisms and maintenance is more critical under a NT system or when operating speeds are increased.
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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 | 0.001 | 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".