Agronomic Management Strategies to Reduce the Yield Loss Associated with Spring Harvested Corn in Ontario
Bibliographic record
Abstract
Some growers in northern corn (Zea mays L.) producing regions forgo the typical autumn harvest for various reasons, but not without the risk of significant yield loss. Therefore, strategies are needed for managing the risks to yield when harvesting corn in spring. Field experiments, with various management strategies, were initiated in Ontario, Canada near Belmont and Ridgetown in 2009 and near Belmont, Ridgetown, and Lucan in 2010. Management strategies investigated the use of hybrids with a range in maturity, the use of standard and reduced plant populations, and the use of a foliar fungicide applied around tasseling. The parameters examined were stay-green in autumn, lodging in spring, and grain yield, moisture, and test weight of corn harvested in autumn and spring. Standard corn production practices consist of using a full-season hybrid planted at 80,000 plants·ha-1 with no late-season fungicide application; however, if over-wintered at Belmont, corn managed using these practices resulted in a 23.1% yield loss (12.1 vs 9.3 Mg·ha-1) averaged across years when the crop was harvested in the spring. An overwintering management strategy for corn was identified, which consisted of planting at a reduced plant population (60,000 plants·ha-1) and spraying the crop with QUILT® (azoxystrobin + propiconazole at 200 g a.i. ha-1) at the VT to R1 growth stage. Averaged across all hybrids, this strategy minimized yield losses through improvements on corn standability with only a 3.5% yield loss at Ridgetown and a 13.2% yield loss at Belmont. Furthermore, grain test weights for corn with the overwintering strategy were similar to or greater than corn overwintered with the standard production practice. However, weather conditions have the potential to overwhelm any management strategy. In spite of the favorable data indicating reduced risks with a spring harvest, lodging was still higher than expected and yield losses would likely be unacceptable for most growers to make a spring corn harvest a widely accepted practice, unless autumn grain moistures are extremely high, drying charges are high, and if stalk strength going into the winter was exceptional.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".