Appropriate Crop Seeding Rate When Herbicide Rate is Reduced<sup>1</sup>
Bibliographic record
Abstract
A study was conducted at three locations in Saskatchewan, Canada, in 1996 and 1997 to determine if increasing the seeding rate of wheat, barley, and lentil by 50% would maintain weed control and crop yield when herbicides are applied at reduced rates or not at all. Three herbicide rates (½ of full, ¾ of full, and full recommended label rate), along with an untreated check, two crop seeding rates (normally recommended and 1.5 times normally recommended rates), and three crops were tested. Increasing seeding rate did not affect weed fresh weights, crop yield, and net return responses to herbicides applied at reduced rates or not at all when averaged across crops, years, and locations. Increased seeding rate, independent of the different herbicide applications, had infrequent and inconsistent effects among the crop by year by location combinations. More broadleaf and grass weed growth, less crop yield, and lower net returns generally occurred when herbicides were not applied or applied at reduced rates. These trends were especially prominent when herbicides were not applied to cereal crops at Saskatoon (40% yield reduction) and when herbicides were applied at ½ the full label rate rather than higher herbicide rates to wheat at the other two locations (16% yield reduction). In 1996, lentil yield and net returns did not respond to herbicide application and rate because of poor grass weed control across all herbicide rates. Lentil yield and net returns decreased by 11% (full vs. ¾), 22% (¾ vs. ½), and 46% (½ vs. none) when herbicides were applied at progressively lower rates in 1997. Reduced herbicide rates did not affect net returns for cereal crops, indicating that herbicide rates lower than the full label rate may be economically viable in certain crops.Nomenclature: Barley, Hordeum vulgare L.; hard red spring wheat, Triticum aestivum L.; lentil, Lens culinaris Medic.Additional index words: Integrated weed management, weed interference, economic return.
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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) | 0.001 | 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".