Effect of Seeding Rate and Weed Control on Glyphosate-Resistant Alfalfa Establishment
Bibliographic record
Abstract
The introduction of glyphosate-resistant (GR) alfalfa offers a new weed management system for alfalfa establishment; however, alfalfa seeding rates are based on conventional cultivars. Determining optimum seeding rates allows forage producers to maximize yield, quality, and profitability with GR alfalfa. Field experiments were established in 2005 and 2006 to determine the effect of seeding rate and weed control on GR alfalfa yield, forage quality, and persistence up to 3 yr after establishment. Seeding rates of 4.5, 9.0, and 18 kg ha−1were evaluated. Weed control methods during the seeding year included no herbicide, glyphosate applied once before the first harvest, and glyphosate applied once before the first harvest and then 7 to 10 d following subsequent harvests. Alfalfa yield was greater at higher seeding rates and when weeds were removed with glyphosate. Season forage yields were the greatest with the 18 kg ha−1seeding rate and where no herbicide was applied. Weed biomass often was lower at the higher seeding rates and was 91 to 98% lower in the glyphosate treatments compared to the nontreated. Forage quality was not affected by seeding rate but varied by herbicide treatment depending on establishment year. Plant density increased with seeding rate and treatment effects persisted for three growing seasons. Herbicide treatment did not affect stand density as greatly as seeding rate and did not influence stand longevity.
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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".