Why Early Season Weed Control Is Important in Maize
Bibliographic record
Abstract
Control of early-emerging weeds is essential to protect the yield potential of maize. An understanding of the physiological changes that occur as a result of weed interference is required to address variability in yield loss across sites and years. Field trials were conducted at the University of Guelph (UG), the Ohio State University (OSU), and Colorado State University (CSU) during 2009 and 2010. There were six treatments (season-long weedy and weed-free, and weed control at the 1st-, 3rd-, 5th-, and 10th-leaf-tip stages of maize development) and 20 individual plants per plot were harvested at maturity. We hypothesized that, as weed control was delayed, weed interference in the early stages of maize development would increase plant-to-plant variability in plant dry-matter accumulation, which would result in a reduction of grain yield at maturity. The onset of the critical period for weed control (CPWC) occurred on average between the third and fifth leaf tip stages of development (i.e., V1 to V3, respectively). Rate of yield loss following the onset of the CPWC ranged from 0.05 MG ha−1d−1at UG 2009 to 0.22 MG ha−1d−1at CSU 2010 (i.e., 0.5 and 1.6% d−1, respectively). On average, reductions in kernel number per plant accounted for approximately 65% of the decline in grain yield as weed control was delayed. Biomass partitioning to the grain was stable through early weed removal treatments, increased and peaked at the 10th-leaf-tip time of control, and decreased in the season-long weedy treatment. Plant-to-plant variability in dry matter at maturity and incidence of bareness increased as weed control was delayed. As weed control was delayed, the contribution of plant-to-plant variability at maturity to the overall yield loss was small, relative to the decline of mean plant dry matter.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".