Mechanisms of Yield Loss in Maize Caused by Weed Competition
Bibliographic record
Abstract
The physiological process underlying grain yield (GY) loss in maize as a result of weed competition is not understood clearly. We designed an experiment to test the hypotheses that early season stress caused by the presence of neighboring weeds will increase plant-to-plant variability (PPV) of individual plant dry matter (PDM) within the population. This increase in PPV will reduce GY through a reduction in harvest index (HI). Field experiments were conducted in 2008, 2009, and 2010. A glyphosate-resistant maize hybrid was cropped at a density of 7 plants m−2. As a model weed, winter wheat was seeded at the same time as maize and controlled with glyphosate at the 3rd or 10th to 12th leaf-tip stage of maize. Weed competition early in the development of maize decreased PDM and GY. This reduction in PDM, which occurred early in the development of maize, was attributed initially to a delay in rate of leaf appearance. Reductions in PDM were accompanied by an increase in PPV of PDM. This increase in PPV, however, did not reduce HI and did not contribute to the GY reductions created by weed competition, as hypothesized. As weed control was delayed, a reduction in fraction of photosynthetically active radiation (fIPAR) accounted for a further reduction in PDM and notably, a reduction in DMA from 17th leaf-tip stage through to maturity. The rapid loss of PDM and the subsequent inability to accumulate dry matter during maturation accounted for a rapid decline in kernel number (KN) and kernel weight (KW).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".