Delaying Weed Control Lengthens the Anthesis-Silking Interval in Maize
Bibliographic record
Abstract
Stress caused by early weed competition is known to delay the rate of maize development which may result in a decrease in kernel number. Kernel number in maize is correlated negatively with the length of the anthesis-silking interval (ASI). A short ASI has been identified as an easily measured, visual trait which may identify enhanced drought tolerance in maize. Field studies were conducted to test whether: (1) delaying weed control would result in a lengthening of ASI in both a drought tolerant and non-drought tolerant maize hybrid and (2) the presence of drought tolerance genetics comes at a physiological cost, resulting in a greater yield reduction under weedy conditions. In this study, the response of a drought tolerant hybrid with its non-drought tolerant near-isoline was compared to seven different timings of weed control using wheat as a surrogate competitor. Results confirmed that there was no treatment by hybrid interaction at any site–yr for any of the parameters evaluated. Delaying weed control reduced plant height, leaf tip number, shifted and reduced biomass accumulation, kernel number and grain yield and lengthened ASI for both hybrids. Although yield losses occurred with the delay in weed control timing, no yield differences were observed between hybrids suggesting that there was no additional physiological cost associated with the drought tolerant traits. The drought tolerant hybrid, however, was found to have a shorter ASI, lower kernel number and higher kernel wt compared to the non-drought tolerant hybrid. This study confirmed that delaying weed control can influence the length of ASI, which is an important drought tolerant trait. The lengthening of ASI by early weed competition resulted in a rate of yield loss of 0.13 T ha−1growing degree days (GDD)−1when averaged across both hybrids and all treatments.
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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) | 0.000 | 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 | 0.001 |
| 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".