Interactions Between Herbicide Application and Timing of Nitrogen Fertilization in Maize Hybrids
Bibliographic record
Abstract
Maize hybrids have different levels of tolerance to the herbicide nicosulfuron, depending on the dose, the environment, the phenological stage of the plant and the timing of nitrogen fertilization, and phytotoxic effects can occur without proper management. There is also limited information on the selectivity of current genetically modified hybrids for other recommended herbicides, such as tembotrione. Thus, the objective of this study was to evaluate the effects of herbicides (tembotrione and nicosulfuron) and the timing of nitrogen fertilization on the yield of maize hybrids. The experimental design was a randomized block design, in a 4 × 2 factorial arrangement, with 4 replicates. The treatments consisted of four treatments [weeding; nicosulfuron + atrazine (20 and 32 + 1250 g ha-1 ai) and tembotrione + atrazine (75.6 + 1250 g ha-1 ai)] and two nitrogen fertilization times (0 and 7 days after application). Maize hybrids P30F53 Leptra, DKB 230PRO3 and KWS 9004PRO2 were assessed in two growing seasons (1st and 2nd harvest) during the 2016/2017 growing season. The percentages of weed control; phytotoxicity at 7, 14 and 21 days after application (DAA); plant height; number of rows per ear; and yield at harvest were evaluated. All herbicides were more effective in controlling weeds at the 1st harvest because of favourable climatic conditions. Nitrogen fertilization can be carried out on the same day as the application of the herbicides nicosulfuron and tembotrione with no reduction in yield for the hybrids P30F53 Leptra, DKB 230PRO3 and KWS 9004PRO2.
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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".