Trinexapac-ethyl as an Alternative to Reduce Lodging and Preserve Grain Yield and Quality of Rye
Bibliographic record
Abstract
Trinexapac-ethyl (henceforth referred to as trinexapac) is a growth regulator that can mitigate some undesirable vegetative growth in Poaceae plants, reducing lodging and optimizing the distribution of photoassimilates to reproductive organs. The objective of this research was to evaluate the morphophysiological and productive responses of rye cultivars and trinexapac doses applied at different phenological stages of plants. Two field experiments were performed in two different seasons. In the 2015 growing season, 8 doses of trinexapac were evaluated: 0, 75, 85, 100, 115, 130, 150, 200 g ha-1 of active ingredient (a.i.), applied at the plant growth stage with the 4th node visible on the main stem (GS34). In the 2018 growing season, two cultivars (IPR 89 and BRS Serrano) and five doses of trinexapac were evaluated: 0, 50, 100, 150 and 200 g a.i. ha-1, applied at the plant growth stage with the 6th node visible on the main stem (GS36). The morphophysiological, productive and qualitative characteristics of grains were evaluated. In the 2015 season, trinexapac applied to shoot plants at GS34 only have low response in decreased plant lodging from around 20% to 10%. However, in the 2018 season, the application of trinexapac on plants at GS36, resulted in lower plant height, associated with reduced lodging and better quality of rye grains. Cultivar IPR 89 showed higher values for yield components and grain quality in comparison to cultivar BRS Serrano. Reduced lodging is dependent on speed of resumption of plant height growth, and the magnitude of response to trinexapac is also stage-cultivar-dose-dependent; in rye, the best response to trinexapac was found at GS 36 in comparison to GS 34, and for IPR 89 in comparison to BRS Serrano cultivar, respectively, with a dose range from 100 to 150 g ai ha-1. In conclusion, trinexapac (around 150 g a.i. ha-1), when applied to plants at GS36, reduces plant height, reduces the lodging index and enables the harvest of high-quality grains.
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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) | 0.002 | 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".