Effects of plant growth regulator application on the malting quality of barley
Bibliographic record
Abstract
BACKGROUND: Lodging can negatively affect yield and quality of barley grain. Synthetic plant growth regulators (PGRs) reduce lodging by producing shorter, thicker, and stronger stems. However, the impact of applying PGRs on malting performance of barley is not known. The objective of this work was to assess the effect of application of three PGRs (ethephon, chlormequat chloride, and trinexapac-ethyl) in combination with different seeding rates on the malting quality of barley grown in several locations and years in western Canada. RESULTS: The kernel weight in PGR-treated barley was reduced by 1.7% to 6.5% compared with the nontreated grain. Application of PGRs had no effect on the concentration of proteins and germination energy. Seeding rates significantly affected kernel weight, protein content, and germination index (GI), but no interactions between PGRs and seeding rates were observed. The smaller kernels of ethephon- and trinexapac-treated barley showed good hydration and grain modification during malting, as indicated by high levels of starch-converting enzymes, high Kolbach indices, and low levels of wort β-glucans. Overall, the fine extract of malt from PGR-treated barley was slightly lower than that of the control malt; however, the extract reduction was statistically significant only for chlormequat- and trinexapac-treated barley. CONCLUSIONS: The application of PGRs had significant effects on kernel plumpness and kernel weight, but the effects of PGR application on the malting quality were generally small and insignificant. The decision of PGRs application on malting barley needs to be considered in combination with potential benefits of PGRs in mitigating lodging and their effects on the agronomic performance of barley. © Her Majesty the Queen in Right of Canada 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".