Harvest time and N fertilization effects on forage yield and quality of quackgrass (<i>Elytrigia repens</i> L.) in northeastern Saskatchewan
Bibliographic record
Abstract
Quackgrass [Elytrigia repens (L.) Beauv.] is a primary noxious weed, but some cattle producers are discovering its value as forage for early-season grazing and for winter feeding as hay. Little information is available on how its production and quality change with advancing maturity and on its response to improved management in the Parkland zone of the Canadian prairies. The objective of this study was to determine the effects of harvest date and N fertilizer (surface-broadcast ammonium nitrate at 0, 56, 112 and 168 kg N ha-1) on the forage yield and quality of quackgrass. The study was done on a 10-yr-old quackgrass (˜ 90%) dominated stand on a silty clay loam (Dark Gray Luvisol) soil near Tisdale in northeastern Saskatchewan. For the harvest dates between early June and early September, maximum dry matter yield (DMY) was in August and maximum protein yield (PY) occurred in July. A delay in harvest reduced protein concentration (PC) and total digestible nutrients concentration (TDN), while it increased acid detergent fiber concentration (ADF). The DMY, PC and PY increased with increasing N rate for both Cut 1 (in early July) and Cut 2 (in late September). Strong quadratic relationships were observed between DMY and N rate. The effect of N application was relatively greater on PY than DMY due to the cumulative effect of increases in DMY and PC, and was greater in Cut 1 than in Cut 2. With the increase in N rate, TDN showed a trend of small increase, while ADF tended to decrease. In summary, the results show that N fertilization increases both forage yield and quality of quackgrass. Harvesting in late July or August is likely to provide maximum DMY when one harvest per season is taken. Key words: Acid detergent fiber, dry matter, forage, harvest date, hay, N fertilization, protein, quackgrass, total digestible nutrients
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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 | 0.000 |
| 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".