FORAGE PRODUCTION AND QUALITY OF SMOOTH BROMEGRASS (BROMUS INERMIS) ON A VEGETATED TREATMENT AREA (VTA)
Bibliographic record
Abstract
Beef cattle (Bos taurus) feedlots pose serious environmental challenges associated with nutrient runoff. Smooth bromegrass is a perennial rhizomatous grass that is widely used in USA and Canada for forage production. The objective of this research is to determine the best management system for producing forage from a vegetated treatment area (VTA) while maintaining the capacity of the VTA to remove nutrients from feedlot effluent. Four harvest management treatments (1-, 2-, and 3-harvest per year and an un-harvested control) were applied during spring 2005 and evaluated over a 3-yr period in a smooth bromegrass sward on a VTA near Howard, SD. Forage production during 2006 ranged from 4.5 Mg ha -1 to 8.5 Mg ha -1 for 1- and 3-harvest systems, respectively. Nutrient removal by the bromegrass was 83 kg ha -1 N and 8 kg ha -1 P for the 1-harvest treatment and 193 kg ha -1 N and 22 kg ha -1 P for the 3-harvest treatment. This indicated that high amounts of forage could be produced from VTAs and that smooth bromegrass was an effective procurer of N and P. Differences were found among harvest treatments for the first harvest during 2007, with the 1-harvest treatment producing 6.0 Mg ha -1 compared with 4.1 Mg ha -1 for the 3-harvest treatment. This indicated that multiple harvests during a growing season could weaken the sward over time and have a negative effect on the runoff capture characteristics of the sward. At the termination of the study in 2008, soil cores will be taken to determine the effect of different harvest treatments on masses of roots and rhizomes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".