Effect of perennial forage system on forage characteristics, soil nutrients, cow performance, and system economics
Bibliographic record
Abstract
Two perennial forage systems were evaluated in a 3-yr study for their effect on forage biomass and nutritive value, botanical composition, soil nutrients, forage disappearance, beef cow performance, and economic analysis. Spring-calving, dry, pregnant Bos taurus beef cows [yr 1 (n = 60), 632 ± 8 kg; yr 2 (n = 60), 638 ± 5 kg; yr 3 (n = 48), 653 ± 5 kg] were managed in 1 of 2 replicated (n = 3) forage systems: (1) grazing stockpiled perennial forage [TDN = 52.5, CP=10.7 (%DM); SPF] in field paddocks or (2) drylot pen feeding round bale hay [TDN = 52.7, CP=10.0 (%DM); HY]. Forage utilization was greater ( P = 0.01) for HY cows in all years (94%) compared with yr-1 and yr-2 SPF cows (58 and 78%, respectively). Forage disappearance was greater ( P = 0.01) for yr-3 SPF cows than HY system cows; however, supplement was greater ( P = 0.01) for SPF cows compared with HY cows during the study period. Soil NO 3 -N ( P = 0.02) and organic carbon ( P = 0.01) amounts at the 0- to 30-cm soil depth were greater in SPF paddocks than HY paddocks. Body weight and BCS did not differ ( P > 0.05) for cows in either SPF or HY systems. Averaged over 3-yr, SPF total system costs were 14% less ( P = 0.01) compared with the HY system. Results suggest field grazing stockpiled perennial forages in western Canada can be a viable strategy without any negative effect to beef cow performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".