Effects of Annual and Perennial Forage Systems on Forage Biomass and Quality, Grazing Animal Performance and Enteric Emissions, and System Economics
Bibliographic record
Abstract
Two experiments were conducted to evaluate annual and perennial forage systems with respect to forage yield and quality, grazing animal performance, rumen fermentation, enteric methane production, and system economics. Forage systems included (1) CDC Austenson barley (Hordeum vulgare L.) + 4010 pea (Pisum sativum L.) + Winfred forage brassica (Brassica oleracea L.× Brassica rapa L.) + Gorilla forage brassica (Brassica napus L.) (BRPEBRS), (2) AC Hazlet fall rye (Secale cereale L.) + Frosty berseem clover (Trifolium alexandrinum L.) (FRCLOV), (3) AC Success hybrid bromegrass (Bromus riparius Rehm. × Bromus inermis Leyss.) + PS3006 alfalfa (Medicago sativa L.) (HBGALF), and (4) AC Armada meadow bromegrass (Bromus riparius Rehm.) + AAC Mountainview sainfoin (Onobrychis viciifolia Scop.) (MBGSF). Experiment 1 was conducted over a 2-yr period (2020 and 2021). Each yr, Bos taurus crossbred steers (n = 156, initial body weight = 364 ± 17 kg) were randomly allocated to 1 of 4 forage systems (n = 3 replicates/treatment) for 54 ± 21 d grazing period each year. Dry matter yield (DMY) of HBGALF was greater (P < 0.01; 3046 kg ha-1) than all other treatments. Annual forage systems had greater (P < 0.01) total digestible nutrients (64.8 vs. 58.6% TDN), greater (P < 0.01) crude protein (13.7 vs. 10.2% CP), less (P < 0.01) neutral detergent fibre (46.9 vs. 59.5% NDF) and greater (P < 0.01) digestibility (74.9 vs. 61.85% NDFD30hr) than perennial forage systems. Steers grazing FRCLOV had greater (P < 0.01) DMI than all other treatments. Total beef production was greatest (P = 0.03) for HBGALF (124 kg ha-1). Enteric methane results were not conclusive. Economic net returns were also greatest (P < 0.01) for HBGALF. In Experiment 2, the in vitro ruminal fermentation of the forage treatments were investigated using the rumen simulation technique (RUSITEC). Annual forage systems had the greatest (P < 0.01) nutrient disappearance, greatest (P < 0.01) total volatile fatty acid (VFA) production (99 vs. 92 mmol d-1), and greatest (P< 0.01) ammonia-nitrogen (NH3-N) production (48.5 vs. 33.8 mg d-1). These fermentation parameters support improved growth performance in ruminants grazing annual forage systems however, DMY of perennial forage systems had a larger impact on total beef production and profitability.
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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) | 0.000 | 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".