PSX-A-1 Impacts of Undigestible NDF and Grain Source in Feedlot Steers on Performance, Feeding Behavior, and Ruminal pH
Bibliographic record
Abstract
Abstract Little is known about how fiber quality impacts rumen health in feedlot diets with minimal forage inclusion. The objective of the study is to investigate the interaction between undigestible NDF (uNDF) levels and grain fermentability (GF) on animal performance, feeding behavior, and ruminal pH in feedlot steers. Using a 2x2 factorial design, Angus crossbred steers (n=96, 505±54 kg) were fed an isonitrogenous and isocaloric diet containing: high uNDF (haylage; 12% uNDF) or lower uNDF (corn silage, 6% uNDF) and high (high moisture corn) or lower ruminally fermentable grain (rolled corn) yielding 4 different treatments. Following adaption, steers were fed their treatment ration for 102±14d before slaughter. Feed intake (DMI) and feeding behavior data were collected using Insentec feeders and continuous ruminal pH was measured during the last 4 weeks before slaughter using indwelling pH probes (12 steers/treatment). Liver abscesses and gut health scores were assessed at slaughter. Steers on the low uNDF diet had greater ADG (P≥0.005) and improved F:G (P≥0.005) for the first half of the finisher period. No other differences in performance traits were found for the second half or overall finisher period between uNDF and GF, or their interaction. DMI (P≥0.64) did not differ, but time spent at the feeder (min/day) was greater (P≥0.02) for low GF steers. Steers fed low uNDF had greater (P≥0.04) time per day spent with ruminal pH below pH 5.8. At slaughter, steers fed high GF treatments had greater hot carcass weights (P≥0.03). This data suggests that low uNDF can improve early gain but may result in increased SARA risk compared to high uNDF at the same forage inclusion level. However, there was limited interaction with grain processing. Further research should continue to investigate relationships between uNDF and ruminal pH, and dietary uNDF concentrations that optimize performance without compromising gut health.
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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 | 0.001 | 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".