368 Effects of balancing feedlot diets for amino acid requirements and effective energy using rumen-protected lysine on growing steer performance
Bibliographic record
Abstract
The objective of this study was to evaluate differences in growth characteristics and feed efficiency of feedlot steers consuming varying levels of rumen-protected lysine. We hypothesized that steers consuming a diet optimized for effective energy (EE) and containing a rumen-protected product meeting the predicted lysine requirement would have greater feed efficiency and gain and lesser intakes than steers consuming diets formulated below or above the requirement. After a 3-wk adaptation period, crossbred steers (n = 120; 269 ± 23 kg) were stratified by BW and color, sorted into pens of 6, and fed for 75 d. Diets were balanced to meet EE requirements and not be limited by non-lysine AA. Treatments included a lysine-limiting control that contained no rumen protected products (NEGCON), a lysine-sufficient control that contained rumen-protected soybean meal (POSCON), a treatment that contained 50% of encapsulated lysine (Aji Pro 3G; Ajinomoto Heartland, Inc.) needed to meet the predicted lysine requirement (AJ50), a treatment that contained 100% of encapsulated lysine needed to meet the predicted lysine requirement (AJ100), and a treatment that contained 150% encapsulated lysine needed to meet the predicted lysine requirement (AJ150). The AJ50, AJ100, and AJ150 were predicted to provide 9.3, 18.6, and 37.3 g Aji Pro 3G/animal per day, respectively. Cattle were fed once daily and consumed feed ad libitum from GrowSafe feeders (GrowSafe Systems Ltd., Airdrie, AB, Canada), from which feed intake was measured daily. Data were analyzed using the PROC GLM procedure (SAS version 9.4; SAS Inst. Inc., Cary, NC). Initial BW (kg) did not differ across treatments (P = 0.85). Final BW (396 ± 29 kg) was significantly greater for AJ100 (408 kg) than for NEGCON (392 kg; P = 0.05) and AJ50 (392 kg; P = 0.05) and tended to be greater than POSCON (394 kg; P = 0.10) and AJ150 (393 kg; P = 0.08). Animal DMI (kg/d) did not differ across treatments (P = 0.57). However, DMI (% BW) was significantly lesser (P = 0.05) for AJ100 (1.50% BW) than for AJ150 (1.63% BW). Differences in ADG (kg) were not observed between treatments (1.68 in NEGCON, 1.65 in POSCON, 1.70 in AJ50, 1.84 in AJ100, and 1.70 in AJ150; P = 0.73.) No treatment differences in the F:G ratio (P = 0.61) were observed for AJ100 (4.32) versus NEGCON (5.15), POSCON (5.05), AJ50 (5.06), and AJ150 (5.13). When optimized for AA and EE requirements, AJ100 steers had statistically greater FBW and statistically lesser DMI (% BW) than steers consuming other treatments.
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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) | 0.001 | 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".