234 Effects of Feeding a Phytogenic Blend with or without Supplementation of Specialty Fats on Growth Performance of Nursery Pigs
Bibliographic record
Abstract
Abstract An experiment was conducted to test the hypothesis that inclusion of a phytogenic feed additive (Fresta Protect, Delacon, Linz, Austria), separately or in conjunction with specialty fats (modified soy oil blend and medium chain fatty acid blend), in diets for nursery pigs would improve growth performance. Weaned pigs [n = 4,400; 5.7 ± 0.1 kg body weight (BW)] were allotted to 50 replicate pens per treatment (20 to 23 pigs per pen) in 5 barns. Pens were blocked by location within barn and randomly allotted to 4 treatments in a 2 x 2 factorial arrangement. Treatment main effects were specialty fats inclusion in phase 1 (0 or 1.3%) and phytogenic inclusion in phase 1 and 2 (0 or 0.1%). Pigs were fed experimental diets during phase 1 and 2 (day 0 – 10 and 10 – 21, respectively), and pig and feed weights were recorded at the beginning and end of each phase. Data were analyzed using the lme4 package of R 4.1.2, and the statistical model included the fixed effects of specialty fats, phytogenic, and their interaction, as well as the random effects of barn and location within barn. In phase 1, an interaction was detected (P < 0.05) for final BW, average daily gain (ADG), average daily feed intake (ADFI), and gain:feed (G:F; Table 1). In general, phase 1 performance was improved by phytogenic inclusion when no specialty fats were present in the diet, but pigs did not benefit from inclusion of the phytogenic when fed in combination with specialty fats. Body weight at the end of phase 2 was reduced (P < 0.05) for pigs fed specialty fats, but inclusion of the phytogenic increased (P < 0.05) phase 2 final BW and ADG regardless of specialty fat inclusion. Supplementation of the phytogenic increased (P < 0.05) phase 2 G:F as well, and the improvement in G:F was more pronounced when fed in conjunction with specialty fats, though this observation may be partially confounded by compensatory gain for the specialty fats and phytogenic combination treatment. The phytogenic also increased (P < 0.05) overall ADG and G:F, whereas the main effect of specialty fats reduced (P < 0.05) overall ADG and G:F. In conclusion, dietary inclusion of a phytogenic feed additive improved growth performance of nursery pigs, whereas specialty fats decreased growth performance, and the best performance was achieved when the phytogenic was fed in the absence of specialty fats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".