108 Liquid feeding fermented DDGS to weanling pigs: improvement of growth performance with added enzymes and microbial inoculants
Bibliographic record
Abstract
Controlled fermentation of coproducts can improve energy availability and gut function through synergistic soluble fiber hydrolysis. This study assessed effects of extended DDGS fermentation on performance and digestive function of newly weaned piglets fed corn and soybean meal based liquid diets. Enzymes (67.2 IU β-glucanase and 51.4 IU xylanase/g DDGS; AB Vista) and silage inoculant (360,000 CFU Pediocoocus pentosaceus 12,455 and Propionibacterium jensenii 30,081/g DDGS; Lallemand Inc.) were added to dry DDGS at the time of liquid feed preparation and delivery (UNFER) or allowed to ferment with DDGS (1 to 7 d at 40°C; 16% DM; FER). Diets were composed of a common base supplement for each of three phases (P; d 0 to 7, 7 to 20, 20 to 42), mixed with DDGS (7.5 (P1), 16.25 (P2), and 25 (P3) % of DM) and water (25% DM). Pigs were separated into two rooms according to initial body weight (BWi; heavy (HBW, 7.6 ± 0.8 kg) or light (LBW, 5.8 ± 0.6 kg)). The study was a randomized block design with results presented as lsmeans ± SEM (FER vs. UNFER, respectively). Owing to a BWi by diet interaction (P < 0.05), data were analyzed separately for the two BWi groups (4 pens/BWi and dietary treatment, 14 pigs/pen). To obtain uniform final BW, LBW pigs were fed P3 diets until d 48. On d 42, pH and organic acid concentration were determined in ileal digesta pooled from 2 pigs/pen. Complete liquid FER diet (n = 9) had higher content of lactic acid (42.6 ± 17.4 vs. 17.6 ± 1.4 mM) and acetic acid (55.3 ± 37.1 vs. 3.9 ± 0.7 mM) than the UNFER diet (n = 3). Overall, there were no differences (P > 0.10) in ADG (424 vs. 424 ± 14 g/d for HBW and 404 ± 15 vs. 386 ± 12 g/d for LBW) and DMI (605 vs. 581 ± 16 g/d for HBW and 540 ± 19 vs. 509 ± 16 g/d for LBW). For d 42 to 48, LBW pigs fed FER had greater ADG (941 ± 60 vs. 773 ± 52 g/d, P < 0.05), resulting in higher end BW (25.8 ± 0.5 vs. 24.5 ± 0.4 kg, P < 0.05). In digesta, total organic acid concentration and pH did not differ between treatments (P > 0.10). Digesta fermentation patterns (% of total organic acids), however, differed with FER increasing n-butyric acid (15.0 vs. 1.0 ± 3.8%, P = 0.04) and tending to lower lactic acid (30.0 vs. 47.1 ± 6.9%, P = 0.06) within HBW, while within LBW, FER tended to increase acetic acid (53.7 ± 7.4 vs. 31.1 ± 6.4%, P = 0.07). FER benefited LBW pigs late in the nursery period, altering the gut metabolome, possibly due to soluble fiber hydrolysis and improved gut development in pigs potentially compromised by low weaning BW.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".