Effects of fiber concentrations and fermentation rates on reproductive performance, nutrient digestibility, immune response, and microbiota of lactating sows
Bibliographic record
Abstract
Certain types of fiber inclusion in sow diets can offer benefits such as promoting a healthy microbiome, improving intestinal health, and enhancing welfare through satiety mechanisms. While most research has focused on gestating sows, studies on lactating sows, especially regarding fiber types, levels, and fermentation rates, are limited. This study explored how fiber inclusion and fermentation rate affect lactation performance, nutrient digestibility, immune response, and microbiota. The study involved 136 lactating sows (Landrace × Large White) assigned to four dietary treatments with varying total dietary fiber (TDF) levels and proportions of fast-fermentable fiber. Diets 1 and 2 had low TDF (15%), while Diets 3 and 4 had high TDF (25%). Fast-fermentable fiber was included at 1.25% and 2.5% in the low-fiber diets, and at 3.75% and 5% in the high-fiber diets. Fiber sources like sugar beet pulp, oat hulls, wheat millrun, and barley were used based on their fermentable fiber proportions, measured using an in-house Trouw Nutrition assay. Effective energy, crude protein (CP), and lysine contents were consistent across diets. Sows were fed starting at 3 kg/d (primiparous) or 3.5 kg/d (multiparous), increasing to 7 kg/d or 8 kg/d by 8 or 9 d postfarrowing. High-fiber diets reduced apparent total tract digestibility (ATTD) of nutrients (P < 0.001) but increased sow fat loss (P < 0.05). Sows consuming fast-fermentable fiber had lower body weight (BW) and protein loss (P < 0.05), decreased serum interleukin-8 (IL-8) levels (P < 0.05), and improved ATTD of gross energy (P = 0.016), dry matter on day 19 (P < 0.001), CP (P < 0.001), and nitrogen (P < 0.001) and a trend toward higher fat levels in milk (P = 0.099). High-fiber diets decreased microbiota diversity but increased beneficial families such as Prevotellaceae, Ruminococcaceae, and Lachnospiraceae. Although high-fiber diets lowered ATTD of nutrients, feeding these diets during lactation improved sow performance without negatively affecting feed intake, milk composition, microbiota and did not impact litter weaning weight. Fast-fermentable fiber also reduced IL-8 levels, BW loss, and protein loss, suggesting it benefits lactating sows.
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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 | 0.001 |
| 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 | 0.000 | 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".