Effects of multienzyme supplementation on energy and nutrient digestibility in various feed ingredients for pregnant gilts
Bibliographic record
Abstract
The utilization of exogenous fiber-degrading enzymes in commercial swine diets is a strategy to increase the nutrient and energy density of poorly digestible ingredients. In a prior set of studies, dietary multienzyme blend (MEblend) supplementation increased the apparent total tract digestibility (ATTD) of nutrients, non-starch polysaccharides, and energy in complete high-fibrous gestation diets by 6% when fed to gestating sows. The current study aimed to determine the effects of MEblend (containing xylanase, β-glucanase, cellulase, amylase, protease, pectinase, and invertase activities) supplementation on ATTD of energy and nutrients of individual feedstuffs commonly used in gestating sow diets across major pork-producing regions worldwide, which differ in their fibrous components. Twenty-seven gilts (initial body weight 176 ± 6.6 kg), in a crossover design with 4 periods (periods 1, 2, 3, and 4 from days 41 to 55, 56 to 70, 71 to 85, and 86 to 100 of gestation, respectively), were allocated to one of 7 diets (with or without MEblend supplementation at 0.1% inclusion; 7 to 8 observations per treatment) to determine the ATTD of energy and neutral detergent fiber. Three diets contained corn, wheat, and sorghum as the sole source of energy. In the other diets, soybean meal (SBM), field peas (FP), canola meal (CM), and sugar beet pulp (SBP) each replaced 25% of the corn in the corn diet to determine the energy value of individual feedstuffs. Data were analyzed using a Student's t-test to evaluate the effect of enzyme supplementation on these feedstuffs. The MEblend increased the metabolizable and net energy of corn (P = 0.10) and wheat (P < 0.01) by 2% and 3%, respectively. The energy content of sorghum was not impacted by MEblend. Furthermore, a 6%, 4%, and 25% increase was observed in metabolizable and net energy of SBM, FP, and CM, respectively (P ≤ 0.05). The energy value in SBP was not affected by MEblend supplementation. In conclusion, supplementing diets with a multienzyme blend increased the energy content of corn, wheat, soybean meal, FP, and CM fed to gestating sows by approximately 2% to 25%, depending on the feedstuffs. The energy value of sorghum and SBP was not affected by the multienzyme blend. This should be considered when formulating fibrous diets for gestating sows to increase nutrient utilization of feedstuffs.
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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 | 0.000 | 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".