Role of Feed Processing on Gut Health and Function in Pigs and Poultry: Conundrum of Optimal Particle Size and Hydrothermal Regimens
Bibliographic record
Abstract
The current feed processing technologies are such that ingredient choices and diet form are refined to improve feed intake and nutrient utilization efficiency. Finer feed particle size enables optimal nutrient utilization and enhances animal performance due to increased surface area allowing better contact with digestive enzymes. Moreover, adequate diminution of feed ingredients is beneficial to feed manufacturing processes such as mixing and hydrothermal processes including pelleting, extrusion and expansion. However, emerging trends in consumer and regulatory demands for restriction or cessation of animal production practices such as use of antimicrobial growth promoters are challenging current approaches to feed processing. For example, there is limit as to the fineness of the particle size, as very fine particles negatively affect gut health due to higher incidences of stomach ulceration in pigs and gizzard dysfunction in poultry. Coarse particle size increases stomach and hindgut acidification which may be beneficial in controlling proliferation of enteric pathogens such as salmonella and E. coli. Optimal particle size could be designed in the grinding process using roller or hammer mill. However, since most commercial pigs and poultry diets are subjected to hydrothermal processes, additional reduction of feed particle size is inevitable. The need to achieve high physical quality and to reduce potential levels of feed-borne pathogens such as Salmonella has led to the application of relatively high conditioning temperatures during conventional hydrothermal processes, a practice that does not favour high nutrient utilisation and stability of heat sensitive feed additives such as feed enzymes. Therefore, with evolving pig and poultry production practices, the quality of feed products will no longer be appreciated only in terms of supplying nutrients, but also in terms of impact on feed hygienic status, efficacy of feed additives, animal health and food safety. The aim of the paper is to give an overview of available literature data on the role of feed processing on gut health and function with specific focus on particle size and hydrothermal processing. In addition, influence of feed processing on efficacy of exogenous feed enzymes will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".