Invited review: Role of physically effective fiber and estimation of dietary fiber adequacy in high-producing dairy cattle
Bibliographic record
Abstract
Highly fermentable diets require the inclusion of adequate amounts of fiber to reduce the risk of subacute rumen acidosis (SARA). To assess the adequacy of dietary fiber in dairy cattle, the concept of physically effective neutral detergent fiber (peNDF) has received increasing attention because it amalgamates information on both chemical fiber content and particle size (PS) of the feedstuffs. The nutritional effects of dietary PS and peNDF are complex and involve feed intake behavior (absolute intake and sorting behavior), ruminal mat formation, rumination and salivation, and ruminal motility. Other effects include fermentation characteristics, digesta passage, and nutrient intake and absorption. Moreover, peNDF requirements depend on the fermentability of the starch source (i.e., starch type and endosperm structure). To date, the incomplete understanding of these complex interactions has prevented the establishment of peNDF as a routine method to determine dietary fiber adequacy so far. Therefore, this review is intended to analyze the quantitative effects of and interactions among forage PS, peNDF, and diet fermentability with regard to rumen metabolism and prevention of SARA, and aims to give an overview of the latest achievements in the estimation of dietary fiber adequacy in high-producing dairy cattle. Recently developed models that synthesize the effects of both peNDF and fermentable starch on rumen metabolism appear to provide an appropriate basis for estimation of dietary fiber adequacy in high-producing dairy cows. Data suggest that a period lasting more than 5 to 6h/d during which ruminal pH is <5.8 should be avoided to minimize health disturbances due to SARA. The knowledge generated from these modeling approaches recommends that average amounts of 31.2% peNDF inclusive particles >1.18mm (i.e., peNDF(>1.18)) or 18.5% peNDF inclusive particles >8mm (i.e., peNDF(>8)) in the diet (DM basis) are required. However, inclusion of a concentration of peNDF(>8) in the diet beyond 14.9% of diet DM may lower DM intake level. As such, more research is warranted to develop efficient feeding strategies that encourage inclusion of energy-dense diets without the need to increase their content in peNDF above the threshold that leads to lower DM intake. The latter would require strategies that modulate the fermentability characteristics of the diet and promote absorption and metabolic capacity of ruminal epithelia of dairy cows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".