Nutritive value of maize silage in relation to dairy cow performance and milk quality
Bibliographic record
Abstract
Maize silage has become the major forage component in the ration of dairy cows over the last few decades. This review provides information on the mean content and variability in chemical composition, fatty acid (FA) profile and ensiling quality of maize silages, and discusses the major factors which cause these variations. In addition, the effect of the broad range in chemical composition of maize silages on the total tract digestibility of dietary nutrients, milk production and milk composition of dairy cows is quantified and discussed. Finally, the optimum inclusion level of maize silage in the ration of dairy cows for milk production and composition is reviewed. The data showed that the nutritive value of maize silages is highly variable and that most of this variation is caused by large differences in maturity at harvest. Maize silages ensiled at a very early stage (dry matter (DM) < 250 g kg(-1)) were particularly low in starch content and starch/neutral detergent fibre (NDF) ratio, and resulted in a lower DM intake (DMI), milk yield and milk protein content. The DMI, milk yield and milk protein content increased with advancing maturity, reaching an optimum level for maize silages ensiled at DM contents of 300-350 g kg(-1), and then declined slightly at further maturity beyond 350 g kg(-1). The increases in milk (R(2) = 0.599) and protein (R(2) = 0.605) yields with maturity of maize silages were positively related to the increase in starch/NDF ratio of the maize silages. On average, the inclusion of maize silage in grass silage-based diets improved the forage DMI by 2 kg d(-1), milk yield by 1.9 kg d(-1) and milk protein content by 1.2 g kg(-1). Further comparisons showed that, in terms of milk and milk constituent yields, the optimum grass/maize silage ratio depends on the quality of both the grass and maize silages. Replacement of grass silage with maize silage in the ration, as well as an increasing maturity of the maize silages, altered the milk FA profile of the dairy cows, notably, the concentration of the cis-unsaturated FAs, C18:3n-3 and n-3/n-6 ratio decreased in milk fat. Despite variation in nutritive value, maize silage is rich in metabolizable energy and supports higher DMI and milk yield. Harvesting maize silages at a DM content between 300 and 350 g kg(-1) and feeding in combination with grass silage results in a higher milk yield 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".