Dry Matter Intake, Digestibility and Milk Yield by Friesian Cows Fed Two Napier Grass Varieties
Bibliographic record
Abstract
The objective of this study was to compare two varieties of Napier grass (Bana Napier grass vs French Cameroon Napier grass) and to determine whether feed intake, digestibility, average daily gain (ADG) and milk yield of lactating Friesian cows from fresh cut Bana Napier grass was greater than from French Cameroon Napier grass, using a completely randomized design.Results show that Bana Napier grass had similar percent dry matter (DM), ash and gross energy (GE) to French Cameroon.Bana grass had higher percent crude protein (CP) and lower fiber fractions, acid detergent fibre (ADF), neutral detergent fibre (NDF) and lignin compared to French Cameroon.Overall the forage quality was marginally higher in Bana Napier grass compared to French Cameroon.The DM and NDF intake expressed as a percentage of body weight (BW) were similar in both Napier grass types.Both grasses had similar digestible DM and energy.Bana had higher digestible CP but lower digestible ADF and NDF than French Cameroon.Bana Napier was not different from French Cameroon when fed as a sole diet to lactating cows in terms of low DM intake, milk yield and a loss of BW and condition.To improve the efficient utilization of both Napier grass varieties, a supplement capable of supplying 1085-1227 g CP/d and 17.0-18.0Mcal ME/d is required for cows to support moderate gains 0.22 kg/d and 15 kg 4% fat corrected milk/d.
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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 | 0.000 |
| 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".