Production, Forage Quality, and Performance of Holstein Cows under Intermittent Grazing on Tifton 85
Bibliographic record
Abstract
Tropical forage grasses compose the base diet of the Brazilian cattle herd due to their low production cost, high production potential, and good adaptation to various Brazilian ecosystems. In recent years, the search has intensified for alternatives that increase the yield in pasture milk production systems. Thus, the present study evaluated the production and quality of Tifton 85 grass in relation to the production parameters (e.g. greater leaf: stem ratio), and milk quality of Holstein cows in an intermittent grazing system during different seasons of the year. The experiment was conducted at the Córrego da Ponte Farm in Santa Helena de Goias, Goias, Brazil, from April 2014 to March 2015. The experiment used a completely randomized design with nine replicates and the four seasons (fall, winter, spring, and summer) as treatments. The Holstein cows had a live weight of 560±36.8 kg and averages four years of age. The grazing method used was a mob-stocking, with one day of grazing and 19 days of rest. The results showed that Tifton 85 was efficient regarding production parameters (total dry mass) and forage quality (IVDMD, NDF, ADF). The milk production was satisfactory during the fall, spring, and summer. The forage showed limited production during the winter, due the highest NDF, ADF levels and lower IVDMD (P < 0.05), which was directly reflected in the milk yield, fat, protein and lactose (P < 0.05). A reduction in the stocking rate or an increase in the number of paddocks is advisable during the winter to maximize milk production without compromising forage development. The correlation data showed the importance of consuming better-quality forage to increase milk production without compromising the levels of milk solids.
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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) | 0.000 | 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".