Effect of Seasons on Enteric Methane Emissions from Cattle Grazing Urochloa brizantha
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of seasons under a tropical climate on forage quality, as well the effect of an Urochloa brizantha cv. Marandu grazing system on enteric methane (CH4) emissions from Nellore cattle in the Southeast region of Brazil. Sixteen Nellore steers (18 months old and initial weight 318.0 ± 116.59 kg of LW; final weight 469 ± 98.50 kg of LW) were used for a trial period of 10 months, with four collection periods in winter (August), spring (December), summer (February) and autumn (May). Each collection period consisted of 28 days, corresponding to the representative month of each season where the last six days were designed for methane data collection. Animals were randomly distributed within 16 experimental plots, distributed in four random blocks over four trial periods. CH4 emissions were determined using the sulphur hexafluoride (SF6) tracer gas technique measured by gas chromatography and fluxes of CH4 calculated. The forage quality was characterized by higher CP and IVDMD and lower lignin contents in spring, differing specially from winter forage. Average CH4 emissions were between 102.49 and 220.91 g d-1 (37.4 to 80.6 kg ani-1 yr-1); 16.89 and 30.20 g kg-1 DMI; 1.35 and 2.90 Mcal ani-1 d-1; 0.18 and 0.57 g kg-1 ADG-1 and 5.05 and 8.76% of GE. Emissions in terms of CO2 equivalents were between 4.68 and 14.22 g CO2-eq-1 g-1 ADG. Variations in CH4 emissions were related to seasonal effect on the forage quality and variations in dry matter intake.
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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 | 0.001 | 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".