Potential of Greenhouse Gas Production by Guinea Grass Subjected to Weed Competition
Bibliographic record
Abstract
The world population growth and the increasing purchasing power have raised the demand for animal proteins, especially meat and milk. This has made it essential to intensify livestock production by making it productive, efficient and at the same time sustainable, which today is a great challenge for cattle ranchers. The objective of this study is to evaluate the effects of coexistence with weeds on productivity, nutritional quality and potential of greenhouse gas (GHG) emission by Guinea grass (Panicum maximum cv. Mombasa) in pasture renewal conditions. The experiment was set up in randomized blocks, four replications, with treatments consisting of eight periods of coexistence: 0, 15, 30, 45, 60, 75, 90 and 120 days after seedlings emergence. Phyto-sociological evaluations were performed in the weed community at the end of each coexistence period. The Guinea grass was evaluated as for morphological, chemical and bromatological parameters and as for the potential of total gas, methane and carbon dioxide emission. The results show that productivity, nutritional quality and energy value of Guinea grass decreases proportionally to the increase in the period of coexistence with weeds. The non-coexistence with weeds provides a better in vitro digestibility of organic matter and, consequently, decreases the GHG emission potential by the dry matter of Guinea grass.
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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".