Intensive Silvopastoral Systems Mitigate Enteric Methane Emissions from Cattle
Bibliographic record
Abstract
Assessments of the efficiency of grazing systems, in terms of productivity and ecological sustainability, are necessary in view of the increased demand for animal protein. In this study, the methane (CH4) emissions (sniffer methodology), dry matter (DM) yield, paddock chemical composition (AOAC and Van Soest methods), nutrient intake (dry matter, DMI; crude protein, CPI; metabolizable energy, MEI), daily milk yield (DMY), body condition score (BCS), and body weight (BW) of cattle, in intensive silvopastoral systems (ISPSs) and monoculture systems (MSs), in the tropics of Mexico were evaluated. In the ISPS, CH4 emissions (18% less) and DMY were lower than in the MS. Cows from MSs tend to disperse across higher values of CH4 emissions per kg of DMI, as well as higher milk production, while cows from the ISPS were dispersed over a higher intake (DMI, CPI, and MEI) and lower CH4 emissions. There were no differences between systems in paddock DM yield, chemical composition, cows’ BCS, and BW, regardless of whether it was the dry (April to May) and rainy (September to October) season. Based on the results obtained in this study, ISPSs contribute to the mitigation of methane emissions in cattle; forage and animal production variables in both systems were similar, with a lower use of imported inputs in the ISPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".