175 Effect of age at calving on greenhouse gas emissions from simulated beef farms grazing four stockpiled forage species in late fall/early winter
Bibliographic record
Abstract
Abstract The impact of age at first calving (2 versus 3 yrs) and type of forage species grazed in late fall/early winter on lifetime greenhouse gas (GHG) emissions from a cow-calf herd over an 8 or 9 yr period was examined. Farm simulations, based in Manitoba, Canada, were assessed using the Holos model to determine whole-farm GHG emissions for each scenario. The baseline herd consisted of 170 cows, 6 bulls, and their progeny which were sold at weaning, apart from herd replacements. Each simulation began with 207 newborn, female calves, with GHG emissions measured annually. From October to December, 1 of 4 stockpiled forages/forage mixtures were grazed: i) standing corn (COR), ii) tall fescue/meadow bromegrass (TFM), iii) orchardgrass/alfalfa (OGA), and iv) tall fescue/alfalfa/cicer milkvetch (TAC). All other feeding phase diets did not differ across all scenarios. Herd GHG emissions (Mg CO2e) were lower with heifers calving at 2 yrs (3,938 ± 71 Mg CO2e) versus 3 yrs (4,634 ± 72 Mg CO2e). Enteric methane (CH4) was the largest source of GHG emissions accounting for 66% of the total in both the 2- and 3-yr scenarios. Average enteric CH4 values were 3,820±61, 4,251 ± 68, 4,887±79, and 4,220 ± 68 Mg CO2e for simulations grazing COR, TFM, OGA, and TAC, respectively and were inversely related to total digestible nutrient (TDN) content of the forage mixtures with 72, 54, 45 and 55% TDN. Emissions were highest from OGA, the lowest quality forage, in both calving scenarios. Nitrous oxide emissions from livestock manure were the second highest contributing source, representing 15% of total emissions. Reducing age at first calving (2 versus 3 yrs) and providing higher energy forage in late fall/early winter reduced cow-calf GHG emissions. The adoption of management strategies such as reducing age at first calving and improving forage quality for extended grazing may reduce emissions from the cow-calf sector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".