The concordance between greenhouse gas emissions, livestock production and profitability of extensive beef farming systems
Bibliographic record
Abstract
Here we examine the concordance among emissions, production and gross margins of extensive beef farming systems by modelling a range of scenarios for herd management, animal genotype and pasture nutritive quality. We based our simulations on a case-study farm in central Queensland, Australia, and studied the influence of interventions designed for emissions mitigation, increasing productivity, or increasing gross margin. Interventions included replacing urea supplementation with nitrate, finishing cattle on the perennial forage leucaena (L), herd structure optimisation (HO), higher female fecundity (HF), and a leucaena finishing enterprise that had net farm emissions equal to the baseline (leucaena equal emissions; LEE). The HO intervention reduced the ratio of breeding cows relative to steers and unmated heifers, and lowered the ratio of costs to net cattle sales. Gross margin of the baseline, nitrate, L, LEE, HO and HF scenarios were AU$146 000, AU$91 000, AU$153 000, AU$170 000, AU$204 000 and AU$216 000, respectively. Enterprises with early joining of maiden heifers as well as HO and HF further increased gross margin (AU$323 000), while systems incorporating all compatible interventions (HO, HF, early joining, LEE) had a gross margin of AU$315 000. We showed that interventions that increase liveweight turnoff while maintaining net farm emissions resulted in higher gross margins than did interventions that maintained liveweight production and reduced net emissions. A key insight of this work was that the relationship between emissions intensity (emissions per unit liveweight production) or liveweight turnoff with gross margin were negative and positive, respectively, but only when combinations of (compatible) interventions were included in the dataset. For example, herd optimisation by reducing the number of breeding cows and increasing the number of sale animals increased gross margin by 40%, but this intervention had little effect on liveweight turnoff and emissions intensity. However, when herd optimisation was combined with other interventions that increased production, gross margins increased and emissions intensity declined. This is a fortuitous outcome, since it implies that imposing more interventions with the potential to profitably enhance liveweight turnoff allows a greater reduction in emissions intensity, but only when each intervention works synergistically with those already in place.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".