Environmental performance of commercial beef production systems utilizing conventional productivity-enhancing technologies
Bibliographic record
Abstract
Abstract The objective of this study was to evaluate the effects of using conventional productivity-enhancing technologies (PETs) with or without other natural PETs on the growth performance, carcass traits, and environmental impacts of feedlot cattle. A total of 768 cross-bred yearling steers (499 ± 28.6 kg; n = 384) and heifers (390 ± 34.9 kg; n = 384) were offered a barley grain-based basal diet and divided into implanted or non-implanted groups. Steers were then allocated to diets that contained either: (i) no additive (control); natural feed additives including (ii) fibrolytic enzymes (Enz), (iii) essential oil (Oleo), (iv) direct-fed microbial (DFM), (v) DFM + Enz + Oleo combination; conventional feed additives including (vi) Conv (monensin, tylosin, and beta-adrenergic agonists [βAA]); or Conv with natural feed additives including (vii) Conv + DFM + Enz; (viii) Conv + DFM + Enz + Oleo. Heifers received one of the first three dietary treatments or the following: (iv) probiotic (Citr); (v) Oleo + Citr; (vi) Melengesterol acetate (MGA) + Oleo + βAA; (vii) Conv (monensin, tylosin, βAA, and MGA); or (viii) Conv + Oleo (ConvOleo). Data were used to estimate greenhouse gas (GHG) and ammonia (NH3) emissions, as well as land and water use. Implant and Conv-treated cattle exhibited improvements in growth and carcass traits as compared to the other treatments (P < 0.05). Improvements in the performance of Conv-cattle illustrated that replacing conventional feed additives with natural feed additives would increase both the land and water required to satisfy the feed demand of steers and heifers by 7.9% and 10.5%, respectively. Further, GHG emission intensity for steers and heifers increased by 5.8% and 6.7%, and NH3 emission intensity by 4.3% and 6.7%, respectively. Eliminating the use of implants in cattle increased both land and water use by 14.6% and 19.5%, GHG emission intensity by 10.5% and 15.8%, and NH3 emission intensity by 3.4% and 11.0% for heifers and steers, respectively. These results demonstrate that the use of conventional PETs increases animal performance while reducing the environmental impacts of beef production. Restricting use would increase the environmental footprint of beef produced for both domestic and international markets.
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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".