Strategic supplementation to improve beef cattle performance and expand utilization of pasture-based production systems
Bibliographic record
Abstract
This project was conducted to examine the effects of supplement intake and variation when delivered using a precision feeding system on forage intake and cattle performance when grazing stockpiled perennial forages in early spring and late fall/early winter in Manitoba. In trial 1, 32 Angus-Shorthorn cross steers (391.8 ± 19.5 kg) were randomly assigned to one of four treatments: 1) orchardgrass/alfalfa with supplementation (OA-S; n=8), 2) orchardgrass/alfalfa without supplementation (OA-NS; n=8), 3) tall fescue/alfalfa/cicer milkvetch with supplementation (TAC-S; n=8) and 4) tall fescue/alfalfa/cicer milkvetch without supplementation (TAC-NS; n=8) over a 32-d period in early spring. Supplemented steers were offered 3.2 kg hd-1 d-1 of a grain screening pellet delivered by the SmartFeed Pro system. Forage dry matter intake (DMI) did not differ between treatments (P>0.05), suggesting no substitution of forage for supplement occurred. On average, supplement intake was below targeted allotment (1.7 kg hd-1 d-1), with between- and within-animal coefficient of variation (CV) ranging from 32 to 153%. Although serum urea nitrogen (SUN) concentrations did not differ between treatments, average daily gain (ADG) was significantly greater for OA-S than OA-NS by d 32. In trial 2, 32 Angus-Simmental cross bred heifers (402.5 ± 30.7 kg), selected based on confirmed pregnancy and adaptation to SmartFeed Pro systems, were randomly assigned to one of two treatments: 1) non-supplemented (NS; n=16) and 2) supplemented (S; n=16); with target supplement intake of 2.2 kg hd-1 d-1 for two consecutive 21-d periods in late fall/early winter. Forage DMI did not differ (P>0.05) between NS and S treatments and supplement DMI averaged 2.1 kg hd-1 d-1 with between- and within-animal CV values that were < 35%. Significantly higher ADG and SUN levels were observed for S heifers compared to NS. Further, methane (CH4) emissions did not differ, however S heifers emitted significantly more carbon dioxide (CO2) than NS heifers as a consequence of increased ADG. In summary, these findings suggest that precision feeding systems can effectively deliver supplement on pasture, however, adaptation to the feeders in confinement and day-to-day monitoring of the system is essential to ensure the targeted allotment of supplement is consumed.
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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".