Beef Cattle Grazing Native Grasslands May Follow Three Different Supplement Response Patterns
Bibliographic record
Abstract
Previous studies on winter supplementation of growing cattle grazing stockpiled native Campos grasslands suggest that forage allowance (FA), herbage mass, and weather conditions before and during the supplementation period could all affect supplement feed efficiency (SFE)—that is, the difference or change in average daily gain (ADG) between supplemented (S) and control (C) animals (ADGchng, kg) per unit (kg) of supplement dry matter (DM) intake. In this study, we analyse data from fifteen collated winter supplementation trials carried out in Uruguay between 2004 and 2018. The working hypotheses of this research paper were: (i) that average substitution rates are positive, and (ii) that ADGchng is not constant throughout the supplementation period and that its variation may be attributed to sward, animal or weather variables. There were two main objectives: (i) to estimate the average supplement substitution rate (sSbR, kg forage, f, dry matter, DM intake reduction: kg supplement DM intake) and potential herbage intake substitution rate (hSbR, kg fDM intake reduction: kg fDM intake of control animals), and its association with SFE, and, (ii) to assess the existence of different phases and supplementation response patterns and its association with other relevant variables. Estimated substitution rates were always positive (sSbR = 0.3–1.1 kg/kg; hSbR = 0.1–0.3 kg/kg) and were negatively and moderately associated with SFE. Supplementation proved to be a dynamic process where three possible supplementation responses over the supplementation period were identified (linear, quadratic and Weibull). While linear patterns did not appear distinctly associated with any particular set of variables, quadratic models were mostly associated with herbage biomass and substitution rates, whereas Weibull models were the clearest in their association with frosts. Regardless of the response pattern, at the beginning of the trials it was the animals’ body weight and supplement quality that most influenced supplement response, whereas towards the end, supplementation intake, supplemented animals’ ADG and forage quality played a more relevant role. The estimated parameters and response patterns are expected to be used as inputs in decision support systems for livestock farmers in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".