Comparison of techniques for estimation offorage dry matter intake bygrazing beef cattle
Bibliographic record
Abstract
Four techniques were compared for their ability to estimate forage dry matter intake (DMI) of grazing animals. In the Cage technique, 10 grazing cages were placed within each pasture, and forage inside and outside the cages was clipped from 0.25-m2 quadrats after 12 to 20 d of grazing. The difference between forage inside and outside the grazing cage represented forage consumed by grazing animals. The second technique used n-alkane controlled release capsules to measure DMI of individual grazing animals (N-alkane marker technique). The remaining two techniques were prediction equations; one utilized body weight (BW) and average daily gain (ADG) to estimate DMI (Minson equation) and the other related dietary net energy concentration and BW to DMI (Net Energy equation). The four methodologies were applied to a 3-yr study evaluating liquid hog manure on the productivity of grass pastures. The study utilized steers of a relatively uniform body size on six continuously grazed grass pastures. When individual animal intakes were compared, the Net Energy equation estimated lower (P < 0.05) DMI than the Minson equation, with the N-alkane marker technique being intermediate. Dry matter intake (mean ± SD, kg d-1) was 5.3 ± 0.76, 7.4 ± 1.86, and 6.5 ± 2.58 for the Net Energy equation, Minson equation, and N-alkane marker technique, respectively. The Cage technique estimated average DMI of animals in each pasture of 17.5 ± 11.61 kg d-1 and this was higher (P < 0.05) than estimates from the other techniques. Dry matter intake estimates from different techniques were ranked: Cage technique > Minson equation > N-alkane marker technique > Net Energy equation. The DMI estimates generated by the Cage technique were associated with the largest coefficient of variation, with extreme values representing a range from 0.3 to 15.2% BW. The N-alkane marker technique estimated DMI that ranged from 0.6 to 4.5% BW. The strongest correlation (r = 0.30; P = 0.001) in DMI estimates was between the Net Energy and Minson equations due to the inclusion of BW as a component in both equations. Results of this study suggest that prediction equations can be useful for rapid DMI estimation. The N-alkane marker technique may be the preferred technique when a wide range of pasture conditions is anticipated. Key words: Grazing cages, n-alkanes, prediction equations, dry matter intake, grazing animals
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".