Predicting dry matter intake by growing and finishing beef cattle: Evaluation of current methods and equation development12
Bibliographic record
Abstract
The NRC (1996) equation for predicting DMI by growing-finishing beef cattle, which is based on dietary NEm concentration and average BW(0.75), has been reported to over- and underpredict DMI depending on dietary and animal conditions. Our objectives were to 1) develop broadly applicable equations for predicting DMI from BW and dietary NEm concentration and 2) evaluate the predictive value of using NE requirements and dietary NE concentrations to determine the DMI required (DMIR) by feedlot cattle. Two new DMI prediction equations were developed from a literature data set, which represented treatment means from published experiments from 1980 to 2011 that covered a wide range of dietary NEm concentrations. Dry matter intake predicted from the 2 new equations, which were based on NEm concentration and either the ending BW for a feeding period or the DMI per unit of average BW (End BW and DMI/BW, respectively), accounted for 61 and 58% of the variation in observed DMI, respectively, vs. 48% for the 1996 NRC equation. When validated with 4 independent data sets that included 7,751 pen and individual observations of DMI by animals of varying BW and feeding periods of varying length, DMI predicted by the 1996 NRC equation, the End BW and DMI/BW equations, and the DMIR method accounted for 13.1 to 82.9% of the variation in observed DMI, with higher r(2) values for 2 feedlot pen data sets and lower values for pen and individual data sets that included animals on lower-energy, growing diets as well as those in feedlot settings. The DMIR method yielded the greatest r(2) values and least prediction errors across the 4 data sets; however, mean biases (P < 0.01) were evident for all the equations across the data sets, ranging from as high as 1.01 kg for the DMIR method to -1.03 kg for the 1996 NRC equation. Negative linear bias was evident in virtually all cases, suggesting that prediction errors changed as DMI increased. Despite the expanded literature database for equation development, other than a trend for lower standard errors of prediction with the DMI/BW equation, the 2 new equations did not offer major advantages over the 1996 NRC equation when applied to the validation data sets. Because the DMIR approach accounted for the greatest percentage of variation in observed DMI and had the least root mean square error values in all data sets evaluated, this approach should be considered as a means of predicting DMI by growing-finishing beef cattle.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".