The dynamic behavior of feed efficiency in primiparous dairy cattle
Bibliographic record
Abstract
A variety of measures are currently used to evaluate feed efficiency in livestock, with some of the most popular being the linear regression of nutrient intake on the various energy sinks of the cohort being evaluated. The adoption of novel on-farm technologies has greatly increased the breadth and depth of data being collected on a daily basis, which creates the opportunity for the implementation of more advanced feed efficiency models. The objectives of the current study were to leverage data collected using high-throughput biometric technologies to evaluate changes of early-lactation feed efficiency on a daily basis and to develop a novel measure of feed efficiency based on the NRC net energy model. Daily records of nutrient intake, fat- and protein-corrected milk yield, body weight, and body condition score were used to model 7 different indices of feed efficiency (gross feed efficiency, return over feed costs, residual feed intake, residual net energy intake, and net energy efficiencies of lactation, maintenance, and body reserve flux) in primiparous Holstein heifers (n = 40) from 0 to 150 d in milk. We observed that feed efficiency was dynamic and fluctuated over the course of the experimental period regardless of the efficiency measure used, but most measures followed similar trends in changes of efficiency on a day-to-day basis. Of the measures evaluated, return over feed costs would be most suited for management purposes due to practicality and simplicity, whereas other measures, such as the efficiency of net energy flux to and from body reserves, present potential targets for inclusion in breeding goals. Net energy efficiency of lactation was estimated to have higher repeatability compared with residual feed or energy intake, which may reduce the time required to estimate whole-lactation efficiency. Because decreased net energy balance was strongly correlated with increased efficiency for all indices evaluated, future work investigating the associations of feed efficiency with the magnitude and duration of negative energy balance is warranted.
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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 | 0.001 |
| 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".