Factors associated with feed efficiency traits in Italian Jersey cows
Bibliographic record
Abstract
Feed efficiency in lactating cows is one of the major aspects in modern dairy farm management. Purpose of the present study was to identify sources of variation of body live weight (LW) and feed efficiency traits in a population of 8,516 primiparous Jersey cows. The variables tested included LW, fat corrected milk (FCM), predicted dry matter intake (pDMI), energy corrected milk (ECM), and predicted feed efficiency (pFE_ECM), defined as ECM/pDMI. Linear scoring and age at evaluation were used to estimate LW. Factors associated with such traits were investigated using linear mixed models which included the fixed effects of origin of the paternal grandsire (USA, Denmark, Canada), stage of lactation, year of birth, and age at first calving. Random terms were cow, contemporary group (herd-year-season at linear scoring), and the residual. Mean of LW and pFE_ECM were 435 kg and 1.36, respectively. Feed efficiency was weakly negatively correlated (-0.15) with LW, and pDMI was positive correlated with ECM (0.88). Values of LW ranged from 444.01 to 449.88 kg in cows born between 2006 and 2014. Furthermore, cows with paternal grandsire born in Canada were the heaviest, but they had the lowest values for FCM, pDMI, ECM and pFE_ECM. Feed efficiency, FCM, ECM, and pDMI increased in the first three months of lactation, and then decreased. Kilos of pDMI and FCM increased concurrently with age at first calving and systematically decreased as year of birth proceeds. Energy corrected milk of cows born between 2011 and 2014 was lower than ECM of animals born between 2008 and 2010, consequently resulting in lower pFE_ECM. Late calving Jerseys were less feed efficient compared to early calving contemporaries. Systematic environmental factors associated with feed efficiency will be used to adjust phenotypes for routine genetic evaluation.
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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 | 0.001 | 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".