Association of herd hyperketolactia prevalence with transition management practices and herd productivity on Canadian dairy farms—A retrospective cross-sectional study
Bibliographic record
Abstract
The objective of this observational study was to assess the relationship between herd-level prevalence of hyperketolactia (HPH) with management practices of the transition period and herd milk production. Dairy herds (n = 71) were selected based on their inclusion in a herd management risk assessment study (August 2014–March 2018) using a Vital 90 (Elanco) Risk Assessment tool (one assessment per farm). Data from multiple milk recording test-days (Dairy Herd Improvement, DHI; Lactanet) were included in the analysis. Tests performed within ±6 mo relative to each farm's risk assessment date were included (10 ± 2 SD tests per farm). The majority of the farms were located in Ontario (83%). For each farm DHI test, the data set included herd average milk yield (kg/cow per day), average milk fat and protein (%), average somatic cell count (cells/mL), average days in milk (DIM), number of cows tested for ketosis, number of ketosis-positive tests (milk β-hydroxybutyrate ≥0.15 mmol/L), and proportion of cows by parity groups. Overall HPH (5–21 DIM) was calculated based on data available per farm (sum of all positive tests within 5–21 DIM/sum of all cows tested within 5–21 DIM). Each farm average was obtained by considering all test-days. A logit-transformation was applied to hyperketolactia prevalence. Linear regression models (PROC GLM and MIXED of SAS, Version 9.4) were used to predict herd HPH (milk β-hydroxybutyrate ≥0.15 mmol/L within 5 to 21 DIM; the outcome of interest). Four initial models (far-off, close-up, and fresh periods, and DHI) were separately built to assess associations between their variables and HPH; a final model considered variables selected in the initial models. Univariable (liberal P < 0.25) followed by multivariable models were used to build specific models for each period of the risk assessment. Herd prevalence of hyperketolactia was 27 ± 14%, with an average herd size of 141 ± 110 cows. The final HPH model (R 2 = 24.8%) included weighted milk yield, the proportion of primiparous cows, water access in the close-up period, and access to rest areas or stall access in the fresh period. Herd prevalence of hyperketolactia was negatively associated with milk yield [odds ratio, OR=0.96 (95% confidence interval 0.92–0.99)] and proportion of primiparous cows [OR = 0.98 (0.96–0.99)]. The odds of hyperketolactia were greater with poor water access and quality (<5 cm of linear access per cow; dirty water; only 1 water location in pen) than with ≥10.2 cm of linear access per cow; clean water; >2 water locations in pen [1.23 (1.11–2.39)] in the close-up period. The odds of hyperketolactia were greater in farms providing limited access to rest areas in the fresh period than in farms providing constant access to rest areas, without dead-ends [1.64 (1.03–2.80)]. In Canadian dairy herds, HPH in early lactation was associated with certain transition-period management practices and was negatively associated with herd productivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".