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Record W4320717776 · doi:10.3168/jds.2022-22377

Association of herd hyperketolactia prevalence with transition management practices and herd productivity on Canadian dairy farms—A retrospective cross-sectional study

2023· article· en· W4320717776 on OpenAlexafffundabout
Rita Couto Serrenho, Chris Church, Darren McGee, T.F. Duffield

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsNutreco (Canada)University of Guelph
FundersElanco Animal HealthOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsHerdSomatic cell countAnimal scienceKetosisDairy cattleBulk tankLogistic regressionAnimal husbandryMastitisMedicineVeterinary medicineIce calvingMathematicsBiologyStatisticsLactationAgriculturePregnancy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.284
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes3
Has abstractyes

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