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Record W4400860338 · doi:10.3168/jds.2023-24618

Magnitude of change in prepartum feed intake: Estimations using multiple classes of predictors and associations with transition metabolism, health, and milk production

2024· article· en· W4400860338 on OpenAlexafffund
M.G.S. Santos, B. Mion, E.S. Ribeiro

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural AffairsOntario Agri-Food Innovation AllianceCanada First Research Excellence FundUniversity of Guelph
KeywordsIce calvingAnimal scienceDry matterLactationParity (physics)Milk productionBiologyPregnancy

Abstract

fetched live from OpenAlex

The objectives of this study were to identify factors associated with the relative change in prepartum dry matter intake ( RCDMI ) of 273 cows fed individually, evaluate the performance of linear models to estimate RCDMI using different classes of predictors, and characterize the implications of RCDMI to transition metabolism, health, and subsequent milk production. Two periods of interest were established. Period 1 comprised d −21 to −12 relative to calving, when DMI was stable. Period 2 comprised d −4 to −1, when average DMI was distinctly declined. The RCDMI from period 1 to 2 was calculated as a percentage value, which ranged from −75 to 15% and averaged −18.1% (±15.0). Season, parity, body fatness, body weight, milk production in the previous lactation and at dry-off, and length of dry period were associated with RCDMI and explained 11% of the variation in all cows, and 19% when only parous cows were considered. Performance of linear models to predict RCDMI was improved when data on rumination and physical activities and target blood metabolites were added. The adjusted R 2 increased to values between 0.45 and 0.55, and selected models performed consistently in cross-validation analyses. To evaluate the implications of RCDMI, cows were ranked within parity according to RCDMI and classified into terciles as large decline ( LD ), moderate decline ( MD ), or small decline ( SD ). By design, DMI did not differ between tercile groups in period 1 (13.3 ± 0.2 kg/d), but differed substantially in period 2 (LD = 8.8; MD=11.2; SD=12.7 ± 0.2 kg/d), creating important differences in RCDMI among groups (LD = −33.8; MD=−16.2; SD=−3.4% ± 0.8%). At enrollment, cows in the LD and MD groups were heavier (LD = 788; MD=775; SD=750 ± 7 kg), and the proportion of cows with BCS >3.5 was higher in LD (LD = 63; MD=47; SD=38%). An interaction of group and time was observed for postpartum DMI, which started lower in LD than in SD cows, but equaled by the end of transition, and inverted at wk 13 and 14 after calving. Yields of energy-corrected milk were greater in LD than in SD cows, and both groups did not differ from MD (LD = 41.0; MD=40.3; SD=39.0 ± 0.5 kg/d). LD cows had decreased energy balance and greater concentrations of nonesterified fatty acids, β-hydroxybutyrate, and aspartate aminotransferase in serum, and greater glutathione peroxidase activity in plasma than SD cows. Larger declines in prepartum DMI were also associated with increased risk for postpartum disease, although the associations were only weak to moderate. In conclusion, a large decline in prepartum DMI was associated with important adjustments in the energy metabolism and antioxidants activities, and greater milk production in the subsequent lactation. These findings indicate that feed intake decline close to parturition is likely a normal response to physiological adaptations at the onset of lactation when cows are fed at libitum.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.300
Teacher spread0.243 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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