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Record W2996073539 · doi:10.3168/jds.2019-17557

Moisture content of high-straw dry cow diets affects intake, health, and performance of transition dairy cows

2019· article· en· W2996073539 on OpenAlexafffund
Casey Havekes, T.F. Duffield, A.J. Carpenter, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Agri-Food Innovation AllianceUniversity of Guelph
KeywordsDry matterAnimal scienceTotal mixed rationStrawIce calvingLactationChemistrySilageLactoseRuminationDairy cattleNeutral Detergent FiberFood scienceBiologyPregnancy

Abstract

fetched live from OpenAlex

The objective of this study was to determine the effect of adding water to a high-straw dry cow diet on feeding behavior, health, and performance of dairy cows. Holstein cows (n = 40) entering their second or greater lactation were enrolled at dry off (∼45 d before expected calving) and assigned to 1 of 2 dietary treatments, consisting of a high-straw dry cow total mixed ration [TMR; 36% wheat straw, 41% corn silage, 23% pellet, on a dry matter (DM) basis, formulated for 11.6% crude protein, 1.35 Mcal/kg net energy for lactation] with (1) no water (CON; n = 20; DM = 53.4%) or (2) water added to decrease the DM by ∼10 percentage points (wet diet, WD; n = 20; DM = 45.4%). Upon calving, all cows were fed the same lactating TMR (45.2% DM, 14.9% crude protein, 1.68 Mcal/kg net energy for lactation) and followed for 28 d. Dry matter intake, feeding behavior, and rumination activity were recorded automatically. Fresh TMR and orts samples were collected 2×/wk to determine differences in sorting. A particle separator was used to separate feed samples into 4 fractions: long (>19 mm), medium (<19, >8 mm), short (<8, >4 mm), and fine (<4 mm) particles. Feed sorting was calculated as actual intake of each particle fraction expressed as a % of predicted intake. Cows fed WD had greater DM intake during the dry period than cows fed CON (CON = 13.4 ± 0.24, WD = 14.2 ± 0.24 kg/d), but rumination did not differ (CON = 522.2 ± 8.48, WD = 518.5 ± 8.69 min/d) between treatments. Cows fed WD tended to consume their feed faster (CON = 0.08 ± 0.001, WD = 0.09 ± 0.001 kg of DM/min) compared with CON cows. Regardless of treatment, cows sorted against the longest ration particles, with cows fed CON sorting more against these (CON = 81.1 ± 2.51%, WD = 94.9 ± 2.58%). Cows fed CON tended to sort more in favor of the medium particles than those fed WD (CON = 105.0 ± 0.36%, WD = 104.2 ± 0.37%). Postcalving DM intake and feeding behavior were not affected by dry diet treatment. Cows fed WD experienced a less rapid daily decline in rumen pH during the first 7 d after calving compared with those fed the CON diet. The results suggest that increasing the moisture content of high-straw dry cow diets, by adding water, may improve intake and reduce sorting against the longest ration particles, which may help promote consistency in targeted nutrients consumed during the dry period and greater rumen health in early lactation.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.237
Teacher spread0.212 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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