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Record W3043138469 · doi:10.3168/jds.2020-18499

Substitution of wheat straw with sugarcane bagasse in low-forage diets fed to mid-lactation dairy cows: Milk production, digestibility, and chewing behavior

2020· article· en· W3043138469 on OpenAlexaff
Mohammad Reza Molavian, G.R. Ghorbani, Hassan Rafiee, K. A. Beauchemin

Bibliographic record

VenueJournal of Dairy Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersIsfahan University of Technology
KeywordsDry matterNeutral Detergent FiberLatin squareForageBagasseStrawAnimal scienceChemistryLactationTotal mixed rationFermentationFodderDairy cattleFood scienceRumenAgronomyBiologyBiotechnologyIce calving

Abstract

fetched live from OpenAlex

Sugarcane bagasse (SB) is a low-quality roughage source that is often plentiful during times of forage shortage. It is generally less costly compared with other conventional sources of forage. We hypothesized that SB could be used as a source of roughage for dairy cattle by replacing wheat straw (WS), another low-quality forage. This study evaluated the effects of replacing WS with SB in diets offered to mid-lactation dairy cows on milk production and fatty acid profile, intake, digestibility, chewing activity, and ruminal fermentation. Nine multiparous Holstein cows averaging (mean ± standard deviation) 105 ± 12 d in milk, 42.1 ± 2.9 kg of milk/d, and 617 ± 59 kg of body weight were used in a replicated 3 × 3 Latin square with 21-d periods. Treatments were (% of dietary dry matter, DM): (1) 0SB, diet containing 0% SB and 27% WS, (2) 9SB, diet containing 9% SB and 18% WS, and (3) 18SB, diet containing 18% SB and 9% WS. Sugarcane bagasse had greater organic matter (OM; 94.1 vs. 85.1% of DM), neutral detergent fiber (NDF; 86.2 vs. 76.4% of DM), acid detergent fiber (ADF; 62.9 vs. 45.2% of DM), and lignin (19.9 vs. 10.3% of DM) concentration, but less crude protein (CP; 2.63 vs. 3.72% of DM) concentration than WS. Sugarcane bagasse also had greater physically effective NDF (total dietary NDF multiplied by % of TMR on the 8-mm + 19-mm sieves, peNDF 8 ; 63.2 vs. 40.6% of DM) and undegraded NDF after 288 h of incubation (uNDF 288 ; 35.5 vs. 21.2% of DM) contents than WS. The undegraded NDF after 30 h of incubation (uNDF 30 ) content was similar for all diets; however, peNDF 8 concentration and proportion of long particles (retained on a 19-mm sieve) increased linearly as SB inclusion in the diets increased. Cows increasingly sorted against long particles as SB replaced WS. Intakes of DM (26.53 kg/d) and NDF (8.58 kg/d) did not differ among the treatments, but intakes of OM and CP decreased, whereas ADF and uNDF 288 intakes increased with SB inclusion level. Total-tract digestibilities of OM, CP, and NDF decreased linearly as SB replaced WS. Milk yield (37.0 kg/d), energy-corrected milk yield (ECM; 38.2 kg/d), feed efficiency (1.44 kg ECM yield/kg DM intake), and milk composition (fat, 3.89%; true protein, 2.90%) did not differ among diets. Increasing SB concentration of the diet linearly increased rumination time, but ruminal pH (ruminocentesis, 4 h after feeding) decreased. Total volatile fatty acid concentration increased linearly, whereas acetate:propionate decreased linearly, as SB replaced WS. The results indicate that replacement of WS with increasing levels of SB in low-forage diets with similar uNDF 30 concentrations did not affect performance of mid-lactation dairy cows. We conclude that SB can be used as a fiber source in diets fed to dairy cows in mid-lactation; however, the decrease in total-tract digestibility of diets may decrease lactational performance when fed to high-producing dairy cows.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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

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 head0.031
GPT teacher head0.261
Teacher spread0.230 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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