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Record W4415532046 · doi:10.3168/jds.2025-26966

Effect of ensiled or finely ground α-amylase–enabled corn grain on lactation performance, chewing, ruminal fermentation, digestibility, and nitrogen partition of dairy cows

2025· article· en· W4415532046 on OpenAlexafffund
Wesley de Rezende Silva, Adilson Nunes Da Silva, Mariane A Tiengo, João Pedro Andrade Rezende, Stefânia Priscila Souza, R.A.N. Pereira, T.J. DeVries, Marcos Neves Pereira

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisUniversidade Federal de LavrasCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of Guelph
KeywordsLatin squareLactationStarchSoybean mealMilk fatMealDairy cattleCorn starch

Abstract

fetched live from OpenAlex

α-Amylase-enabled corn (AAC) and ensiling of corn kernels can improve starch digestion, feed efficiency, and N partition into milk of dairy cows. This experiment evaluated the effect of AAC on cows fed finely ground (GRC) or rehydrated and ensiled (REC) corn kernels on the lactational performance, DMI, ruminal fermentation, digestibility, chewing behavior, and N partition of dairy cows. Twenty-four individually fed Holstein cows (37.1 ± 4.8 kg/d milk yield, 143 ± 100 DIM, 633 ± 64 kg body weight), arranged in 4 × 4 Latin squares (with 21-d periods), were assigned to 4 treatment sequences. Treatments were arranged in a 2 × 2 factorial with 2 corn processing methods (GRC vs. REC) and 2 corn types (AAC, 48.8% vitreousness vs. isogenic control [CTL], 51.1% vitreousness). Kernels were weekly ground, hydrated (61.7% ± 0.55% DM for AAC and 61.7% ± 0.45% of DM for CTL) and ensiled to maintain a constant storage duration (28 ± 3 d). Diets had 13.0% corn grain from each treatment, 22.4% total starch, and 9.6% starch from each treatment, on DM basis. No effect of treatment was detected for milk yield (36.1 kg/d) and DMI (22.4 kg/d). Milk fat tended to be lower for cows fed REC compared with GRC (3.68% vs. 3.74%). The ECM/DMI did not differ between treatments, but REC tended to increase milk yield/DMI compared with GRC (1.63 vs. 1.61). Cows fed REC had shorter meals than cows fed GRC (35.9 vs. 37.5 min/meal). Meal frequency was lowest with GRC-CTL than the other treatments (9.3 vs. 10.4 meals/d). Meal size was reduced in cows fed REC-CTL (2.2 kg DM/meal) compared with GRC-CTL (2.5 kg DM/meal), whereas no difference was detected between REC-AAC (2.3 kg DM/meal) and GRC-AAC (2.4 kg DM/meal). Cows fed AAC tended to have higher total-tract starch digestibility (93.7% vs. 92.9% of starch intake) and lower fecal starch concentration (4.3% vs. 4.8% of DM) compared with CTL. No effect of treatment was detected on NDF digestibility (48.6% of NDF intake), ruminal microbial yield and molar proportions of VFA. Ruminal pH was lower in cows fed REC compared with GRC (6.72 vs. 6.83) and tended to be lower on AAC compared with CTL (6.74 vs. 6.82). Fecal pH did not differ between treatments. Cows fed REC tended to have a greater proportion of N intake in milk compared with GRC (34.2% vs. 33.7%), but fecal N and urine N excretions (g/d and % of N intake), and urea N in milk and in plasma did not differ. Results showed few significant interactions between corn type and processing, suggesting that the mode of action of AAC was in the digestive tract, independently of ensiling. Overall, providing AAC to lactating dairy cows may enhance starch digestibility without affecting DMI or milk yield, regardless of the corn kernel processing method used.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 integrity0.0000.001
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 head0.019
GPT teacher head0.276
Teacher spread0.256 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

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