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Record W4396651782 · doi:10.1093/jas/skae102.355

PSV-19 Impact of precision feeding and bump feeding strategy during gestation on the performance of post-weaning pigs

2024· article· en· W4396651782 on OpenAlexaff
C. A. St. Pierre, L. Cloutier, Lucie Galiot, Frédéric Guay

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsCentre de Développement du Porc du QuébecUniversité Laval
Fundersnot available
KeywordsWeaningGestationAnimal scienceBiologyPregnancy

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to evaluate the post-weaning growth performance of piglets from sows of parities 1 to 3 fed with 4 different feeding programs during gestation; Flat feeding (FF, constant concentration and feed intake), Bump feeding (BF, constant concentration and variable feed intake), precision feeding by parity (PFP, variable concentration and variable feed intake using the average weight per parity), individual precision feeding (PFI, variable concentration and variable feed intake using the weight of each sow at breeding). At weaning of each parity, five litters from each of the dietary treatments were selected and five average piglets from each of these litters were transferred to a nursery building with 20 pens. All pens received the same feeding program in 3 phases of 14 d each. Piglets were weighed at the beginning and end of each feeding phase. The quantity of feed was noted for each phase. At the end of phases 1 and 3, one piglet per pen was scanned on the DXA to assess its body lean, fat, and bone mineral composition. During phase 1, average daily gain (ADG) was greater for piglets from parity 1 sows than those from parity 2 and 3 while average daily feed intake (ADFI) and feed conversion (FC) were greater for piglets from parity 3 sows (P < 0.05; Table). Dietary treatments during gestation did not affect performances in phase 1. In phase 2, ADG and ADFI were greater while FC was decreased for piglets from sows of parity 2 and 3 (P< 0.001). Piglets from FF treatment had a greater ADG and a lesser FC (P < 0.05) than piglets from BF, piglets from PFP and PFI being intermediate. In phase 3, piglets from parity 2 sows and those from FF treatment had greater ADG and ADFI than piglets from PFI treatment, piglets from PFP and BF treatments being intermediate (P < 0.005). For all post-weaning period, piglets from parity 2 sows had greater ADG, ADFI and body weight at 42 d (P < 0.001) while piglets from the FF treatment had better ADG and weight at 42 d (P < 0.05) than those from BF and PFI (P < 0.05), PFP being intermediate. The lean ADG was greater for piglets from FF treatment (P < 0.021) than piglets from PFI, piglets from BF and PFP being intermediate. Piglets from sows of parity 2 and 3 had also greater lean ADG (P < 0.001). Fat ADG was also higher for piglets from parity 2 sows (P < 0.010) than piglets from parity 1 sows, piglets from parity 3 sows being intermediate. Piglets from parity 2 sows clearly had better post-weaning performances. The FF treatment during gestation led to better post-weaning performance, suggesting that energy or nutrient intake in early gestation affects offspring growth and development in post-weaning period.

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 score0.005

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 integrity0.0000.000
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 head0.032
GPT teacher head0.287
Teacher spread0.255 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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