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Record W4396652111 · doi:10.1093/jas/skae102.070

13 Impact of precision feeding during gestation on the productive career of sows over three reproductive parities

2024· article· en· W4396652111 on OpenAlexaff
Béatrice Sauvé, L. Cloutier, Lucie Galiot, Frédéric Guay, Gabrielle Dumas, Charlotte Gaillard, Jean-Yves Dourmad, Aude Simongiovanni, Patrick Gagnon

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsCentre de Développement du Porc du QuébecUniversité Laval
Fundersnot available
KeywordsLitterWeaningAnimal scienceGestationBiologyLactationBody weightWeight gainReproductionPregnancyEndocrinologyEcology

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to evaluate the impact of precision feeding during gestation on the career performances and longevity of sows, initially gilts, monitored over three successive reproductive cycles. Four isoenergetic treatments were compared: two conventional constant-concentration feeding strategies (0.53 % DIS Lys) with feed supply either constant (flat feeding; FF) or variable (bump feeding; BF), and two precision feeding strategies based on the InraPorc model applied considering either the mean weight by parity (PFP) or the individual weight of each sow at breeding (PFI). Four groups, representing 393 gilts, were followed from breeding to weaning over three cycles. Body weight and backfat depth were measured at breeding, 90 d of gestation, parturition, and weaning. Feed consumption was monitored daily. Litter variables were measured, including birth and weaning weight, mortality, and overall body weight gain. A mixed model was used to analyze differences between the four treatments using the sow as the experimental unit for performance analysis during the third reproductive cycles. For the analysis of the overall productive career over the three reproductive cycles, the performances of the two conventional strategies (with constant Lys DIS concentration feeding, CONV) were compared with the two precision feeding strategies (PF). During the third cycle, PFP and PFI sows had a lighter body weight at the beginning of gestation than the FF sows (P < 0.01), but increased their body weight gain during gestation (P < 0.001). Backfat loss in PFP and PFI sows during lactation was greater compared with BF sows (P = 0.05), FF sow being intermediate. PFP sows had a lower stillborn rate (P = 0.04) and tended to have less total piglet mortality (P = 0.06) than FF and BF sows. Over the three reproductive cycles, the stillborn rate and total piglet mortality were less in PF sows than in CONV sows (respectively -0.9 %; P = 0.02 and -1.5 %; P = 0.03). PF sows weaned more piglets during their three cycles than CONV sows did (+ 0.7; P = 0.03), with weaning litter weight gain also increasing for PF sows (+3.7 %; P = 0.02). The culling rate of gilts after three cycles did not differ significantly among treatments (P = 0.27), but tended to be greater for PF sows after one cycle (+4.9 %; P = 0.08). The results seem to show a lasting benefit of precision feeding for sows, with a decrease in piglet mortality during lactation, suggesting greater piglet viability without significantly decreasing sow longevity.

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)0.001
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
Metaresearch0.0000.001
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 head0.087
GPT teacher head0.374
Teacher spread0.287 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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