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Record W3016998145 · doi:10.1017/s1751731120000798

Improving the estimation of amino acid requirements to maximize nitrogen retention in precision feeding for growing-finishing pigs

2020· article· en· W3016998145 on OpenAlexafffund
Aline Remus, Jérôme R. E. del Castillo, C. Pomar

Bibliographic record

Venueanimal · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversité de MontréalUniversité de Sherbrooke
FundersAgriculture and Agri-Food CanadaSwine Innovation Porc
KeywordsAnimal scienceCompletely randomized designFeed conversion ratioEnergy requirementNitrogen balanceLysineNitrogenMathematicsChemistryAmino acidBody weightBiologyBiochemistryRegressionStatistics

Abstract

fetched live from OpenAlex

Precision feeding requires a mathematical model to estimate standardized ileal digestible (SID) lysine (Lys) requirements (SIDLysR) in real time. However, this type of model requires constant calibration updates. The objective of this study was to review the calibration of the model used to estimate the real-time Lys requirements of individual growing-finishing pigs. A digestibility trial (n = 10) was conducted to evaluate amino acids digestibility during the growing and finishing phases. Additionally, 120 pigs were used in two 28-day growth experiments conducted as completely randomized design with growing (25 ± 2.1 kg BW, n = 60; 10 pigs per treatment) or finishing barrows (68.1 ± 6 kg BW, n = 60; 10 pigs per treatment). In each experiment, the pigs were divided into six equal treatment groups and fed 60%, 70%, 80%, 90%, 100% or 110% of their estimated individual SIDLysR. The Lys requirement of each pig was estimated daily using a real-time model. Body composition was measured with dual-energy X-ray densitometry on day 1 and 28 of the experiments. Average daily feed intake increased quadratically (P < 0.05) during both growth phases. Maximum average daily gain (ADG) (0.98 kg) and maximum protein deposition (PD; 170 g/day) were observed in growing pigs fed 100% of the estimated SIDLysR (P < 0.001). During the growing period, PD in BW gain (17% to 19%) and N efficiency (52% to 65%) increased linearly (P < 0.01) with increasing inclusion rates of SID Lys. Finishing pigs had maximum ADG (1.2 kg/day) when they were fed 100% of the requirements. However, the amount of protein in BW gain (13% to 16%) and N efficiency (40% to 55%) increased linearly (P < 0.01) with increasing inclusion rates of SID Lys. In conclusion, the model proposed for precision feeding is correctly calibrated to predict SIDLysR that maximize PD and ADG of average pigs from 25 to 50 kg BW. Still, there is an opportunity to improve the estimation of SIDLysR and N retention in individual pigs by better representing the individual proportion of protein in BW gain and the factors controlling the efficiency of Lys utilization in individual pigs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.114

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.069
GPT teacher head0.267
Teacher spread0.198 · 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 teacher head, 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

Citations21
Published2020
Admission routes2
Has abstractyes

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