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Record W2395162874

Pregnant beef heifers categorized by residual feed intake measured in adolescence exhibit differential intake and feeding behaviors when fed a restricted diet

2014· article· en· W2395162874 on OpenAlexaff
Carolyn Fitzsimmons

Bibliographic record

Venue2014 ADSA-ASAS-CSAS Joint Annual Meeting · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsResidual feed intakeRumpAnimal sciencePregnancyPurebredWeight gainInseminationArtificial inseminationEstrous cycleBiologyMedicineBody weightEndocrinologyFeed conversion ratioBreed
DOInot available

Abstract

fetched live from OpenAlex

Selection for residual feed intake (RFI) in cattle will bring about changes in metabolism and physiology that are not explicitly known. We appraised feed intake and feeding behaviour in heifers, characterized by a range of RFI, when fed two different planes of nutrition from day 30 to 150 of pregnancy. Sixty-nine purebred Angus heifers, with RFIF (RFI corrected for fat, ave=0.047, SD=0.7678) measured in adolescence, entered a GrowSafe® automated feed intake recording system after confirmation of pregnancy at 30-days post artificial insemination (AI). Heifers were divided randomly, yet equally in terms of RFIF, weight at start of test (SOT), and SOT rib and rump fat, into 2 diet-groups. Heifers received a ration formulated to allow gain of either 0.5 kg/d (L-diet), or 0.7 kg/d (H-diet). Rations were fed until 150 d of pregnancy, and were adjusted periodically to account for heifer and fetal growth plus decreasing fall temperatures. Heifer weight, as well as rib and rump fat were measured approximately once every 4 weeks, and individual feed intake and feeding behaviour was continuously measured by GrowSafe®. All weight and fat measurements, feed intake and behaviour were analyzed using PROC GLM in SAS 9.0, with RFIF, diet (Hor L-diet), RFIF*diet, and AI (1st or 2nd) included in the model. There were no significant differences due to RFIF, diet, or their interaction on SOT weight, or SOT rib and rump fat. By end of test (EOT), significant diet effects were seen upon EOT weight, both EOT rib and rump fat, and ADG during the feed trial (P<0.01), with heifers consuming H-diet displaying higher weights and fat measurements, but no effect of RFIF was detected. However, significant diet and RFIF effects were detected in average daily intake, feeding duration and head-down time (P<0.05), where heifers with lower RFIF ate less, had a lower average daily feeding duration and head-down time, than those with higher RFIF. Therefore, regardless of diet consumed and under limiting nutritional conditions, low RFIF pregnant heifers ate less, yet maintained the same growth and body condition when compared to high RFIF pregnant heifers. This result is important as RFIF is typically measured on virgin animals and under ad-libitum conditions. If selection for RFI is to become mainstream in the cattle industry, investigating the performance of high and low RFI animals in different nutritional environments and physiological conditions is important. RFI (residual feed intake): is a measurement of how much an animal eats either above or below what is estimated from its weight and stage of growth. Cattle with divergent RFI process and store the energy they receive from feedstuffs differently. Nutrition : Maternal nutrition during pregnancy has been shown to have permanent effects on post-natal growth and development of offspring. Depending upon the time and type of nutritional treatment, these effects may target different biological pathways, and possibly become multi-generational. Results of feed trial during gestation: • Regardless of diet fed during pregnancy, low RFIF heifers exhibited different feeding behaviors and continued to eat less than high RFIF heifers (Table 1, Figure 1), although no differences were seen in weight gain or ultrasound rib and rump fat (Table 2). • As RFI of the heifer became positive (less efficient), average daily intake (Intake AVE), feeding event duration average (Duration AVE), and average time the animal spent detected by GrowSafe (Head-down AVE), increased (Figure 1-3). Significance: Usually RFI is measured in the feedlot under ad-libitum conditions. In this experiment we have seen that heifers with a lower RFI (more efficient) as initially measured while virgin and under ad-libitum conditions, still eat less while pregnant and on a restricted diet while maintaining similar growth and body condition, as compared to heifers with a more positive RFI (less efficient). Table 1. Significance (P) values for the influence of residual feed intake as measured as a heifer (RFIF), and level of diet restriction (Diet), on feeding behaviour characteristics measured by GrowSafe. Trait RFIF1 Diet2 RFIF*Diet Intake AVE <.0001 <.0001 0.0776 Duration AVE 0.0239 <.0001 0.9105 Head-down AVE3 0.0004 <.0001 0.7534 Event AVE 0.6334 0.0402 0.2815 1Residual feed intake corrected for backfat. 2Two diets: High (formulated to gain 0.7 kg/day), and Low (0.5 kg/day). 3Square-root of Head-down AVE. Table 2. Significance (P) values for the influence of residual feed intake as measured as a heifer (RFIF), and level of diet restriction (Diet), on growth and fat characteristics of heifers during the feed trial. Trait RFIF1 Diet2 RFIF*Diet AI3 SOTweight 0.1568 0.2229 0.4634 <.0001 EOTweight 0.2656 0.0028 0.2407 0.0021 ADGFeedTrial 0.8757 <.0001 0.2211 0.032 SOTribfat 0.9066 0.9288 0.2238 0.784 EOTribfat 0.3781 <.0001 0.4117 0.0176 SOTrumpfat 0.6926 0.7868 0.1765 0.5426 EOTrumpfat 0.9207 <.0001 0.9069 0.0381 1Residual feed intake corrected for backfat. 2Two diets: High (formulated to gain 0.7 kg/day), and Low (0.5 kg/day). 3Pregnant at 1st or 2nd AI. Figure 1. Intake AVE increases as RFI becomes positive, regardless of diet fed. Figure 2. Duration AVE increases as RFI becomes positive, regardless of diet fed. Figure 3. Head-down AVE increases as RFI becomes positive, regardless of diet fed. In ta ke A V E

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.223
Teacher spread0.204 · 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.

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
Published2014
Admission routes1
Has abstractyes

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