Literature DB >> 25192352

Phenotypic and genetic relationships between growth and feed intake curves and feed efficiency and amino acid requirements in the growing pig.

R Saintilan1, L Brossard2, B Vautier2, P Sellier1, J Bidanel3, J van Milgen2, H Gilbert1.   

Abstract

Improvement of feed efficiency in pigs has been achieved essentially by increasing lean growth rate, which resulted in lower feed intake (FI). The objective was to evaluate the impact of strategies for improving feed efficiency on the dynamics of FI and growth in growing pigs to revisit nutrient recommendations and strategies for feed efficiency improvement. In 2010, three BWs, at 35±2, 63±9 and 107±7 kg, and daily FI during this period were recorded in three French test stations on 379 Large White and 327 French Landrace from maternal pig populations and 215 Large White from a sire population. Individual growth and FI model parameters were obtained with the InraPorc® software and individual nutrient requirements were computed. The model parameters were explored according to feed efficiency as measured by residual feed intake (RFI) or feed conversion ratio (FCR). Animals were separated in groups of better feed efficiency (RFI- or FCR-), medium feed efficiency and poor feed efficiency. Second, genetic relationships between feed efficiency and model parameters were estimated. Despite similar average daily gains (ADG) during the test for all RFI groups, RFI- pigs had a lower initial growth rate and a higher final growth rate compared with other pigs. The same initial growth rate was found for all FCR groups, but FCR- pigs had significantly higher final growth rates than other pigs, resulting in significantly different ADG. Dynamic of FI also differed between RFI or FCR groups. The calculated digestible lysine requirements, expressed in g/MJ net energy (NE), showed the same trends for RFI or FCR groups: the average requirements for the 25% most efficient animals were 13% higher than that of the 25% least efficient animals during the whole test, reaching 0.90 to 0.95 g/MJ NE at the beginning of the test, which is slightly greater than usual feed recommendations for growing pigs. Model parameters were moderately heritable (0.30±0.13 to 0.56±0.13), except for the precocity of growth (0.06±0.08). The parameter representing the quantity of feed at 50 kg BW showed a relatively high genetic correlation with RFI (0.49±0.14), and average protein deposition between 35 and 110 kg had the highest correlation with FCR (-0.76±0.08). Thus, growth and FI dynamics may be envisaged as breeding tools to improve feed efficiency. Furthermore, improvement of feed efficiency should be envisaged jointly with new feeding strategies.

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Year:  2014        PMID: 25192352     DOI: 10.1017/S1751731114002171

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  9 in total

1.  Response to a selection index including environmental costs and risk preferences of producers.

Authors:  Beshir M Ali; John W M Bastiaansen; Yann de Mey; Alfons G J M Oude Lansink
Journal:  J Anim Sci       Date:  2019-01-01       Impact factor: 3.159

Review 2.  Review: divergent selection for residual feed intake in the growing pig.

Authors:  H Gilbert; Y Billon; L Brossard; J Faure; P Gatellier; F Gondret; E Labussière; B Lebret; L Lefaucheur; N Le Floch; I Louveau; E Merlot; M-C Meunier-Salaün; L Montagne; P Mormede; D Renaudeau; J Riquet; C Rogel-Gaillard; J van Milgen; A Vincent; J Noblet
Journal:  Animal       Date:  2017-01-25       Impact factor: 3.240

3.  Finishing pigs that are divergent in feed efficiency show small differences in intestinal functionality and structure.

Authors:  Barbara U Metzler-Zebeli; Peadar G Lawlor; Elizabeth Magowan; Ursula M McCormack; Tânia Curião; Manfred Hollmann; Reinhard Ertl; Jörg R Aschenbach; Qendrim Zebeli
Journal:  PLoS One       Date:  2017-04-05       Impact factor: 3.240

4.  Proteomic analysis indicates that mitochondrial energy metabolism in skeletal muscle tissue is negatively correlated with feed efficiency in pigs.

Authors:  Liangliang Fu; Yueyuan Xu; Ye Hou; Xiaolong Qi; Lian Zhou; Huiying Liu; Yu Luan; Lu Jing; Yuanxin Miao; Shuhong Zhao; Huazhen Liu; Xinyun Li
Journal:  Sci Rep       Date:  2017-03-27       Impact factor: 4.379

5.  Evaluating environmental impacts of selection for residual feed intake in pigs.

Authors:  T Soleimani; H Gilbert
Journal:  Animal       Date:  2020-06-22       Impact factor: 3.240

6.  Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs.

Authors:  Farouk Messad; Isabelle Louveau; David Renaudeau; Hélène Gilbert; Florence Gondret
Journal:  BMC Genomics       Date:  2021-07-03       Impact factor: 3.969

7.  Unraveling the Fecal Microbiota and Metagenomic Functional Capacity Associated with Feed Efficiency in Pigs.

Authors:  Hui Yang; Xiaochang Huang; Shaoming Fang; Maozhang He; Yuanzhang Zhao; Zhenfang Wu; Ming Yang; Zhiyan Zhang; Congying Chen; Lusheng Huang
Journal:  Front Microbiol       Date:  2017-08-15       Impact factor: 5.640

8.  A global comparison of the microbiome compositions of three gut locations in commercial pigs with extreme feed conversion ratios.

Authors:  Jianping Quan; Gengyuan Cai; Jian Ye; Ming Yang; Rongrong Ding; Xingwang Wang; Enqin Zheng; Disheng Fu; Shaoyun Li; Shenping Zhou; Dewu Liu; Jie Yang; Zhenfang Wu
Journal:  Sci Rep       Date:  2018-03-14       Impact factor: 4.379

9.  Effects of sire line, birth weight and sex on growth performance and carcass traits of crossbred pigs under standardized environmental conditions.

Authors:  Kathrin Elbert; Neal Matthews; Ralf Wassmuth; Jens Tetens
Journal:  Arch Anim Breed       Date:  2020-11-03
  9 in total

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