Estimation of heritability of feeding behaviour traits and their correlation with production traits in Finnish Yorkshire pigs

Kavlak, A.T.; Uimari, Pekka

doi:10.1111/jbg.12408

Cited by 29 publications

(39 citation statements)

References 31 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is also possible that the prior of the Bayesian animal model overly influenced the estimate. In contrast to other studies that reported heritabilities of ADG and FCR in pigs ranging from 0.05 to 0.48 and 0.22 to 0.40 for ADG and FCR, respectively (Kavlak & Uimari, 2019; Nascimento, Nascimento, Dekkers, & Serão, 2019; Saintilan et al, 2013; Shirali, Varley, & Jensen, 2018; Silva, Lopes, Lopes, & Gasparino, 2019), our heritability estimate of ADG was close to zero and the one of FCR was rather low. Several QTLs were identified for ADG (Shirali et al, 2013; Silva et al, 2019) and FCR (Reyer et al, 2017; Silva et al, 2019), indicating a genetic basis of these traits.…”

Section: Discussioncontrasting

confidence: 71%

Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis

Kasper

Ruiz-Ascacibar

Stoll

et al. 2020

J Animal Breeding Genetics

View full text Add to dashboard Cite

Pig production contributes to environmental pollution through the excretion and emission of nitrogenous compounds and phosphorus (P) (Notarnicola, Tassielli, Renzulli, Castellani, & Sala, 2017; Springmann et al., 2018). Specific legal regulations, such as the Gothenburg Protocol or the Nitrates Directive of the European Commission, strive to close nutrient cycles and reduce adverse effects of manure on sensitive ecosystems. However, closing nutrient cycles can be difficult in areas with both high livestock density and low availability of arable land. The application of pig slurry to agricultural land can also result in excess soil P due to its low N:P ratios (Wienhold, 2005). The ecological impact of livestock production also manifests in large-scale imports of soy, which is a preferred feed component because of its favourable amino acid profile (Bracher, 2019; Wang et al., 2011), from Brazil, Argentina or the USA. The geographical separation of soya bean cultivation and livestock production leads to a

show abstract

Section: Discussioncontrasting

confidence: 71%

Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis

Kasper

Ruiz-Ascacibar

Stoll

et al. 2020

J Animal Breeding Genetics

View full text Add to dashboard Cite

show abstract

“…The mean carcass PE in this study was 0.38, which is in line with the carcass PE of Kasper et al (2020a), who investigated the potential to genetically improve protein efficiency in pigs. The mean FO, DFI and FR observed in this study were slightly lower to those reported by Kavlak and Uimari (2019) (FO: 61 min/d; DFI: 2100 g/d; FR: and 40 g/min) and those reported by Do et al (2013) (FO: 78min/d; DFI: 2340 g/d; FR: 31 g/min). However, the mean feeding behaviour traits reported by Kavlak and Uimari (2019) were recorded over five periods (i.e.…”

Section: Discussioncontrasting

confidence: 88%

“…Such a proxy could be feeding behavior (Ding et al, 2018) that allows the evaluation and selection of animals during their lifetimes without the need for slaughter. Previous studies have reported phenotypic correlations of -0.01 to 0.35 between feed conversion ratio (FCR) and feeding behaviour traits (Carcò et al, 2018;Kavlak and Uimari 2019), and slightly higher correlations of 0.147 to 0.408 were reported between feeding behaviour traits and protein retention (Carcò et al, 2018). In addition, an increasing number of farms are using computerized feeding systems that automatically record individual feeding patterns without disturbing the animals.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of feeding behaviour traits to predict efficiency traits in pigs using partial least square regression

Ewaoluwagbemiga¹,

Bee²,

Kasper³

2020

Preprint

View full text Add to dashboard Cite

The objective of this study was to explore the potential of using automatically recorded feeding behaviour as a proxy for protein efficiency (PE) by investigating the relationship between feeding behaviour and PE. A total of 402 Swiss Large White pigs were used in this experiment (204 females and 198 castrated males). Pigs were fed ad libitum on a reduced protein diet (80% of standard) from 20kg to 100kg BW. Individual daily feed intake was monitored and carcass composition at slaughter was determined by dual-energy X-ray absorptiometry (DXA). The PE was calculated as the ratio of protein in the carcass (estimated by DXA) to the total protein consumed. Feeding behaviour traits monitored were daily feed intake (DFI; g/day), feed intake per visit (FIV; g/visit), number of daily visits (NDV; visits/day), duration of visits (DUV; min/visit), feeding rate (FR; g/min), and feeder occupation (FO; min/day). Regression analysis was used to estimate the relationship between PE and feeding behaviour, while correcting for the effects of sex, experimental series and age. Weak Pearson’s correlations (−0.25 to 0.12) were found between PE and feeding behaviour traits. Beta (β) estimates from this analysis for feeding behaviours were also very low (0.0093% to 0.087%). An increase in FR (g/min) will increase PE by 0.087% and an increase in DFI (g/day) will decrease PE by 0.0093%. In conclusion, feeding behaviours are not suitable for the identification of protein-efficient pigs, as estimates are negligible.ImplicationsThis study suggests that feeding behaviour traits recorded via automatic feeder are not reliable predictors of protein efficiency in Swiss Large White pigs receiving a protein-reduced diet. Despite the large differences in protein efficiency, only negligible changes in a range of feeding behaviours were observed. Hence, feeding behaviours are not suitable proxies for the high-throughput phenotyping of protein efficiency and the selection of live animals for use in nutrition experiments or for breeding.

show abstract

“…Most of the estimated variance components are of similar magnitude to those from other studies. For example, the direct genetic variance (3349 ± 451) is marginally greater than those published by other studies (2762, 2521, and 3200) [ 9 , 47 – 49 ]; the direct litter variance (633 ± 131) is in the range of those published by other studies (984, 652, and 1576) [ 9 , 47 , 48 ]; and the residual variance (10,989 ± 295) was greater than those published by other studies (7246, 5439, and 10,000) [ 9 , 47 – 49 ]. We may have obtained greater estimates of residual variances because of how we modelled contemporary groups (HYM).…”

Section: Discussionmentioning

confidence: 57%

Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information

Poulsen¹,

Ask

Nielsen³

et al. 2020

Genet Sel Evol

View full text Add to dashboard Cite

Background Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ($${\mathbf{A}}$$ A ) and a combined pedigree and genomic relationship matrix ($${\mathbf{H}}$$ H ); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). Results The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when $${\mathbf{H}}$$ H was used rather than $${\mathbf{A}}$$ A . Conclusions This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with $${\mathbf{H}}$$ H than $${\mathbf{A}}$$ A ; (3) using $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A primarily improves the predictive performance of direct genetic effects.

show abstract

Estimation of heritability of feeding behaviour traits and their correlation with production traits in Finnish Yorkshire pigs

Cited by 29 publications

References 31 publications

Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis

Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis

Evaluation of feeding behaviour traits to predict efficiency traits in pigs using partial least square regression

Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information

Contact Info

Product

Resources

About