Integrating economic parameters into genetic selection for Large White pigs

Evaluation of direct and maternal responses in reproduction traits based on different selection strategies for postnatal piglet survival in a selection experiment

BACKGROUND

Postnatal piglet survival is important both in economic and animal welfare terms. It is influenced by the piglet's own direct genetic effects and by maternal genetic effects of the dam, associated with milk production and mothering abilities. These genetic effects might be correlated, affected by other non-genetic factors and unfavourably associated with other reproduction traits such as litter size, which makes the development of optimal breeding strategies a challenge. To identify the optimum selection strategy for piglet survival, a selection experiment was carried out to compare responses in survival and reproduction traits to selection on only direct, only maternal, or both genetic effects of postnatal survival. The data of the experiment were recorded from outdoor reared pigs, with first- and second-generation sires selected based on their estimated breeding values for maternal and direct effects of postnatal survival of indoor reared offspring, respectively, with the opportunity to identify potential genotype-by-environment interaction.

RESULTS

A Bayesian multivariate threshold-linear model that was fitted to data on 22,483 piglets resulted in significant (Pr(h2 > 0) = 1.00) estimates of maternal and direct heritabilities between 0.12 and 0.18 for survival traits and between 0.29 and 0.36 for birth weight, respectively. Selection for direct genetic effects resulted in direct and maternal responses in postnatal survival of 1.11% ± 0.17 and - 0.49% ± 0.10, respectively, while selection for maternal genetic effects led to greater direct and maternal responses, of 5.20% ± 0.34 and 1.29% ± 0.20, respectively, in part due to unintentional within-litter selection. Selection for both direct and maternal effects revealed a significant lower direct response (- 1.04% ± 0.12) in comparison to its expected response from single-effect selection, caused by interactions between direct and maternal effects.

CONCLUSIONS

Selection successfully improved post- and perinatal survival and birth weight, which indicates that they are genetically determined and that genotype-by-environment interactions between outdoor (experimental data) and indoor (selection data) housed pigs were not important for these traits. A substantially increased overall (direct plus maternal) response was obtained using selection for maternal versus direct or both direct and maternal effects, suggesting that the maternal genetic effects are the main limiting factor for improving piglet survival on which selection pressure should be emphasized.

Economic weights of maternal and direct traits of pigs calculated by applying gene flow methods

Multiple trait selection indexes in pig breeding programmes should take into account the population structure and time delay between parent selection and expressions of traits in all production levels next to the trait impacts on economic efficiency of production systems. Gene flow procedures could be used for the correct evaluation of maternal and direct traits of pig breeds involved in breeding or crossbreeding systems. Therefore, the aim of this study was to expand a previously developed bioeconomic model and computer program to calculate the marginal economic values by including a gene flow procedure to calculate the economic weights for maternal and direct traits in pig breeds. The new program was then applied to the three-way crossbreeding system of the Czech National Programme for Pig Breeding. Using this program, the marginal economic values of traits for dam breeds Czech Large White in the dam position and Czech Landrace in the sire position, and for the sire breed Pietrain were weighted by the number of discounted gene expressions of selected parents of each breed summarised within all links of the crossbreeding system during the 8-year investment period. Economic weights calculated in this way were compared with the approximate economic weights calculated previously without a gene flow procedure. Taking into account the time delay between parent selection and trait expression (using discounting with half-year discount rates of 2% or 5%) and including more than one generation of parent progeny had little impact on the relative economic importance of maternal and direct traits of breeds involved in the evaluated three-way crossbreeding system. These results indicated that this gene-flow method could be foregone when estimating the relative economic weights of traits in pig crossbreeding systems applying artificial insemination at all production levels.

Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line

Background

Improving feed efficiency (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI) should be of value for further research on biological aspects of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE. Here, we present a random regression model that extends the classical definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE components: use of feed for growth (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG), use of feed for backfat deposition (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG), use of feed for maintenance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW), and unspecific efficiency in the use of feed (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI). Expected response to alternative selection indexes involving different components is also studied.

Results

Based on goodness-of-fit to the available feed intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. The estimated heritabilities of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI using the model that accounts for animal-specific needs and the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG, respectively. Estimates of genetic correlations of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI were positive with amount of feed used for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG but negative for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW. Expected response in overall efficiency, reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI was considered.

Conclusions

Expected response in overall efficiency, by reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. The relatively small difference between the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. Overall, a model that accounts for animal-specific needs for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG is statistically superior and allows for the possibility to act differentially on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE components.

Production parameters and pig production cost: temporal evolution 2010-2014

Background

The assessment of the cost of production and the relative weight of the different production parameters is very important in pig farming. The goals of the present work were 1) to describe reliable reference values for production parameters and pig production cost from 2010 to 2014, 2) to describe their temporal evolution and 3) to determine the influence of the pig company size on them. Between 61 and 107 pig production companies from Spain were included in this study from 2010 to 2014. These companies sent data on feed consumption, number of pig produced, expenses and census every month. Sip consultors SL standardized collected data and calculate cost and production parameters to obtain values comparables between the different pig production companies. The collected data each month were merged to obtain a yearly average value taking into account the pig production flow each month. A suitable statistical analysis was carried out to tackle the goals.

Results

The production performance has been continuously improving in the piglet production and fattening phase from 2010 to 2014. Thus, the number of piglets by sow and year will increase 0.5 pigs by year and the total feed conversion rate will decrease approximately 0.03 kg feed/kg gain by year in the future if the same tendency continues. However, feed price has been steadily increasing from 2010 to 2012 and decreasing afterwards and the total cost per kilogram produced has followed a similar pattern. This result highlights the relevance of the feed price in the final cost in spite of continuous improvement in production performance across years. Finally, pig company size affected most of the production parameters studied. Thus, the best technical parameters were obtained for companies with less than 5000 sows. However, the opposite tendency is observed for feed price where the highest value was observed for the smallest companies.

Conclusions

Pig production parameters have generally improved in the last five years but this improvement did not directly imply a reduction in pig production cost due to the high feed prices during the period 2010–2013.

Electronic supplementary material

The online version of this article (doi:10.1186/s40813-016-0027-0) contains supplementary material, which is available to authorized users.

Economic weights for performance and survival traits of growing pigs

The objective of this paper was to derive economic weights for performance and survival traits of growing pigs including feed conversion ratio (FCR), daily feed intake (DFI), ADG, postweaning survival of the growing pig (SG), and carcass fat depth at the P2 site (CFD). An independent model was developed for each trait to derive economic values directly based on a typical Australian production system. This flexible approach may be used to customize economic values for different production systems and alternative trait combinations in breeding objectives. Discounted genetic expressions were used as a means of taking into account differences in frequency and timing of expression of traits to obtain economic weights. Economic values for SG were derived based on a cost-saving and a lost-revenue approach. The correct formulation of the economic value of ADG depends on how feed cost is included in the breeding objective. If FCR is defined as a breeding objective trait, then savings in feed costs through earlier slaughter should not be counted in the economic value of ADG. In contrast, if DFI is included in the breeding objective instead of FCR, then feed-cost savings through earlier slaughter need to be attributed to the economic value for ADG, as a benefit from faster ADG. The paper also demonstrates that economic weightings in indexes for FCR can potentially be overestimated by 70% when it is assumed that DFI or FCR records taken from a limited duration test period reflect the corresponding trait over the full lifetime of the growing pig destined for slaughter. Postweaning survival of the growing pig was the most important breeding objective trait of growing pigs. The relative importance of each breeding objective trait in a sire-line index based on the genetic SD of each trait was 44.5, 27.0, 17.4, and 11.1% for SG, FCR, ADG, and CFD, respectively. Further studies to better clarify the extent of genetic variation that exists in SG under nucleus-farm and commercial-farm conditions are warranted, given the high economic importance of this survival trait of growing pigs.

Economic weights for maternal traits of sows, including sow longevity

The objective of this study was to develop a transparent, comprehensive, and flexible model for each trait for the formulation of breeding objectives for sow traits in swine breeding programs. Economic values were derived from submodels considering a typical Australian pig production system. Differences in timing and expressions of traits were accounted for to derive economic weights that were compared on the basis of their relative size after multiplication by their corresponding genetic standard deviation to account for differences in scale and genetic variability present for each trait. The number of piglets born alive had the greatest contribution (27.1%) to a subindex containing only maternal traits, followed by daily gain (maternal; 22.0%) and sow mature weight (15.0%). Other traits considered in the maternal breeding objective were preweaning survival (11.8%), sow longevity (12.5%), gilt age at puberty (8.7%), and piglet survival at birth (3.1%). The economic weights for number of piglets born alive and preweaning piglet survival were found to be highly dependent on the definition of scale of enterprise, with each economic value increasing by approximately 100% when it was assumed that the value of extra output per sow could be captured, rather than assuming a consequent reduction in the number of sows to maintain a constant level of output from a farm enterprise. In the context of a full maternal line index that must account also for the expression of direct genetic traits by the growing piglet progeny of sows, the maternal traits contributed approximately half of the variation in the overall breeding objective. Deployment of more comprehensive maternal line indexes incorporating the new maternal traits described would lead to more balanced selection outcomes and improved survival of pigs. Future work could facilitate evaluation of the economic impacts of desired-gains indexes, which could further improve animal welfare through improved sow and piglet survival. The results justify further development of selection criteria and breeding value prediction systems for a wider range of maternal traits relevant to pig production systems.

Novel variation in the FABP3 promoter and its association with fatness traits in pigs

This study examines associations between SNPs in the promoter region of the fatty acid binding protein 3 (FABP3) gene and fatness traits in pure bred Large White (n=98), Duroc (n=99) and Pietrain (n=98) populations. In the Large White breed, SNP g.-634 C>A was associated a 27% increase in IMF (%) in the heterozygote (CA) and a 38% increase in the homozygote (CC) relative to the (AA) genotype in the M. semimembranosus (SM) muscle (P=0.02). While the associations observed in this breed were suggestive of significance in both the SM and in the M. longissimus thoracis et lumborum (LTL) (P=0.08), these associations no longer attained significance at thresholds adjusted for multiple testing. In conclusion, SNPs in the FABP3 promoter may contribute to IMF without influencing carcass fatness traits in pigs, however further confirmation of these associations in larger independent populations would be essential before their incorporation into breeding programmes.