Estimating the environmental impact of dairy cattle breeding programs through emission intensity

A recently developed methodological approach for determining the greenhouse gas emissions impact of national breeding programs was applied to measure the effects of current and future breeding goals on the emission intensity (EI) of the Canadian dairy industry. Emission intensity is the ratio of greenhouse gas outputted in comparison to the product generated. Traits under investigation affected EI by either decreasing the direct emissions yield (i.e. increasing feed performance), changing herd structure (i.e. prolonging herd life) or through the dilution effect of increased production (i.e. increasing fat yield). The intensity value (IV) of each trait, defined as the change in emissions’ intensity per unit change in each trait, was calculated for each of the investigated traits. The IV trend of these traits was compared for the current and prospective selection index, as well as for a system with and without quota (the supply management policy designed to prevent overproduction). The overall EI of the average genetic merit Canadian dairy herd per breeding female was 5.07 kg CO₂eq/kg protein equivalent output. The annual reduction in EI due to the improvement of production traits was −0.027, −0.018 and −0.006 for fat, protein and milk other solids, respectively. The functional traits, herd life and mastitis resistance, had more modest effects (−0.008 and −0.001, respectively). These results are consistent with international studies that identified traits related to production, survival, health and fertility as having the largest impact on the environmental footprint of dairy cattle. Overall, the dairy industry is becoming more efficient by reducing its EI through selection of environmentally favorable traits, with a 1% annual reduction of EI in Canada.     

Prediction of effects of beef selection indexes on greenhouse gas emissions

Genetic improvement in production efficiency traits can also drive reduction in greenhouse gas emissions. This study used international ‘best-practice’ methodology to quantify the improvements in system-wide CO₂ equivalent emissions per unit of genetic progress in the Irish Maternal Replacement (MR) and Terminal (T) beef cattle indexes. Effects of each index trait on system gross emissions (GE) and system emissions intensity (EI) were modelled by estimating effects of trait changes on per-animal feed consumption and associated methane production, per-animal meat production and numbers of animals in the system. Trait responses to index selection were predicted from linear regression of individual bull estimated breeding values for each index trait on their MR or T index value, and the resulting regression coefficients were used to calculate trait-wise responses in GE and EI from index selection. Summed over all trait responses, the MR index was predicted to reduce system GE by 0.810 kg CO₂e/breeding cow per year per € index and system EI by 0.009 kg CO₂e/kg meat per breeding cow per year per € index. These reductions were mainly driven by improvements in cow survival, reduced mature cow maintenance feed requirements, shorter calving interval and reduced offspring mortality. The T index was predicted to reduce system EI by 0.021 kg CO₂e/kg meat per breeding cow per year per € index, driven by increased meat production from improvements in carcass weight, conformation and fat. Implications for incorporating an EI reduction index to the current production indexes and long-term projections for national breeding programs are discussed.     

Farmer views on calving difficulty consequences on dairy and beef farms

Calving difficulty (CD) is a key functional trait with significant influence on herd profitability and animal welfare. Breeding plays an important role in managing CD both at farm and industry level. An alternative to the economic value approach to determine the CD penalty is to complement the economic models with the analysis of farmer perceived on-farm impacts of CD. The aim of this study was to explore dairy and beef farmer views and perceptions on the economic and non-economic on-farm consequences of CD, to ultimately inform future genetic selection tools for the beef and dairy industries in Ireland. A standardised quantitative online survey was released to all farmers with e-mail addresses on the Irish Cattle Breeding Federation database. In total, 271 farmers completed the survey (173 beef farmers and 98 dairy farmers). Both dairy and beef farmers considered CD a very important issue with economic and non-economic components. However, CD was seen as more problematic by dairy farmers, who mostly preferred to slightly reduce its incidence, than by beef farmers, who tended to support increases in calf value even though it would imply a slight increase in CD incidence. Farm size was found to be related to dairy farmer views of CD with farmers from larger farms considering CD as more problematic than farmers from smaller farms. CD breeding value was reported to be critical for selecting beef sires to mate with either beef or dairy cows, whereas when selecting dairy sires, CD had lower importance than breeding values for other traits. There was considerable variability in the importance farmers give to CD breeding values that could not be explained by the farm type or the type of sire used, which might be related to the farmer non-economic motives. Farmer perceived economic value associated with incremental increases in CD increases substantially as the CD level considered increases. This non-linear relationship cannot be reflected in a standard linear index weighting. The results of this paper provide key underpinning support to the development of non-linear index weightings for CD in Irish national indexes.     

Impact of including growth,carcass and feed efficiency traits in the breeding goal for combined milk and beef production systems

Improving feed efficiency in dairy cattle could result in more profitable and environmentally sustainable dairy production through lowering feed costs and emissions from dairy farming. In addition, beef production based on dairy herds generates fewer greenhouse gas emissions per unit of meat output than beef production from suckler cow systems. Different scenarios were used to assess the profitability of adding traits, excluded from the current selection index for Finnish Ayrshire, to the breeding goal for combined dairy and beef production systems. The additional breeding goal traits were growth traits (average daily gain of animals in the fattening and rearing periods), carcass traits (fat covering, fleshiness and dressing percentage), mature live weight (LW) of cows and residual feed intake (RFI) traits. A breeding scheme was modeled for Finnish Ayrshire under the current market situation in Finland using the deterministic simulation software ZPLAN+. With the economic values derived for the current production system, the inclusion of growth and carcass traits, while preventing LW increase generated the highest improvement in the discounted profit of the breeding program (3.7%), followed by the scenario where all additional traits were included simultaneously (5.1%). The use of a selection index that included growth and carcass traits excluding LW, increased the profit (0.8%), but reduced the benefits resulted from breeding for beef traits together with LW. A moderate decrease in the profit of the breeding program was obtained when adding only LW to the breeding goal (−3.1%), whereas, adding only RFI traits to the breeding goal resulted in a minor increase in the profit (1.4%). Including beef traits with LW in the breeding goal showed to be the most potential option to improve the profitability of the combined dairy and beef production systems and would also enable a higher rate of self-sufficiency in beef. When considering feed efficiency related traits, the inclusion of LW traits in the breeding goal that includes growth and carcass traits could be more profitable than the inclusion of RFI, because the marginal costs of measuring LW can be expected to be lower than for RFI and it is readily available for selection. In addition, before RFI can be implemented as a breeding objective, the genetic correlations between RFI and other breeding goal traits estimated for the studied population as well as information on the most suitable indicator traits for RFI are needed to assess more carefully the consequences of selecting for RFI.     

Estimation of genetic parameters for young stock survival in beef x dairy crossbred calves

Young stock survival is a trait of crucial importance in cattle breeding as calf mortality leads to economic losses and represents an animal welfare issue. The aim of this study was to estimate genetic parameters and sire breeding values for young stock survival in beef x dairy crossbred calves. Two traits were analysed with a univariate animal model: young stock survival between 1 to 30 days and 31 to 200 days after birth. Breed combinations with Belgian Blue sires outperformed all other sire breeds. The lowest survival rates were found for breed combinations with Jersey dams or Blonde d’Aquitaine sires. The results showed low but significant heritabilities (0.045 to 0.075) for both survival traits. Differences in breeding values between sires ranged from −2.5% to 3.5% and from −5.4% to 4.7% survival from 1 to 30 days and 31 to 200 days, respectively. Based on these findings, we concluded that it is feasible to breed for improved young stock survival in beef x dairy crossbred calves. This will hopefully contribute to increasing the survival rate of the calves and reduce economic losses for the farmers.     

A bivariate quantitative genetic model for a threshold trait and a survival trait

Many of the functional traits considered in animal breeding can be analyzed as threshold traits or survival traits with examples including disease traits, conformation scores, calving difficulty and longevity. In this paper we derive and implement a bivariate quantitative genetic model for a threshold character and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted in which model parameters were augmented with unobserved liabilities associated with the threshold trait. The fully conditional posterior distributions associated with parameters of the threshold trait reduced to well known distributions. For the survival trait the two baseline Weibull parameters were updated jointly by a Metropolis-Hastings step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. The Gibbs sampler was tested in a simulation study and illustrated in a joint analysis of calving difficulty and longevity of dairy cattle. The simulation study showed that the estimated marginal posterior distributions covered well and placed high density to the true values used in the simulation of data. The data analysis of calving difficulty and longevity showed that genetic variation exists for both traits. The additive genetic correlation was moderately favorable with marginal posterior mean equal to 0.37 and 95% central posterior credibility interval ranging between 0.11 and 0.61. Therefore, this study suggests that selection for improving one of the two traits will be beneficial for the other trait as well.     

Predicted accuracy of and response to genomic selection for new traits in dairy cattle

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.     

Economic aspects of implementing genomic evaluations in a pig sire line breeding scheme

Background

Replacing pedigree-based BLUP evaluations by genomic evaluations in pig breeding schemes can result in greater selection accuracy and genetic gains, especially for traits with limited phenotypes. However, this methodological change would generate additional costs. The objective of this study was to determine whether additional expenditures would be more profitably devoted to implementing genomic evaluations or to increasing phenotyping capacity while retaining traditional evaluations.

Methods

Stochastic simulation was used to simulate a population with 1050 breeding females and 50 boars that was selected for 10 years for a breeding goal with two uncorrelated traits with heritabilities of 0.4. The reference breeding scheme was based on phenotyping 13 770 candidates per year for trait 1 and 270 sibs of candidates per year for trait 2, with selection based on pedigree-based BLUP estimated breeding values. Increased expenditures were allocated to either increasing the phenotyping capacity for trait 2 while maintaining traditional evaluations, or to implementing genomic selection. The genomic scheme was based on two training populations: one for trait 2, consisting of phenotyped sibs of the candidates whose number increased from 1000 to 3430 over time, and one for trait 1, consisting of the selection candidates. Several genomic scenarios were tested, where the size of the training population for trait 1, and the number of genotyped candidates pre-selected based on their parental estimated breeding value, varied.

Results

Both approaches resulted in higher genetic trends for the population breeding goal and lower rates of inbreeding compared to the reference scheme. However, even a very marked increase in phenotyping capacity for trait 2 could not match improvements achieved with genomic selection when the number of genotyped candidates was large. Genotyping just a limited number of pre-selected candidates significantly reduced the extra costs, while preserving most of the benefits in terms of genetic trends and inbreeding. Implementing genomic evaluations was the most efficient approach when major expenditure was possible, whereas increasing phenotypes was preferable when limited resources were available.

Conclusions

Economic decisions on implementing genomic evaluations in a pig nucleus population must take account of population characteristics, phenotyping and genotyping costs, and available funds.     

Economic weights of fertility, prolificacy, milk yield and longevity in dairy sheep

Economic weights have been estimated in two breeds (Latxa and Manchega) using economic and technical data collected in 41 Latxa and 12 Manchega dairy sheep flocks. The traits considered were fertility (lambing per year), prolificacy (number of lambs), milk yield (litres) and longevity (as productive life, in years). A linear function was used, relating these traits to the different costs in the flock. The variable costs involved in the profit function were feed and labour. From this function, economic weights were obtained. Labour is considered in the Latxa breed to be a constraint. Moreover, farm profits are unusually high, which probably means that some costs were not included according to the economic theory. For that reason, a rescaling procedure was applied constraining total labour time at the farm. Genetic gains were estimated with the resulting economic weights to test if they give any practical difference. Milk yield only as selection criterion was also considered. The medians of the estimated economic weights for fertility, prolificacy, milk yield and longevity were 138.60 € per lambing, 40.00 € per lamb, 1.18 € per l, 1.66 € per year, and 137.66 € per lambing, 34.17 € per lamb, 0.73 € per l, 2.16 € per year under the linear approach in the Latxa and Manchega breeds respectively. Most differences between breeds can be related to differences in production systems. As for the genetic gains, they were very similar for all economic weights, except when only milk yield was considered, where a correlated decrease in fertility led to a strong decrease in profit. It is concluded that the estimates are robust for practical purposes and that breeding programmes should consider inclusion of fertility. More research is needed to include other traits such as somatic cell score, milk composition and udder traits.     

Invited review: Piglet survival: benefits of the immunocompetence

Piglet mortality has a negative impact on animal welfare and public acceptance. Moreover, the number of weaned piglets per sow mainly determines the profitability of piglet production. Increased litter sizes are associated with lower birth weights and piglet survival. Decreased survival rates and performance of piglets make the control of diseases and infections within pig production even more crucial. Consequently, selection for immunocompetence becomes an important key aspect within modern breeding programmes. However, the phenotypic recording of immune traits is difficult and expensive to realize within farm routines. Even though immune traits show genetic variability, only few examples exist on their respective suitability within a breeding programme and their relationships to economically important production traits. The analysis of immune traits for an evaluation of immunocompetence to gain a generally improved immune response is promising. Generally, in-depth knowledge of the genetic background of the immune system is needed to gain helpful insights about its possible incorporation into breeding programmes. Possible physiological drawbacks for enhanced immunocompetence must be considered with regards to the allocation theory and possible trade-offs between the immune system and performance. This review aims to discuss the relationships between the immunocompetence of the pig, piglet survival as well as the potential of these traits to be included into a breeding strategy for improved robustness.     

Turning science on robust cattle into improved genetic selection decisions

More robust cattle have the potential to increase farm profitability, improve animal welfare, reduce the contribution of ruminant livestock to greenhouse gas emissions and decrease the risk of food shortages in the face of increased variability in the farm environment. Breeding is a powerful tool for changing the robustness of cattle; however, insufficient recording of breeding goal traits and selection of animals at younger ages tend to favour genetic change in productivity traits relative to robustness traits. This paper has extended a previously proposed theory of artificial evolution to demonstrate, using deterministic simulation, how choice of breeding scheme design can be used as a tool to manipulate the direction of genetic progress, whereas the breeding goal remains focussed on the factors motivating individual farm decision makers. Particular focus was placed on the transition from progeny testing or mass selection to genomic selection breeding strategies. Transition to genomic selection from a breeding strategy where candidates are selected before records from progeny being available was shown to be highly likely to favour genetic progress in robustness traits relative to productivity traits. This was shown even with modest numbers of animals available for training and when heritability for robustness traits was only slightly lower than that for productivity traits. When transitioning from progeny testing to a genomic selection strategy without progeny testing, it was shown that there is a significant risk that robustness traits could become less influential in selection relative to productivity traits. Augmentations of training populations using genotyped cows and support for industry-wide improvements in phenotypic recording of robustness traits were put forward as investment opportunities for stakeholders wishing to facilitate the application of science on robust cattle into improved genetic selection schemes.     

Defining the breeding goal for a sheep breed including production and functional traits using market data

In this study, the economic values for production and functional traits of dairy sheep are estimated through the application of a profit function model using farm-level technical and economic data. The traits incorporated in the model were milk production, prolificacy, fertility, milking speed, longevity and mastitis occurrence. The economic values for these traits were derived as the approximate partial derivative of the specified profit function. A sensitivity analysis was also conducted in order to examine how potential changes in input and output prices would affect the breeding goal. The estimated economic values of the traits revealed their economic impact on the definition of the breeding goal for the specified production system. Milk production and fertility had the highest economic values (€40.30 and €20.28 per standard genetic deviation (SDa)), while, mastitis only had a low negative value of −0.57 €/SDa. Therefore, breeding for clinical mastitis will have a minor impact on farm profitability because it affects a small proportion of the flock and has low additive variance. The production traits, which include milk production, prolificacy and milking speed, contributed most to the breeding goal (70.0%), but functional traits still had a considerable share (30.0%). The results of this study highlight the importance of the knowledge of economic values of traits in the design of a breeding program. It is also suggested that the production and functional traits under consideration can be categorized as those which can be efficiently treated through genetic improvement (e.g. milk production and fertility) while others would be better dealt with through managerial interventions (e.g. mastitis occurrence). Also, sub-clinical mastitis that affects a higher proportion of flocks could have a higher contribution to breeding goals.     

Expected early genetic gain from selection for milk yield in dairy cattle

Summary A matrix program to predict short term genetic gain from single trait selection for milk yield was developed. Rate of genetic gain was calculated as the annual change in the mean breeding value of all producing females. Several parameters sets representing various selection policies were used to examine situations pertinent to dairy populations of the United States. Approach to the asymptotic rates of genetic gain within the model varied with the choice of parameters, but even with consistent selection policies, predicted total genetic gain in the first 10 years was only half of the expected from classical theory. Considerable year to year variation in the rate of gain occurred. Early gains were more dependent on female selection decisions than gains during the steady state. In a two-phase model, the approach to the linear rate of gain in the second phase was accelerated by starting with an ongoing improvement program, but considerable delays still existed. Selection for sex- limited traits such as milk yield, which require pedigree selection and a waiting time for progeny test results reached asymptotic rates more slowly than previously assumed.     

Developing breeding schemes to assist mitigation of greenhouse gas emissions

Genetic improvement of livestock is a particularly effective technology, producing permanent and cumulative changes in performance. This paper highlights some of the options for including mitigation in livestock breeding schemes, focusing on ruminant species, and details three routes through which genetic improvement can help to reduce emissions per kg product via: (i) improving productivity and efficiency, (ii) reducing wastage in the farming system and (iii) directly selecting on emissions, if or when these are measurable. Selecting on traits that improve the efficiency of the system (e.g. residual feed intake, longevity) will have a favourable effect on the overall emissions from the system. Specific examples of how genetic selection will have a favourable effect on emissions for UK dairy systems are described. The development of breeding schemes that incorporate environmental concerns is both desirable and possible. An example of how economic valuation of public good outcomes can be incorporated into UK dairy selection indices is given. This paper focuses on genetic selection tools using, on the whole, currently available traits and tools. However, new direct and indirect measurement techniques for emissions will improve the potential to reduce emissions by genetic selection. The complexities of global forces on defining selection objectives are also highlighted.     

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture’s contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9–323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.     

Evaluation of the effect of accounting method,IPCC v. LCA,on grass-based and confinement dairy systems’ greenhouse gas emissions

Life cycle assessment (LCA) and the Intergovernmental Panel on Climate Change (IPCC) guideline methodology, which are the principal greenhouse gas (GHG) quantification methods, were evaluated in this study using a dairy farm GHG model. The model was applied to estimate GHG emissions from two contrasting dairy systems: a seasonal calving pasture-based dairy farm and a total confinement dairy system. Data used to quantify emissions from these systems originated from a research study carried out over a 1-year period in Ireland. The genetic merit of cows modelled was similar for both systems. Total mixed ration was fed in the Confinement system, whereas grazed grass was mainly fed in the grass-based system. GHG emissions from these systems were quantified per unit of product and area. The results of both methods showed that the dairy system that emitted the lowest GHG emissions per unit area did not necessarily emit the lowest GHG emissions possible for a given level of product. Consequently, a recommendation from this study is that GHG emissions be evaluated per unit of product given the growing affluent human population and increasing demand for dairy products. The IPCC and LCA methods ranked dairy systems’ GHG emissions differently. For instance, the IPCC method quantified that the Confinement system reduced GHG emissions per unit of product by 8% compared with the grass-based system, but the LCA approach calculated that the Confinement system increased emissions by 16% when off-farm emissions associated with primary dairy production were included. Thus, GHG emissions should be quantified using approaches that quantify the total GHG emissions associated with the production system, so as to determine whether the dairy system was causing emissions displacement. The IPCC and LCA methods were also used in this study to simulate, through a dairy farm GHG model, what effect management changes within both production systems have on GHG emissions. The findings suggest that single changes have a small mitigating effect on GHG emissions (<5%), except for strategies used to control emissions from manure storage in the Confinement system (14% to 24%). However, when several management strategies were combined, GHG emissions per unit of product could be reduced significantly (15% to 30%). The LCA method was identified as the preferred approach to assess the effect of management changes on GHG emissions, but the analysis indicated that further standardisation of the approach is needed given the sensitivity of the approach to allocation decisions regarding milk and meat.     

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication)

In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.     

Selection for litter size and litter birthweight in Large White pigs: Maximum,mean and variability of reproduction traits

Gradually increasing trend of litter size poses a challenge to pig farmers in terms of managing larger litters. Therefore, it seems that a balanced approach that optimises litter size, litter birthweight, and uniformity of those traits is needed in order to address animal welfare and farm management concerns. This study aimed to investigate this issue by defining several traits for total number born (TNB), number born alive (NBA) and litter birthweight (LW). First, the highest value from at least five records per sow was selected as maximum (max) value for each reproduction trait. Second, a mean (mean) for each reproduction trait was calculated per sow. Last, the variability of reproduction traits between parities of the sow was calculated as log-transformed variance of residuals of all observations per sow for each reproduction trait (LnVar). In total, 23 193 Large White sows from Topigs Norsvin with 152 282 litter records were used for analysis in ASReml 4.1. Also, a simulation of breeding schemes was performed with the use of SelAction 2.1 and estimates from genetic analysis. Maximum value of reproductive traits had a much higher heritability than repeated observations or mean of reproduction traits, e.g., 0.31 for maxTNB vs. 0.12 for TNB and 0.07 for meanTNB, which allows for a faster response under selection. The maximum value traits, however, were found to carry more risks, i.e. higher ratio of stillborn (not for maxNBA) and increased variability of traits. Thus, using them in breeding programme should be carefully considered. The genetic coefficient of variation on SD level estimated to indicate the genetic magnitude for variability phenotypes indicated a maximum change of 6–9% in genetic SD of TNB, NBA and LW. The genetic correlations between mean and corresponding variability traits varied from 0.66 to 0.74, whereas the correlation between other mean and variability traits ranged from 0.33 to 0.99. The simulation indicated that even with selection targeted against the variability of reproduction traits, a very limited change should be expected due to a complex genetic and phenotypic relationship between the traits. In the scenarios with selection against LnVarTNB and LnVarLW, this was a decrease of 0.1–0.6% per year, whereas in scenario with selection against LnVarNBA, the range was 0.6–1.1% per year. It is still possible to increase litter size and birthweight further, however, a balance between mean and variability of reproduction traits is required, which can be obtained only by a very well designed breeding programme.     

Application of Genomics Tools to Animal Breeding

The main goal in animal breeding is to select individuals that have high breeding values for traits of interest as parents to produce the next generation and to do so as quickly as possible. To date, most programs rely on statistical analysis of large data bases with phenotypes on breeding populations by linear mixed model methodology to estimate breeding values on selection candidates. However, there is a long history of research on the use of genetic markers to identify quantitative trait loci and their use in marker-assisted selection but with limited implementation in practical breeding programs. The advent of high-density SNP genotyping, combined with novel statistical methods for the use of this data to estimate breeding values, has resulted in the recent extensive application of genomic or whole-genome selection in dairy cattle and research to implement genomic selection in other livestock species is underway. The high-density SNP data also provides opportunities to detect QTL and to encover the genetic architecture of quantitative traits, in terms of the distribution of the size of genetic effects that contribute to trait differences in a population. Results show that this genetic architecture differs between traits but that for most traits, over 50% of the genetic variation resides in genomic regions with small effects that are of the order of magnitude that is expected under a highly polygenic model of inheritance.