The predicted benefits of genomic selection on pig breeding objectives.

The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.

Economic values for production, fertility and mastitis traits for temperate dairy cattle breeds in tropical Sri Lanka.

Economic values for annual milk yield (MY, kg), annual fat yield (FY, kg), annual protein yield (PY, kg), age at first calving (AFC, days), number of services per conception (NSC), calving interval (CI, days) and mastitis episodes (MS) were derived for temperate dairy cattle breeds in tropical Sri Lanka using a bio-economic model. Economic values were calculated on a per cow per year basis. Derived economic values in rupees (LKR) for MY, FY and PY were 107, -162 and -15, while for AFC, NSC, CI and MS, economic values were -59, -270, -84 and -8,303. Economic values for FY and PY further decreased with higher feed prices, and a less negative economic value for FY was obtained with increased price for fat. Negative economic values for FY and PY show that genetic improvement for these traits is not economical due to the high feed costs and/or the insufficient payment for fat and protein. Therefore, revision of milk fat and protein payments is recommended. Furthermore, the breeding objective developed in this study was dominated by milk production and fertility traits. Adaptability and functional traits that are important in a temperate dairy cattle breeding programme in tropical Sri Lanka, such as longevity, feed efficiency, disease resistance and heat tolerance should be recorded to incorporate them in the breeding objective. Continued trait recording of all traits is recommended to ensure dairy cows can be selected more effectively in a tropical environment based on a breeding objective that also includes adaptability and functional traits.

Prospects for sustainability of pig production in relation to climate change and novel feed resources.

Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems. The first scenario is a high input-high output system based on sustainable intensification, maximizing animal protein production efficiency on a limited land surface at the same time as minimizing environmental impacts. The second scenario is a reduced input-reduced output system based on selecting animals that are more robust to climate change and are better adapted to transform low quality feed (local feeds, feedstuff co-products, food waste) into meat. However, in contrast to the first scenario, the latter scenario results in reduced predicted yields, reduced production efficiency and possibly increased costs to the consumer. National evaluation of the availability of local feed and feedstuff co-product alternatives, determination of limits to feed sourced from international markets, available land for crop and livestock production, desired production levels, and a willingness to politically enforce policies through subsidies and/or penalties are some of the considerations to combine these two scenarios. Given future novel sustainable alternatives to livestock animal protein, it may become reasonable to move towards an added general premium price on 'protein from livestock animals' to the benefit of promoting higher incomes to farmers at the same time as covering the extra costs of, politically enforced, welfare of livestock animals in sustainable production systems. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka.

OBJECTIVE: This study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka. METHODS: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model. RESULTS: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and -0.35 in Jersey-Friesian cows. CONCLUSION: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

Reaction norm analysis of pig growth using environmental descriptors based on alternative traits.

Contemporary group (CG) estimates of different phenotypes have not been widely explored for pigs. The objective of this study was to extend the traits used to derive environmental descriptors of the growing pig, to include CG estimates of early growth between birth and start of feed intake test (EADG), growth during feed intake test (TADG), lifetime growth (ADG), daily feed intake (DFI), backfat (BF) and muscle depth (MD). Pedigree and performance records (n = 7,746) from a commercial Australian piggery were used to derive environmental descriptors based on CG estimates of these six traits. The CG estimates of growth traits described different aspects of the environment from the CG estimates of carcass traits (r < 0.10). These definitions of the environment then were used in reaction norm analysis of growth, to evaluate sire-by-environment interaction (Sire × E) for growth. The most appropriate reaction norm model to evaluate Sire × E for growth was dependent on the environmental descriptor used. If the trait used to derive an environmental descriptor was distinctly different from growth (e.g., BF and MD), CG as an additional random effect was required in the model. If not included, inflated common litter effect and sire intercept variance suggest there was unaccounted environmental variability. There was no significant Sire × E using any of the definitions of the environment, with estimated variance in sire slopes largest when environments were defined by BF ( σ ^ bi 2  = 97 ± 83 (g/day)2 ), followed by environments defined by DFI ( σ ^ bi 2  = 39 ± 101 (g/day)2 ). While there appears to be differences in ability to detect Sire × E, improved data structure is required to better assess these environmental descriptors based on alternative traits. The ideal trait, or combination of traits, used to derive environmental descriptors may be unique for individual herds. Therefore, multiple phenotypes should be further explored for the evaluation of Sire × E for growth in the pig.

Genetic parameters and inbreeding effects for production traits of Thai native chickens.

OBJECTIVE: Estimate genetic parameters, the rate of inbreeding, and the effect of inbreeding on growth and egg production traits of a Thai native chicken breed Lueng Hang Kao Kabinburi housed under intensive management under a tropical climate. METHODS: Genetic parameters were estimated for weight measured at four weekly intervals from body weight at day 1 (BW1D) to body weight at 24 weeks (BW24) of age, as well as weight at first egg, age at first egg (AFE), egg weight at first egg, and total number of eggs (EN) produced during the first 17 weeks of lay using restricted maximum likelihood. Inbreeding depression was estimated using a linear regression of individual phenotype on inbreeding coefficient. RESULTS: Direct additive genetic effect was significant for all traits. Maternal genetic effect and permanent environmental hen effects were significant for all early growth traits, expect for BW24. For BW24, maternal genetic effect was also significant. Permanent environmental hen effect was significant for AFE. Direct heritabilities ranged from 0.10 to 0.47 for growth traits and ranged from 0.15 to 0.16 for egg production traits. Early growth traits had high genetic correlations between them. The EN was lowly negatively correlated with other traits. The average rate of inbreeding for the population was 0.09% per year. Overall, the inbreeding had no effect on body weight traits, except for BW1D. An increase in inbreeding coefficient by 1% reduced BWID by 0.09 g (0.29% of the mean). CONCLUSION: Improvement in body weight gain can be achieved by selecting for early growth traits. Selection for higher body weight traits is expected to increase the weight of first egg. Due to low but unfavorable correlations with body weight traits, selection on EN needs to be combined with other traits via multi-trait index selection to improve body weight and EN simultaneously.

Improving sow welfare and outcomes in the farrowing house by identifying early indicators from pre-farrowing assessment.

Poor outcomes reflect low performance during the farrowing and lactation periods and unanticipated sow removals. Since the period around farrowing has the highest risk for sow health issues, monitoring of sows in that time-period will improve both welfare and productivity. The aim of this study was to identify the most relevant risk factors for predicting poor outcomes and the implication for sow welfare. Identifying these factors could potentially enable management interventions to decrease incidences of compromised welfare or poor performance. Data from 1,103 sows sourced from two nucleus herds were recorded for a range of variables investigated as potential predictors of poor outcomes in the farrowing house. Poor outcomes (scored as binary traits) reflected three categories in a sow's lifecycle: farrowing, lactation, and removals. Univariate logistic regression was used to identify predictors in the first instance. Predictors from univariate analyses were subsequently considered together in multi-variate models. The least square means representing predicted probabilities of poor outcomes were then reported on the observed scale. Several predictors were significant across two different environments (farms) and for all three categories. These predictors included feed refusal (lack of appetite), crate fit, locomotion score, and respiration rate. Normal appetite compared to feed refusals reduced the risk of farrowing failure (13.5 vs. 22.2%, P = 0.025) and removals (10.4 vs. 20.4%, P < 0.001). Fit in the crate was significant (P < 0.001) for farrowing and lactation outcomes, and was more informative than parity. Sows with sufficient space had two to three times reduced risk of poor outcomes compared to restrictive crates relative to sow dimensions. Sows with good locomotion score pre-farrowing had two to three times less risk of farrowing failure (P = 0.025) and reduced piglet mortality (P < 0.001), weaned two piglets more relative to affected sows (P < 0.001), and were less likely to be removed before weaning (3.24 vs. 12.3%, P = 0.014). Sows with higher respiration rates had a significantly (P < 0.001) reduced risk of poor farrowing outcomes. This study demonstrated it is possible to predict poor outcomes for sows prior to farrowing, suggesting there are opportunities to decrease the risk of poor outcomes and increase overall sow welfare.

Farrowing and lactation are the most vulnerable events in sows' lifecycle with high risk of compromised health. For at-risk sows, poor health could result in an increased level of stillborn piglets, a decreased number of weaned piglets, or premature removals of sows from the herd. This can potentially be avoided by identifying at-risk sows prior to farrowing, to enable effective management interventions, thereby improving sow welfare. Several risk factors are shown to be consistent in identifying sows with compromised health or welfare across two different management systems. These risk factors are low appetite before farrowing, low respiration rate, leg problems, and the sows' fit within the farrowing crate relative to their dimensions. This study suggested that at-risk sows may be identified; altering their subsequent management and treatment could result in higher performance and reduced risk of premature removals.

Economic and environmental assessments of combined genetics and nutrition optimization strategies to improve the efficiency of sustainable pork production.

We evaluated the economic and environmental impacts of strategies that incorporated selection for pig feed efficiency and dietary optimization based on a single or multiple objectives tailored to meet the population nutritional requirements, with the goal to optimize sustainable farm feed efficiency. The economic and environmental features of the strategy were evaluated using life cycle assessment (LCA) and bio-economic models. An individual trait-based LCA model was applied to evaluate global warming potential, terrestrial acidification potential, freshwater eutrophication potential (EP), and land occupation of the combined genetics and nutrition optimization to produce 1 kg of live pig weighing 120 kg at the farm gate. A parametric individual trait-based bio-economic model was developed and applied to determine the cost breakdown, revenue, and profit to be gained from a 120-kg live pig at the farm gate. Data from two genetic lines with contrasted levels of feed efficiency were used to apply the combined genetics and nutrition optimization: accounting for the average nutritional requirements for each line, the individual pig responses to diets formulated for least cost, least environmental impacts, or minimum combination of costs and environmental impacts objectives were predicted with INRAPorc. Significant differences in the environmental impacts (P < 0.0001) and profit (P < 0.05) between lines predicted with the same reference diet showed that selection for feed efficiency (residual feed intake) in pigs improves pig production sustainability. When pig responses were simulated with their line-optimized diets, except for EP, all the line environmental impacts were lower (P < 0.05) than with the reference diet. The high correlations of feed conversion ratio with the environmental impacts (> 0.82) and the profit (< -0.88) in both lines underlined the importance of feed efficiency as a lever for the sustainability of pig production systems. Implementing combined genetics and nutrition optimization, the inherent profit and environmental differences between the genetic lines were predicted to be reduced from 23.4% with the reference diet to 7.6% with the diet optimized jointly for economic and environmental objectives (joint diet). Consequently, for increased pig sustainability, diet optimization for sustainability objectives should be applied to cover the specific nutritional requirements arising in the herd from the pigs genetic level.

Feed intake and feeding behavior traits for gestating sows recorded using electronic sow feeders.

Electronic sow feeding (ESF) systems are used to control feed delivery to individual sows that are group-housed. Feeding levels for gestating sows are typically restricted to prevent excessive body weight gain. Any alteration of intake from the allocated feeding curve or unusual feeding behavior could indicate potential health issues. The objective of this study was to use data recorded by ESF to establish and characterize novel feed intake and feeding behavior traits and to estimate their heritabilities. Raw data were available from two farms with in-house manufactured (Farm A) or commercial (Farm B) ESF. The traits derived included feed intake, time spent eating, and rate of feed consumption, averaged across or within specific time periods of gestation. Additional phenotypes included average daily number of feeding events (AFE), along with the cumulative numbers of days where sows spent longer than 30 min in the ESF (ABOVE30), missed their daily intake (MISSF), or consumed below 1 kg of feed (BELOW1). The appetite of sows was represented by averages of score (APPETITE), a binary value for allocation eaten or not (DA_bin), or the standard deviation of the difference between feed intake and allocation (SDA-I). Gilts took longer to eat than sows (15.5 ± 0.13 vs. 14.1 ± 0.11 min/d) despite a lower feed allocation (2.13 ± 0.00 vs. 2.36 ± 0.01 kg/d). The lowest heritability estimates (below 0.10) occurred for feed intake traits, due to the restriction in feed allocation, although heritabilities were slightly higher for Farm B, with restriction in the eating time. The low heritability for AFE (0.05 ± 0.02) may have reflected the lack of recording of nonfeeding visits, but repeatability was moderate (0.26 ± 0.03, Farm A). Time-related traits were moderately to highly heritable and repeatable, demonstrating genetic variation between individuals in their feeding behaviors. Heritabilities for BELOW1 (Farm A: 0.16 ± 0.04 and Farm B: 0.15 ± 0.09) and SDA-I (Farm A: 0.17 ± 0.04 and Farm B: 0.10 ± 0.08) were similar across farms. In contrast, MISSF was moderately heritable in Farm A (0.19 ± 0.04) but lowly heritable in Farm B (0.05 ± 0.07). Heritabilities for DA_bin were dissimilar between farms (Farm A: 0.02 ± 0.02 and Farm B: 0.23 ± 0.10) despite similar incidence. Individual phenotypes constructed from ESF data could be useful for genetic evaluation purposes, but equivalent capabilities to generate phenotypes were not available for both ESF systems.

Quantifying the health challenges in an Australian piggery using medication records for the definition of disease resilience1.

Disease resilience is the ability to maintain performance and health, despite infection challenges in the environment. The evaluation of disease resilience requires measures of environment infection challenges, along with other environmental challenges. The overall objective of this study was to define disease resilience using pedigree, production, and medication records from an Australian herd of Large White pigs. The extent to which the infection challenges were captured by environmental descriptors based on contemporary group (CG) estimates of growth was assessed (n = 8,835). There were moderately negative linear relationships (r = -0.29, p = 0.08) between CG estimates (39 CGs) of growth and the frequency of medicated pigs (n = 812 medicated pigs). This suggests that CG estimates of growth partly capture health challenges. However, because the health challenges were not of the pathogenic nature for this herd, these environmental descriptors may not be appropriate for the evaluation of disease resilience. Subsequently, an alternative approach to select for health was provided, where health was defined as a binary outcome of medication status, fitted in a generalized linear mixed sire model. Two health-trait definitions were explored, which differed in the number of control (nonmedicated) pigs per litter. The 'reduced-control' health trait had a representative sample of littermates with available performance records, and the 'full-control' health trait included all piglets weaned per litter (i.e., performance-tested and non-performance-tested pigs). All 812 medicated pigs had performance records available. The remaining 8,023 pigs in the reduced-control and 21,352 pigs in the full-control health traits were assumed to have not been medicated (controls). Male pigs from litters with a higher number of postweaning deaths were more likely to be medicated for both health traits. Heritability was consistent for both trait definitions, at 0.06 ± 0.04 (± SE) (reduced-control) and 0.04 ± 0.03 (full-control). While results may be specific for individual herds depending on health status, these estimates align with those presented in literature for other health traits. Together, these results demonstrate that routinely collected medication records may be useful for pig breeding programs and their economic importance and genetic background should be explored further.

Selection of pigs for improved coping with health and environmental challenges: breeding for resistance or tolerance?

The benefits of improved health and welfare in pigs have driven refinements in management and selection practices, one of which is the production of pig phenotypes that can maintain health and productivity by improving response against pathogens. Selection has traditionally been made for host resistance; but the alternative host defence mechanism-host tolerance-is now being considered, as breeding for disease tolerance allows maintenance of high performance across environments of increasing pathogenic load. A distinction must be made between these two mechanisms as they vary in their influence on host-pathogen interactions and pathogen evolution, and consequently on the results of breeding programs. Many pig production studies have failed to distinguish between resistance and tolerance; although a distinction may not always be possible. This article reviews current perspectives in selective breeding for disease resistance and tolerance in growing pigs, and the attendant industry implications. To assess the viability of breeding for resistance and/or tolerance for improved response to disease and other environmental challenges, we propose the use of routine farm records, instead of data measurements taken from laboratory experiments. Consequently, a number of factors need to be taken into account simultaneously for a multidimensional modeling approach. This includes not only genotype and disease variables, but also descriptors of the environment, as well as any possible interactions. It may not be feasible to record individual pathogen loads, and therefore true tolerance, on farm using routinely collected data. However, it may be estimated with group (farm) means, or other proxy measures. Although this results in a bias, this may still be useful for modeling and quantifying resistance and tolerance. We can then quantify success of selection, and this may enable us to decide whether to select for disease resistance versus disease tolerance.