With the projected increase in the global population and food demand on a planet in climate crisis, the debate about the role of livestock continues to intensify. Animals farmed for food are both victims and contributors of climate change (CC), with animals suffering from heat stress due to CC but also being major contributors to greenhouse gas emissions (GHG). Livestock studies have demonstrated that novel breeding technologies could partly mitigate these negative effects, yet their wider implications for food security and the socio-economy are not well investigated yet. In this proof-of-concept study, we incorporate (1) the effects of heat stress on livestock’s productivity and feed efficiency and (2) the use of genomic selection as a recently established novel breeding technology, into a global macroeconomic projection model (GMPM) to evaluate their broader impacts on food security. We find that both effects can drastically alter the model projections. In particular, the results suggest that novel breeding technologies can substantially mitigate the adverse effects of CC on production, food availability, land use, and GHG emissions, whilst also causing implications for the labour market. The study demonstrates the importance of integrating advances in livestock science and technology into GMPMs for better projections that inform livestock management, policy and consumer decision-making, requiring closer collaboration between livestock scientists and systems modellers.

RESEARCH HIGHLIGHTS:Vaccination against MD can confer significant reduction in virus shedding, causing beneficial direct and flock-level protection.Selection for genetic resistance to MD based on tumour incidence or death did not confer significant downstream flock-level protection in this study.Feather viral load emerged as a promising biomarker for both individual and flock-level protection from MD.Reduction of MDV transmission is likely to be the most promising method for long-term MD control.

The host’s ability to clear parasites (resistance) and their capacity to reduce the impact of a given parasite burden (tolerance) is often described under conditions of a single pathogen challenge. We developed a mouse infection model to investigate the impact of co-infection on host resistance and tolerance to Heligmosomoides bakeri. C57BL/6 mice were infected with the helminth parasite H. bakeri and Theiler’s murine encephalomyelitis virus (TMEV), that both reside in the small intestine. Two infection protocols were used to also investigate the impact of the order of pathogen administration on host resistance and tolerance (H-V protocol: helminth first and V-H protocol: virus first). Non-infected controls, H.bakeri only and TMEV only infected animals were also included. Our data showed that co-infection in the H-V protocol, resulted in significantly improved resistance to H.bakeri, as measured by 30

Coccidiosis, a widespread disease in poultry caused by protozoan parasites of the genus Eimeria, leads to significant economic losses. The increasing resistance of Eimeria species to anti-parasitics, combined with the high cost of vaccines, underscores the need for alternative intervention strategies against coccidiosis. This article explores the relative impact of several traits on the health of the group, accounting for the population dynamics of the infection. We focus on five traits that can potentially be influenced by genetic selection, treatment, vaccination or nutrition: (1) susceptibility, (2) recoverability, (3) infectivity, (4) tolerance, and (5) compensatory growth occurring after the infection ends. We propose an epidemiological model of coccidiosis based on literature review concerning chicken coccidiosis epidemiology and parameter estimations based on published data. Using this model, we investigate the direct and indirect impacts of each individual trait on the health and productivity of the flock. This approach aims at understanding the relative role of these individual traits on population level disease resistance and economical profitability of farms undergoing coccidiosis epidemics. The results showed increasing recoverability and tolerance were particularly beneficial for the health and productivity of the flock, both through direct and indirect effects whilst reducing infectivity has the highest beneficial effect on reducing the infectious load in the environment and on flock level protection. This approach has the potential to guide disease control strategies aimed at enhancing coccidiosis management within the poultry industry.

In infected hosts, immune responses trigger a systemic energy reallocation away from energy storage and growth, to fuel a costly defense program. The exact energy costs of immune defense are however unknown in general. Life history theory predicts that such costs underpin trade-offs between host disease resistance and other fitness related traits, yet this has been seldom assessed. Here we investigate immune energy cost induced by infection, and their potential link to a trade-off between host resistance and fat storage that we previously exposed in sheep divergently selected for resistance to a pathogenic helminth. To this purpose, we developed a mathematical model of host-parasite interaction featuring individual changes in energy allocation over the course of infection. The model was fitted to data from an experimental infectious challenge in sheep from genetically resistant and susceptible lines to infer the magnitude of immune energy costs. A relatively small and transient immune energy cost in early infection best explained within-individual changes in growth, energy storage and parasite burden. Among individuals, predicted responses assuming this positive energy cost conformed to the observed trade-off between resistance and storage, whereas a cost-free scenario incorrectly predicted no trade-off. Our mechanistic model fitting to experimental data provides novel insights into the link between energy costs and reallocation due to induced resistance within-individual, and trade-offs among individuals of selected lines. These will be useful to better understand the exact role of energy allocation in the evolution of host defenses, and for predicting the emergence of trade-offs in genetic selection.

Social network analysis (SNA) provides a means of understanding animals' agonistic behaviour in a group. The aim of this study was to use SNA to characterise how individual cognitive performance affects agonistic behaviour. Using 175 pigs, we hypothesised that their choice of opponents would be affected by their ability to discriminate spatial information and to adapt their behaviour when cues were reversed. A spatial discrimination test was conducted; left and right locations were assigned as positive (food reward) and negative (fan) and each pig's learning speed was recorded. The cues were then reversed, and we tested whether pigs adjusted their behaviour. At age 14 weeks, pigs were regrouped into 14 groups, and their behaviour recorded for 5h, from which weighted and unweighted networks were constructed. Skin lesions were counted after 24h, 1 week and 2 weeks. Males delivered more aggression, and heavier pigs were involved in more aggression. Betweenness centrality (a network position linking otherwise unconnected individuals) increased with network size and decreased with body weight. Passing the reversal learning test predicted more involvement in unilateral aggression. The current study therefore shows links between cognitive performance and aggression and advances the understanding of social network analysis in the context of animal welfare.

Three-dimensional (3D) measurements extracted from beef carcass images were used to predict the weight of four saleable meat yield (SMY) traits (total SMY and the SMY of the forequarter, flank, and hindquarter) and four primal cuts (sirloin, ribeye, topside and rump). Data were collected at two UK abattoirs using time-of-flight cameras and manual bone out methods. Predictions were made for 484 carcasses, using multiple linear regression (MLR) or machine learning (ML) techniques. Model inputs included breed type, sex, and abattoir as fixed effects, and cold carcass weight, visually assessed EUROP fat and conformation classes, and 3D measurements as covariates. Machine learning techniques were only used for models including 3D measurements. The CCW and fixed effects resulted in high accuracy (SMY R 2 = 0.72-0.90, RMSE = 2.12-3.96 kg, primal R 2 = 0.56-0.67, RMSE = 0.36-0.91 kg), and including the EUROP covariates increased accuracies (SMY R 2 = 0.75-0.96, RMSE = 2.00-3.11 kg, primal R 2 = 0.58-0.79, RMSE = 0.36-0.79 kg). The 3D measurement covariates and abattoir resulted in moderate accuracy (SMY MLR R 2 = 0.39-0.58, RMSE = 3.26-10.31 kg, primal MLR R2 = 0.33-0.52, RMSE = 0.44-1.14 kg) and high accuracy when combined with CCW and all fixed effects (SMY MLR R 2 = 0.72-0.95, RMSE = 1.81-3.42 kg, primal MLR R 2 = 0.52-0.74, RMSE = 0.40-0.81 kg). The best ML models resulted in similar accuracies to the MLR models. Models including 3D measurements produced similar accuracies to models built using conventional data recorded at the abattoir, indicting the potential for automated prediction.

Genetic variation in host resistance to individual parasites is well documented in cattle; however, the influence of coinfection on these genetic responses to selection remains poorly characterized. In particular, it is unclear how concurrent exposure to multiple parasite species alters phenotypic expression, heritability estimates, or genetic correlations between resistance traits. To address these gaps, we evaluated the impact of coinfection on the genetic architecture of parasite resistance in yearling Nellore calves naturally challenged with ectoparasites (ticks) and endoparasites (gastrointestinal nematodes and Eimeria spp.). Using longitudinal parasite count data, we estimated genetic parameters and examined how coinfection modifies both individual parasite resistance and the genetic correlations among traits. Our results confirmed that coinfection is a common phenomenon (almost ¾ of samples contained multiple parasites) and that resistance to individual parasites is a heritable trait. Furthermore, coinfection with Eimeria spp. reduced the phenotypic resistance to nematodes, and vice versa. We observed diverse genetic associations for resistance to different parasites, including positive, negative, and nonsignificant correlations. Notably, coinfection had no significant effect on genetic resistance to individual parasites, nor did it alter genetic variances or associations between resistance to different parasites. While coinfection may influence the outcomes of nongenetic parasite control programs, its impact on genetic control strategies appears minimal. In other words, genetic resistance of Nellore cattle to three key parasite species appears to be robust and unaffected by the presence of coinfection.

Background: The social interactions of farm animals affect their performance, health and welfare. This proof-of-concept study addresses, for the first time, the hypothesis that applying social network analysis (SNA) on AI-automated monitoring data could potentially facilitate the analysis of social structures of farm animals. Methods: Data were collected using automated recording systems that captured 2D-camera images and videos of pigs in six pens (16-19 animals each) on a PIC breeding company farm (USA). The system provided real-time data, including ear-tag readings, elapsed time, posture (standing, lying, sitting), and XY coordinates of the shoulder and rump for each pig. Weighted SNA was performed, based on the proximity of "standing" animals, for two 3-day period-the early (first month after mixing) and the later period (60 days post-mixing). Results: Group-level degree, betweenness, and closeness centralization showed a significant increase from the early-growing period to the later one (p < 0.02), highlighting the pigs' social dynamics over time. Individual SNA traits were stable over these periods, except for the closeness centrality and clustering coefficient, which significantly increased (p < 0.00001). Conclusions: This study demonstrates that combining AI-assisted monitoring technologies with SNA offers a novel approach that can help farmers and breeders in optimizing on-farm management, breeding and welfare practices.

Social interactions of farm animals affect their performance, health and welfare. The recent advances in AI-automated monitoring technologies offer digital phenotypes, at low-cost, that record the animals in real-time. This proof of concept study addresses, for the first time, the hypothesis that applying social network analysis (SNA) on automated data could potentially facilitate the analysis of social structures of farm animals. Data was collected using automated recording systems that captured 2D camera images and videos of pigs in six pens (16 to 19 animals each) on a PIC breeding company farm (USA). The system provided real-time data, including ear-tag readings, elapsed time, posture (standing, lying, sitting), and XY coordinates of the shoulder and rump for each pig. Weighted SNA was performed, based on the proximity of standing animals, for two 3-day periods, the early growing period (first month after mixing) and the later period (60 days post-mixing). Group level degree, betweenness, and closeness centralization showed a significant increase from the early growing period to the later one (p<0.02), highlighting the pigs social dynamics over time. Largest clique size remained unchanged (p=0.28), but the number of maximal cliques significantly decreased from the early to late growing period (p=0.007). Individual SNA traits were stable over these periods, except for closeness centrality and clustering coefficient which significantly increased (p<0.00001). This study demonstrates that combining AI-assisted monitoring technologies with SNA offers an efficient, real-time approach to gain novel insights into animal social interactions. This approach can optimize on-farm management or breeding practices, leading to improved animal performance, health, and welfare. ### Competing Interest Statement The authors have declared no competing interest.

Atlantic salmon, Salmo salar, have traditionally been reared in net-pens in freshwater (FW) lochs up to smoltification, with subsequent transfer to saltwater (SW) cages for grow-out. Recently, interest in recirculating aquaculture systems (RAS) has grown due to environmental and husbandry benefits. To investigate the impact of RAS on their production cycle, we conducted an experiment under commercial conditions, raising a group of salmon in either a FW-RAS or -loch system. The study evaluated the effects of FW-rearing on SW performance by investigating phenotypic performance, genetic architecture, and genotype-environment interactions (GxE), which describe how the effects of different genotypes on traits change with environmental variation, potentially impacting performance across systems. We co-reared salmon for approximately nine-months before splitting them: half remained in FW-RAS and half transferred to FW-loch, where they were separated for about eight weeks. Both groups were then transferred to a SW cage-site. We sampled fish at the end of FW-rearing as smolts and three-months post-SW transfer as post-smolts, taking fin clips for genotyping. Results indicate that RAS-reared smolts were smaller in FW but demonstrated enhanced growth and lower trait variance post-transfer. Sexually dimorphic growth was observed in the loch population. Heritability of morphological traits increased post-SW transfer in the loch population but decreased in RAS. GxE for SW morphological traits were minimal, though significant genotype re-ranking was observed for SW growth. Genetic correlations between FW and SW morphological traits were high, except for whole-body weight in the loch population. These findings indicate that RAS-origin post-smolts, despite smaller FW size, showed faster growth and reduced phenotypic variance in SW compared to loch-origin fish. Differences in heritability estimates and genotype re-ranking for SW growth suggest that breeding programs may need to refine selection strategies for varied rearing environments.

With the projected increase in the global population and food demand on a planet in climate crisis, the debate about the role of livestock continues to intensify. Animals farmed for food are both a victim and a contributor of climate change (CC), with animals suffering from heat stress due to CC but also a major contributor to greenhouse gas emissions (GHG). Livestock studies have demonstrated that novel breeding technologies could partly mitigate these negative effects, yet their wider implications on food security and the socio-economy are not well investigated yet. In this proof-of-concept study we incorporate, 1) the effects of heat stress on livestock’s productivity and feed efficiency, and 2) the use of genomic selection as a recently established novel breeding technology, into a global macroeconomic projection model (GMPM) to evaluate their broader impacts on food security. We find that both effects can drastically alter the model projections. In particular, the results suggest that novel breeding technologies can substantially mitigate adverse effects of CC on production, food availability, land use, and GHG, whilst also causing implications for the labour market. The study demonstrates the importance of integrating advances in livestock science and technology into GMPMs for better projections that inform livestock management, policy and consumer decision-making, requiring closer collaboration between livestock scientists and systems modellers.

Negative social behaviors represent a welfare and economic problem for farm animals worldwide. The time-consuming nature of observing behavior on a large scale means that social problems are not visible until their negative physical effects are advanced. However, identifying the key initiators or propagators of fighting and/or biting may not be possible by observing injury alone. Therefore, in this study, we investigated the possibility of constructing and analyzing social networks from AI-assisted automated monitoring data in pigs and the feasibility of using spatial proximity association as an indicator of harmful social interactions. Data were collected using automated recording systems that captured 2D camera images and videos of 6 pens of pigs (16-19 per pen) on a PIC breeding farm (USA). The system records continuous video footage with the associated real-time ear-tag ID, elapsed time, posture (standing, lying, sitting) and XY coordinates of the shoulder and rump for each pig. The validation of automated identity, posture and location records show 97-100% agreement with human observations (Agha et al., 2024). Pig movements were recorded from 10:00 to 19:00h for 6 days; 3 days immediately after regrouping and 3 days, 60 days after regrouping. Proximity data was used to create weighted social networks. Group level metrics (degree, betweenness, and closeness centralization) significantly increased from the early to late growing periods (p< 0.02), highlighting that inequality in proximity between pigs increased over time. Largest clique size remained unchanged (p=0.28), but the number of maximal cliques (fully connected subgroups) significantly decreased from the early to late growing period (p=0.007). Individual SNA traits were mostly stable over these periods. Measuring the behavior of prominent individuals during the time they are in proximity will allow targeting of management interventions to improve welfare outcomes. We tested an initial dataset as proof of concept that proximity signatures can be used to identify aggression. Video footage of two pens of 19 pigs were observed and 37 mutual fighting bouts were identified. Proximity matrices were calculated for each pen using shoulder XY location, for the duration of the bout, lasting 2-42 s (median 5.0s). 81% of fighting dyads were identified as in proximity (< 0.5m) compared with 7% (468/6290) of non-fighting dyads, χ2=7.0, p=0.008. Of the dyads in proximity, fighting dyads were in proximity for over twice as much of the interval (median: Q1-Q3); (92: 31.4-96.1%) as non-fighting dyads (44.3: 16.8- 86.1%), p< 0.001 Refinements to improve sensitivity and specificity to diagnose and characterize aggressive behavior from larger samples of proximity data are ongoing. This study demonstrates that integrating SNA with automated data reveals novel insights into pigs’ social interactions and identifies a signature of aggressive encounters using proximity. That could offer promising applications in breeding and management of farmed animals.

Despite extensive use of vaccination, porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2) continues to evolve, likely driven by escape from natural or vaccine-derived immunity. However, direct evidence of vaccine-induced evolutionary pressure remains limited. Here, we tracked the evolution of PRRSV-2 sublineage 1A strain IA/2014 (variant 1A-unclassified) genome from infection chains of sequentially infected pigs under different immune conditions. Weaned pigs were divided into three groups: a non-immunized control group and two groups vaccinated with different modified live virus (MLV) vaccines, namely Prevacent® PRRS MLV (variant 1D.2) and Ingelvac PRRS® MLV (variant 5A.1). Sixty-four days post-vaccination, the pigs were challenged with IA/2014 PRRSV-2. Virus infection chains (which used serum from pigs in batch n to infect batch n + 1) were maintained across six sequential batches of roughly seven pigs each, allowing for virus evolution to occur across the ~ 84 days of the infection chain. A total of 110 serum samples were successfully sequenced. Vaccinated groups exhibited over twice the genetic divergence from the original challenge virus (0.3%-0.4% mean nucleotide distance) compared to non-immunized group (0.15%). Variability was concentrated in ORF1a and ORF1b. Deep sequencing revealed more rapid shifts of viral quasispecies composition in vaccinated pigs, and more homogeneous viral populations over batches compared to non-immunized pigs. Selection pressure analyses indicated strong purifying selection in one vaccinated group, though without clear signals at known antigenic sites in all treatment groups. However, vaccinated pigs had significantly higher cycle threshold values (P<.001), indicating lower viral loads and suggesting potential fitness limitations for highly diverged viruses in immunized pigs. These findings demonstrate that MLV vaccination can exert substantial evolutionary pressure on PRRSV-2, driving genetic diversification and highlighting the need for continuous PRRS monitoring and adaptive control strategies.

Despite extensive use of vaccination, porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2) continues to evolve, likely driven by escape from natural or vaccine-derived immunity. However, direct evidence of vaccine-induced evolutionary pressure remains limited. Here, we tracked the evolution of PRRSV-2 sub-lineage 1A strain IA/2014 (variant 1A-unclassified) genome from infection chains of sequentially infected pigs under different immune conditions. Weaned pigs were divided into three groups: a non-immunized control group and two groups vaccinated with different modified live vaccines (MLVs), namely Prevacent® PRRS MLV (variant 1D.2) and Ingelvac PRRS® MLV (variant 5A.1). Sixty-four days post-vaccination, the pigs were challenged with IA/2014 PRRSV-2. Virus infection chains (which used serum from pigs in batch n to infect batch n + 1) were maintained across six sequential batches of roughly seven pigs each, allowing for virus evolution to occur across the ~ 84 days of the infection chain. A total of 110 serum samples were successfully sequenced. Vaccinated groups exhibited over twice the genetic divergence from the original challenge virus (0.3–0.4% mean nucleotide distance) compared to non-immunized group (0.15%). Variability was concentrated in ORF1a and ORF1b. Deep sequencing revealed more rapid shifts of viral quasispecies composition in vaccinated pigs, and more homogeneous viral populations over batches compared to non-immunized pigs. Selection pressure analyses indicated strong purifying selection in one vaccinated group, though without clear signals at known antigenic sites in all treatment groups. However, vaccinated pigs had significantly higher Ct values (p<.001), indicating lower viral loads and suggesting potential fitness limitations for highly diverged viruses in immunized pigs. These findings demonstrate that MLV vaccination can exert substantial evolutionary pressure on PRRSV-2, driving genetic diversification and highlighting the need for continuous PRRS monitoring and adaptive control strategies.

This study investigated the genetics of bovine tuberculosis (bTB) infectivity in Holstein-Friesian dairy cows using British national data. The analyses included cows with recorded sires from herds affected by bTB outbreaks between 2000 and 2022. Animals were considered bTB positive if they reacted positively to the skin test, had positive postmortem findings, or both. We introduced the "index case approach," based on the assumption that once the initial positively tested animals (index cases) are detected in a herd, subsequent infections (secondary cases) in the early stages of the breakdown are likely to be attributed to these animals. Genetic analysis of the number of secondary cases (NrSC) associated with a given index case was used to establish evidence of genetic variability in bTB infectivity of cattle, and derive EBV for infectivity for the sires of the index cases. Data were analyzed by employing Markov chain Monte Carlo techniques to fit generalized linear mixed models with either Poisson, zero-inflated Poisson (ZIP), hurdle Poisson, or geometric distributions. All 4 models demonstrated presence of genetic variance in cattle infectivity, with the strongest evidence provided by the ZIP and hurdle Poisson models. The hurdle Poisson model offered the most accurate and least biased predictions. Sire infectivity EBV from the Poisson, ZIP, and geometric models showed strong concordance, with pairwise correlations of 0.90 or higher. In contrast, correlations between EBV from the hurdle Poisson model and the other models ranged from 0.36 to 0.39. The association of the sire infectivity EBV with the average observed NrSC per sire and the proportion of infectious index case daughters per sire was generally moderate with correlations between 44% and 47% and 65% to 69%, respectively. Agreement among models for identifying the genetically most infectious sires was also reasonable, with 151 out of 285 sires appearing in the top 10% across models, and 122 (42.8%) also aligning with the top 10% based on observed average NrSC. Results provide novel evidence for exploitable genetic variance in bTB infectivity allowing the derivation of meaningful EBV. Based on the estimated posterior mean genetic variances obtained, reduction in infectivity by 1 genetic SD would result in a 32% to 44% decrease in the expected NrSC per index case. Further research is warranted to refine the phenotypic definition of infectivity and assess correlation with other dairy traits.

BACKGROUND:Genetic selection of individuals that are less susceptible to infection, less infectious once infected, and recover faster, offers an effective and long-lasting solution to reduce the incidence and impact of infectious diseases in farmed animals. However, computational methods for simultaneously estimating genetic parameters for host susceptibility, infectivity and recoverability from real-word data have been lacking. Our previously developed methodology and software tool SIRE 1.0 (Susceptibility, Infectivity and Recoverability Estimator) allows estimation of host genetic effects of a single nucleotide polymorphism (SNP), or other fixed effects (e.g. breed, vaccination status), for these three host traits using individual disease data typically available from field studies and challenge experiments. SIRE 1.0, however, lacks the capability to estimate genetic parameters for these traits in the likely case of underlying polygenic control. RESULTS:This paper introduces novel Bayesian methodology and a new software tool SIRE 2.0 for estimating polygenic contributions (i.e. variance components and additive genetic effects) for host susceptibility, infectivity and recoverability from temporal epidemic data, assuming that pedigree or genomic relationships are known. Analytical expressions for prediction accuracies (PAs) for these traits are derived for simplified scenarios, revealing their dependence on genetic and phenotypic variances, and the distribution of related individuals within and between contact groups. PAs for infectivity are found to be critically dependent on the size of contact groups. Validation of the methodology with data from simulated epidemics demonstrates good agreement between numerically generated PAs and analytical predictions. Genetic correlations between infectivity and other traits substantially increase trait PAs. Incomplete data (e.g. time censored or infrequent sampling) generally yield only small reductions in PAs, except for when infection times are completely unknown, which results in a substantial reduction. CONCLUSIONS:The method presented can estimate genetic parameters for host susceptibility, infectivity and recoverability from individual disease records. The freely available SIRE 2.0 software provides a valuable extension to SIRE 1.0 for estimating host polygenic effects underlying infectious disease transmission. This tool will open up new possibilities for analysis and quantification of genetic determinates of disease dynamics.

The interest in recirculating aquaculture systems (RAS) is growing due to their benefits such as increased productivity, better control over animal care, reduced environmental effects, and less water consumption. However, in some regions of the world, traditional aquaculture methods remain prevalent, and selective breeding has often been designed for performance within these systems. Therefore, it is important to evaluate how current fish populations fare in RAS to guide future breeding choices. In a commercial setting, we explore the genetic structure of growth characteristics, measure genotype-environment interactions (GxE) in salmon smolts, and examine genetic markers related to growth in freshwater lochs and RAS. Young salmon were raised together until they reached the parr stage, after which they were divided equally between freshwater net-pens and RAS. After an 8-week period, we sampled fish from each environment and genotyped them. Our findings revealed that fish reared in RAS were generally smaller in weight and length but exhibited a higher condition factor and uniformity. We found a notably smaller component of unexplained variance in the RAS, leading to higher heritability estimates. We observed a low GxE effect for length and condition factor, but significant re-ranking for whole-body weight, as well as noticeable differences in trait associations across environments. Specifically, a segment of chromosome 22 was found to be linked with the condition factor in the RAS population only. Results suggests that if the use of RAS continues to expand, the efficiency of existing commercial populations may not reach its full potential unless breeding programs specific to RAS are implemented.