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【Objective】Four genetic parameters including days to 100 kg (DAYS_100), average daily gain (ADG_100), loin muscle area (LMA_100) and average back fat thickness (BFT_100) at 100 kg body weight in a Landrace pig population were estimated, and the correlations between traits as well as genetic and phenotypic progress of the four traits were analyzed, which could provide a basis for the genetic improvement of the target population.【Method】Records of growth traits of Landrace pigs were collected in a core breeding pig farm in Guangxi from 2002 to 2020. A fixed effect analysis on the factors affecting the growth traits of Landrace pigs was conducted by R software. In addition, the genetic parameters of the four traits were estimated with DMU software and a multi-trait animal model. Furthermore, the genetic correlations and phenotypic correlations between these traits, genetic progress and phenotypic progress were evaluated.【Result】The estimated heritability for the four growth traits of Landrace pigs, including DAYS_100, ADG_100, LMA_100 and BFT_100 were 0.399, 0.391, 0.433 and 0.421, respectively, and all of them had medium to high heritability. Both genetic correlation and phenotypic correlation between DAYS_100 and ADG_100 were significantly negative, with correlation coefficient -0.997 and -0.992, respectively. In general, the phenotypic trend of DAYS_100 was rising while the phenotypic trends of ADG_100, LMA_100 and BFT_100 were declining; the genetic trends of ADG_100 and BFT_100 showed an overall upward trend while the trends of DAYS_100 and LMA_100 were generally downward.【Conclusion】The four growth traits of Landrace pigs are medium-high heritability traits, therefore, their genetic progress can be accelerated through direct selection. There is a strong correlation between DAYS_100 and ADG_100. The management of phenotypic measurement of pig farms and the selection of target traits for pig population breeding have an important impact on the performance of growth traits. In addition, the improvements in farm production management and changes in breed structure may influence genetic progress.

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The covariance structure was estimated by REML for a joint genetic evaluation of production and reproduction traits for Czech Landrace (58 258 records) and Czech Large White (167 161 records) pigs using four-trait animal models. The following traits were analysed: lean mean percentage at the end of the performance test in the field, estimated from ultrasonic measurements unadjusted for live weight (LM), average daily gain in field test (in g/day) calculated as weight at the end of the test divided by age at the end of the test (ADG), number of piglets born alive in parity 1 (NBA1) and number of piglets born alive in parity 2 and subsequent parities (NBA2+). The heritabilities were in the range from 0.30 to 0.37 for LM, from 0.13 to 0.18 for ADG, from 0.09 to 0.13 for NBA1 and from 0.10 to 0.14 for NBA2+, depending on the breed and on the model (herd-year-season random or fixed). Genetic correlations between production and reproduction traits were estimated to be non-zero. Correlations between traits caused by the herd-year-season effect were mostly positive. As a general conclusion, the joint genetic evaluation of production and reproduction traits is recommended. The herd-year-season effect should be preferably considered as random.

Five alternative models were applied for analysis of dystocia and stillbirth in first and second parities. Models 1 and 2 were included only to estimate the parameters required for model 4, and models 3 and 5 are included only as comparisons to the model 4 estimates. Variance components were estimated by multi-trait REML, including cows with valid calving records for both parities. For the effects of sire of calf on first and second parities, variance components were estimated including only calvings with the same sire of calf for both parities. All heritabilities for the cow effect were quite low, but higher for dystocia than for stillbirth and higher in first parity. The sire-of-calf heritabilities were higher than the cow effect heritabilities, except for stillbirth in parity 2. Unlike the effect of cow correlations, all sire of calf correlations were >0.6, and the correlations for the same trait in parities 1 and 2 were >0.9. Thus, a multi-trait analysis should yield a significant gain in accuracy with respect to the sire of calf effects for bulls not mated to virgin heifers. A multi-trait individual animal model algorithm was developed for joint analysis of dystocia and stillbirth in first and second parities. Relationships matrices were included both for the effects of cow and sire of calf. In addition, random herd-year-season and fixed sex of calf effects were included in the model. Records were preadjusted for calving month and age. A total of 899,223 Israeli Holstein cows with first calvings since 1985 were included in the complete analysis. Approximate reliabilities were computed for both sire of cow and sire of calf effects. Correlations between these reliabilities and reliabilities obtained by direct inversion of the coefficient matrix for a sire of cow-sire of calf model were all close to 0.99. Phenotypic trends for cows born from 1983 through 2007 were economically unfavorable for dystocia and favorable for stillbirth in both parities. Genetic trends were economically unfavorable for both dystocia and stillbirth in first parity. First-parity sire of calf trends were unfavorable for dystocia, but favorable for stillbirth. All environmental trends were nearly zero. Regressions of evaluations of the complete analysis on a model including only calvings before 2011 were all >0.8. All evaluations met the Interbull Method 3 criterion for unbiasedness. Model 4, which computed genetic evaluations for all animals for all 4 traits accounting for all known relationships and correlations among the traits, is recommended for routine genetic evaluation of calving traits. [ABSTRACT FROM AUTHOR]

Genetic parameters and breeding values for live weight from birth to weaning (85 days) of Sardi sheep were estimated using random regression (RR) and multi-trait (MT) animal models. A longitudinal data set for live weight of 44,509 lamb progeny of 605 sires and 18,569 dams were used to describe variance components along growth curve trajectory using RR model. This data set was collected in 89 flocks between 2001 and 2012, with a total of 45,941 animals in the pedigree. Eleven RR models were compared based on the log likelihood and Akaike information criterion. The model with the best fit was found to include quadratic Legendre polynomials for direct and maternal genetic effects and direct permanent environment effects and a linear polynomial for maternal permanent environment effects, including covariance between direct and maternal genetic effects and a heterogeneous residual variance. Direct and maternal heritabilities were 0.11 and 0.03, respectively, at the first day of age, and 0.37 and 0.10, respectively, at 85 days of age. The increase of heritability and genetic variance indicated that there was a large potential for selection of Sardi sheep and suggested that selection during the study period would be more effective if it was done on weaning weight. The low genetic correlation between early weight and weaning weight suggests that these traits are controlled by different genes. Genetic parameters obtained at the selected ages (i.e. 10, 30, 70 days) using MT and RR models were generally similar, but the two methods produced different ranking of genetic merit. This study has demonstrated the possibility of applying RR model for routine genetic evaluation of Sardi sheep. [ABSTRACT FROM AUTHOR]

• Moderate estimates of heritability were observed for the growth traits of Nellore sheep which was unexpected. • The estimate of heritability implies a better expected response to selection in the flock. • Maternal effects were found to be high at all the stages of lamb growth, surprisingly even at yearling age. • Moderate estimates of correlation were observed for the consecutively recorded traits. • This study reveals the decisive information which is necessary for implementation of optimum breeding and management strategies for growth traits in this sheep. The main aim of this study is to estimate genetic parameters and (co) variance components for body weights at various ages in Nellore sheep born during the period from 1993 to 2016 (23 years). A total of 4822 lambs with pedigree descended from 144 sires and 1299 dams were included in this study. Bayesian analysis using Gibbs sampler multi trait animal model with and without maternal effects was studied. Traits included in this study were birth weight (BW), weaning weight (WW), six months body weight (6MW), nine months body weight (9 MW), Yearling weight (YW). Year-season of birth and sex of lamb (contemporary groups) were considered as fixed effects with ewe weight at lambing as a covariate. Estimates of direct heritability estimates for birth weight, WW, 6MW, 9MW and YW based on best model were 0.17, 0.28, 0.27, 26 and 0.23, respectively and the corresponding maternal heritability estimates were 0.12, 0.14, 0.14, 0.12 and 0.13, respectively. The direct-maternal genetic covariance was negative for all the studied traits. Strong and positive estimates of direct additive genetic correlations were observed in growth traits and they ranged from 0.16 to 0.83, whereas the direct-maternal genetic correlations were negative for all the traits and these estimates ranged from ˗0.89 to ˗0.08. Moderate estimates of heritability for the growth traits obtained in this study suggested the presence of better genetic variability and the possibility for genetic improvement of Nellore sheep through selection. Maternal genetic effects were significant in all the traits. Hence, it is recommended to incorporate these effects in the genetic analysis by including it as a genetic or environmental effect or both. The environmental variance obtained in the study suggested that the growth performance of Nellore sheep may be enhanced by improving the feeding and management aspects. Correlations among traits and effects should be considered while implementing breed improvement program. [ABSTRACT FROM AUTHOR]