Connectedness rating among commercial pig breeding herds in Korea

Performance and reproduction data were collected from fifteen Korean pig breeding herds (8 Yorkshire herds, 5 Landrace herds and 4 Duroc herds) born between 1997 and 2016. Two traits were considered to calculate the connectedness between pairs of herds: ADG and NBA. The numbers of records per breed and farm are presented in Tables 1 and 2.

Both ADG and NBA datasets were analyzed for each breed to estimate the breeding value using the following statistical model (1).

where y is the phenotype observation for ADG and NBA, b is a vector of fixed effects (herd effects), a is the vector of random effects (additive animal genetic effects), d is the vector of common litter effects, and e is a vector for environmental residuals $\left(\text{e}\sim \text{N}\left(0,I{\sigma }_e^2\right)\right)$. X, Z and H were used as incidence matrices corresponding to vectors b, a and d related to the random additive genetic effects $\left(\text{a}\sim \text{N}(0,A{\sigma }_a^2,\text{d}\sim \text{N}\left(0,I{\sigma }_d^2\right)\right).{\sigma }_a^2,{\sigma }_d^2$ and ${\sigma }_e^2$ represent the additive genetic variance, litter variance, and environmental residual variance, respectively.

The above statistical model was used to construct the mixed model equation (MME) resulting in equation (2).

where, A is the numerator genetic relationship matrix for animals, a₁ refers to ${\sigma }_e^2/{\sigma }_a^2$ and a₂ refers to ${\sigma }_e^2/{\sigma }_d^2$. The ASReml package [10] was used for solving equation (2).

CR was defined by the following equation (3):

The covariance for the herd and variance of estimation of each herd effect i and j were obtained by solving equation (2).

According to Mathur et al. [11], the accuracy of an individual EBV is estimated using the prediction error variance corresponding to the animals. The PEVD can be used to validate the accuracy of EBVs of two individuals. PEVD is formularized as:

PEVD can then be substituted as the variance of estimates of difference between herd effects (VHD) [11]:

The average CRs for ADG in each breed are listed in Table 3. For the ADG in Yorkshire, a total of 8 herds were analyzed. The average CR between two herds ranges from 1.32 (B herd) to 28.05 (E herd). The maximum CR value was 93.44 between herds E and G, and the lowest CR value was 4.4 between herds B and G. For the ADG trait in Landrace, a total of 5 herds were used for analysis. The highest average CR was 2.50 between herds A and F, and the lowest average CR was 0.55 between herds C and H. All four herds A, B, C, and G were used in Duroc. The highest average CR was 16.14 between herds C and G, and the lowest average CR was 5.03 between herds A and C.

The average CRs for NBA in each breed are presented in Table 4. The same numbers of herds that were used for ADG analysis were used to analyze NBA. The minimum average CR ranged from ~0 (herd A) to 12.79 (herd E) in Yorkshire. The maximum CR was 89.38 between herds E and G, and the lowest CR value was identified between herds A and F. The highest average CR was 0.09 (herd H), and the lowest average CR was ~0 (herds A and F) in Landrace. The highest average CR for NBA ranged from 1.17 to 4.70 in Duroc. According to Mathur et al. [6], the recommended minimum average CRs for ADG and NBA are 3% and 1.5%, respectively. When these criteria for both the performance and reproductive traits are met, the EBV comparison between herds can be performed accurately. The average CR values for the Landrace herd are below this criterion, so the values for the Landrace herd were excluded in the following evaluation step.

If two herds are highly connected, the PEVD decreases. The accuracy of the EBV is therefore greater when a pair of herds is evaluated jointly. According to Kennedy and Trus [4], the VHD is highly correlated with the average PEVD of pairwise comparisons of EBVs. Therefore, VHD can be used as an evaluation of CR. The VHDs for the Yorkshire and Duroc herds were calculated, but the VHD for the Landrace herd was not calculated due to its CR result. The PEVDs for the ADG and NBA traits in Yorkshire and Duroc breeds are shown in Tables 5, 6, 7, and 8. The tables show that the PEVD decreases as the CR increases, suggesting that the PEDV can be used as a validation indicator for the accuracy of the CR.

Based on the results, it can be observed that there are significant differences in CR between different herds for each of the evaluated breeds. For instance, in the case of the Korean Yorkshire breed, the CR between herds E and G is very high at 93.44, whereas the CR between herds B and G is relatively low at 4.4. These findings indicate that the level of genetic exchange varies among different herds, which can have implications for the reliability of genetic evaluations and breeding programs. Another important observation is the relationship between the size of the contemporary group (CG) and the accuracy of the EBVs. When the size of the CG is less than ten animals, the accuracy of the EBVs significantly decreases. Therefore, it is crucial for each CG to consist of at least 10 or more pigs to achieve higher accuracy, which ensures a sufficient sample size for more reliable genetic evaluations and breeding decisions.

Increasing connectedness is crucial for breeding programs, which can be achieved by using common sires from multiple herds or sharing genetically superior AI boars. By doing so, it becomes possible to conduct extensive genetic comparisons across herds, explore the potential for large-scale selection, and achieve greater genetic progress. The continuous supply of genetically superior pigs is directly related to the active participation of swine producers in breeding programs, who can contribute by providing their own genetically superior animals or by participating in the formation of a pool of superior AI boars. Such active involvement allows for the improvement of the national breeding program’s structure, leading to increased genetic variability and connectedness. As a result, the participation of swine producers plays a crucial role in the field of animal breeding and helps foster greater genetic advancements and overall progress.