Jae Gu Lee

Objective: This study analyzed the impact of age at first calving (AFC) on the reproduction and longevity of Hanwoo (Korean beef cattle) cows and proposed selection indices for genetic improvement of AFC. Methods: Data on the age at first calving, lifetime calving count, lifespan, and productive lifespan of 30,534 cows born between 2010 and 2023 and culled or sold at 280 farms of 4 cooperatives in the Chungnam region participating in the Hanwoo cow test project, as well as 49,723 calving interval data of these cows were used for analysis. Results: The most frequent AFC for Hanwoo cows was approximately 24 months (736 days). Reproductive efficiency was highest when AFC was close to 24 months, showing the shortest calving intervals. Superior performance in longevity and productive life was observed within the 22–25 month range of AFC. The heritability of AFC was 0.12, which was higher than that of the calving interval (0.08), indicating potential for genetic improvement through selection. Economic losses occurred if the age of first calving deviates from the appropriate age range. Conclusion: The study suggests selection index models that apply different economic weights based on 23 and 24 month split points of AFC data, where the genetic characteristics of AFC change significantly. In addition, each model assigns different economic weights for AFC according to the positive (+) and negative (−) values of the breeding value.

An optimal genetic evaluation model for milk, fat, and protein yield, and somatic cell score traits across the first 3 parities in South Korean Holsteins was developed by comparing alternative random regression models. Variance component estimations and model comparisons were made among 5 different random regression test-day models incorporating herd-related factors, including Herd-Test-day (HTD), Herd-Year of calving (HY), Herd-Year-Season of calving (HYS) and Herd-Year-Month of calving (HYM). The models were compared using multiple indicators to measure the bias, the goodness of fit, and the accuracy of genetic evaluation. When HTD factor was considered as a fixed effect, the model demonstrated the best goodness of fit but showed a large decrease in the accuracy of genetic evaluation. By contrast, when HTD factor was included as a random effect together with the contemporary group factor, both high accuracy of genetic evaluation and superior goodness of fit were maintained. Furthermore, as the contemporary group definition was refined from HY to HYS and to HYM, additive genetic variance, heritability estimates, and the accuracy of genetic evaluation increased markedly, indicating that HYM provides a more appropriate definition of the contemporary group. Consequently, Model_HYM+HTD(R) was determined to be the most appropriate genetic evaluation model for milk production and somatic cell score traits in South Korean Holsteins.

Objective: To investigate genetic correlation between calving ease (CE) and gestation length (GL) traits of Korean Holstein cattle to understand genetic structures of these two traits and their potential implications.Methods: Records of progenies from first parity (P1, N = 117,921) and second parity (P2, N = 141,104) Holsteins cows were used for analysis. All phenotypes (CE and GL) were considered as calf traits. The CE was an ordered categorical trait. It was scored from 1 (normal calving) to 4 (difficult calving). GL observations were restricted between 260 and 305 days. Variance components and genetic parameters were estimated through a bivariate animal model with a correlated maternal effect using the BLUPF90+ software package.Results: Heritability (h2) estimates of CE for direct and maternal effects were low (less than 0.01) in all parity calves. For GL, despite lower h2 of maternal effect (~0.03), the direct effect was moderately heritable (0.20 to 0.23) in this study. Direct and maternal effects of CE trait were weakly correlated (P1: 0.09±16.60, P2: −0.04±0.00). GL had similar correlation patterns (P1: 0.03±0.00; P2: −0.15±0.05) across parities. Direct genetic correlations of GL and CE were mostly weak (P1: 0.18±0.31; P2: −0.01±0.06), whereas maternal genetic correlations were moderate and positive (P1: 0.39±0.95; P2: 0.46±0.04). Although the genetic influence of GL on CE was not entirely clear due to large estimation errors for parameters, overall positive associations between direct effects and maternal effects essentially indicate a selection potential for GL as an indicator trait of CE.Conclusion: This is the first genetic correlation investigation of GL and CE in Korean Holstein cattle. It provides important insights into genetic architectures of GL and its future potential as an indicator trait for CE improvements in Korean Holsteins.

This study estimated the genetic parameters of calving ease (CE) in Korean Holstein using linear animal-maternal (AMAT) and linear sire-maternal grandsire (SMGS) models. Calves born from the first three parities of cows (P1, P2, P3) between 2000 and 2024 were analyzed in two parity-level data subsets. The first subset comprised 133,998 (P1), 185,988 (P2), and 122,297 (P3) records. The second subset had at least seven records per herd-year subclass, with 104,469, 104,095, and 46,280 records for P1, P2, and P3, respectively. CE was defined as a calf trait, and the scores ranged between 1 and 4. Higher scores indicated greater difficulty at birth. Parity-level (co) variances were obtained for each dataset using the BLUPF90+ software package. Heritability (h²) values for direct effects ranged between 0.002 and 0.008. Maternal h² values from the AMAT and SMGS models were between 0.002 and 0.353 and between 0.004 and 0.008, respectively. Genetic correlations between direct and maternal effects varied widely in the AMAT model but were relatively narrow in the SMGS model. The correlation of estimated breeding value (EBV) of sire between datasets and sire EBV reliabilities was more stable for SMGS than AMAT. We conclude that the AMAT model would be suitable for routine evaluations due to extensive population coverage, whereas SMGS would be better for robust genetic parameter estimations. To leverage the strengths of both models, we suggest using the genetic (co)variance components estimated from the SMGS model within the framework of the AMAT model for the national evaluation of CE in Korean Holstein cattle.

Objective: In Korea, dairy cattle breeding programs have historically prioritized productive, conformation traits, leading to positive improvements, yet reproductive traits have lagged in development. This study was conducted to develop the breeding program of key reproductive traits in the Korean dairy cattle population.Methods: Utilizing data from 7,596 farms and over seven million observations, we conducted quality control to rectify manual entry errors and selected traits in line with international genetic evaluation standards. Traits analyzed included heifer conception rate (HCR), interval from calving to first insemination (CF), cow conception rate (CCR), interval from first to last insemination (FL), and days open (DO). Genetic parameters were estimated using a single trait animal model for HCR and a multiple lactation animal model for CF, CCR, FL, and DO, considering contemporary group of herd-insemination year, insemination month, and monthly age as fixed effects.Results: Results showed low heritability estimates, ranging from 0.007 to 0.035 across different traits and lactations. Theoretical reliability appears to be low on average due to the influence of heritability, but it showed sufficiently high reliability in some sires (over 0.8). In terms of genetic and phenotypic trends, capacity for reproductive traits declined for a long time until around 2014. In recent individuals, improved trend can be found.Conclusion: This study addressed the critical need for enhancing reproductive efficiency to complement the existing breeding goals, thereby supporting sustained economic viability in the dairy industry. The results underscore the need for improved data quality and methodological adjustments for reproduction records to enhance the genetic evaluation of dairy cattle in Korea.

Objective: We investigated genetic parameters of calving ease (CE) using several animal models in Korean Holstein and searched for suitable models for routine evaluation of CE.Methods: Two phenotypic datasets of CE (DS5 and DS10) on first-parity Korean Holstein calves were prepared. DS5 and DS10 included at least 5 and 10 CE records per herd-year level and comprised 117,921 and 80,389 observations, respectively. The CE phenotypes ranged from 1 to 4, from a normal to extreme difficulty calving scale. The CE was defined as a trait of the calf. The BLUPF90+ software was used for (co)variances estimation through four animal models with a maternal effect (M1 to M4), where all models included effects of a fixed calf-sex, a fixed dam calving age (covariate), and one or more fixed contemporary group (CG) terms. The CG effects were different across models—a herd-year-season (M1, HYS), a herd-year and year-season (M2, HY+YS), a herd-year and season (M3, HY+S), and a herd and year-season (M4, H+YS).Results: Direct heritability (h2) estimates of CE ranged from 0.005 to 0.234 across models and datasets. Maternal h2 values were low (0.001 to 0.090). Genetic correlations between direct and maternal effects were strongly negative to lowly positive (–0.814 to 0.078), further emphasizing its importance in CE evaluation models. These genetic parameter estimates also indicate slower future selection progress of CE in Korean Holsteins. The M1 fitted many levels with fewer observations per level deriving unreliable parameters, and the M4 did not account for confounded herd and animal structures. The M2 and M3 were deemed more realistic for implementation, and they were better able to account for data structure issues (incompleteness and confounding) than other models.Conclusion: As the pioneering study to employ animal models in Korean Holstein CE evaluation, our findings hold significant potential for this breed's future and routine evaluation development.

The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

The rapid evolution of 3D technology in recent years has brought about significant change in the field of agriculture, including precision livestock management. From 3D geometry information, the weight and characteristics of body parts of Korean cattle can be analyzed to improve cow growth. In this paper, a system of cameras is built to synchronously capture 3D data and then reconstruct a 3D mesh representation. In general, to reconstruct non-rigid objects, a system of cameras is synchronized and calibrated, and then the data of each camera are transformed to global coordinates. However, when reconstructing cattle in a real environment, difficulties including fences and the vibration of cameras can lead to the failure of the process of reconstruction. A new scheme is proposed that automatically removes environmental fences and noise. An optimization method is proposed that interweaves camera pose updates, and the distances between the camera pose and the initial camera position are added as part of the objective function. The difference between the camera’s point clouds to the mesh output is reduced from 7.5 mm to 5.5 mm. The experimental results showed that our scheme can automatically generate a high-quality mesh in a real environment. This scheme provides data that can be used for other research on Korean cattle.