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Browsing by Author "Chen, Chun-Peng"

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    Genetic Heterogeneity of Residual Variance for Production and Functional Traits in American Angus Cattle
    Amorim, Sabrina Thaise (Virginia Tech, 2024-08-14)
    Beef cattle are continuously selected for different traits and the success in improving these traits has been remarkable. However, for certain traits, it is essential not only to improve the average performance, but also to control the variation around the mean. There is evidence that residual variance may be under genetic control, which opens the possibility of selecting for uniformity. In this sense, the objectives of the present dissertation were: 1) to investigate the extent of genetic heterogeneity of residual variance at the pedigree level in birth weight (BW), weaning weight (WW), yearling weight (YW), foot angle (FA), and claw set (CS) in American Angus cattle; 2) to compare the results of different genetic heterogeneity models; 3) to evaluate the effectiveness of Box-Cox transformation in continuous traits; and 4) to address limitations and explore alternative solutions for implementing genetic parameters for residual variance in genetic evaluations. The first study investigated the genetic heterogeneity of residual variances for BW, WW, and YW. Three models were compared: a homoscedastic residual variance model (M1), a double hierarchical generalized linear model (DHGLM, M2), and a genetically structured environmental variance model (MCMC, M3). The results showed significant genetic heterogeneity of residual variances in growth traits, suggesting the possibility of selection for uniformity. The genetic coefficient of variation for residual variance ranged from 0.90 to 0.92 in M2 and 0.31 to 0.38 in M3 for BW, 0.64 in M2 and 0.01 to 0.29 in M3 for WW, and 0.67 to 0.63 in M2 and 0.25 to 0.31 in M3 for YW. Low heritability estimates for residual variance were found, particularly in M2 (0.08 for BW, 0.06 for WW, and 0.09 for YW). The study identified both negative and positive genetic correlations between mean and residual variance, depending on the trait and data transformation. Negative correlations suggest the potential to increase trait means while decreasing residual variance. However, positive correlations indicate that the genetic response to selection for uniformity may be limited unless a selection index is used. Data transformation reduced skewness but did not eliminate genetic heterogeneity of residual variances. The Bayesian approach provided higher estimates of additive genetic variance for residual variance compared to DHGLM. Overall, the findings indicate the potential to reduce variability through selection and lay the groundwork for incorporating uniformity of growth traits into breeding goals. The second study focused on the genetic heterogeneity of residual variance for two foot conformation traits, FA and CS. Using 45,667 phenotypic records collected between 2009 and 2021, three models were compared: a traditional homoscedastic residual variance model (M1), a DHGLM (M2), and a genetically structured environmental variance model (M3). Results showed that heritability estimates for FA and CS means were within expected ranges, although lower in M2. Despite low heritability estimates for residual variance (0.07 for FA and 0.05 for CS in M2), significant genetic coefficients of variation were found, suggesting that selection on trait mean would also influence residual variance. Positive genetic correlations between mean and residual variance in M2 and M3 indicate that selection for uniformity is feasible, but may require additional strategies such as selection indices. The study highlights the potential of FA and CS as indicators for breeding programs aimed at improving production uniformity in beef cattle. Our findings suggest that selection for uniformity in growth and foot score traits in beef cattle may be limited by low heritability of residual variance and moderate to high positive genetic correlations between mean and residual variance. This was observed for most of the traits studied. To overcome these challenges, further research is needed, particularly to explore genomic information to improve the prediction accuracy of estimated breeding values (EBV) for residual variance. Although studies of uniformity using genomic data are limited, they have shown improved EBV accuracy for residual variance. Additionally, alternative methods for measuring uniformity, such as different uniformity or resilience indicators, should be considered, especially with advances in digital phenotyping. Precision livestock farming technologies that allow for extensive data collection on various production traits should be integrated into the development of new uniformity indicators. This dissertation provides valuable insights into the genetic heterogeneity of residual variance in American Angus cattle and highlights the complexity of selecting for uniformity while improving mean traits. Continued research with larger data sets, genomic information, and further methodological refinement will be critical to advance these findings to improve uniformity and productivity in beef cattle breeding.
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    A model generalization study in localizing indoor cows with cow localization (colo) dataset
    Das, Mautushi (Virginia Tech, 2024-07-10)
    Precision livestock farming increasingly relies on advanced object localization techniques to monitor livestock health and optimize resource management. In recent years, computer vision-based localization methods have been widely used for animal localization. However, certain challenges still make the task difficult, such as the scarcity of data for model fine-tuning and the inability to generalize models effectively. To address these challenges, we introduces COLO (COw LOcalization), a publicly available dataset comprising localization data for Jersey and Holstein cows under various lighting conditions and camera angles. We evaluate the performance and generalization capabilities of YOLOv8 and YOLOv9 model variants using this dataset. Our analysis assesses model robustness across different lighting and viewpoint configurations and explores the trade-off between model complexity, defined by the number of learnable parameters, and performance. Our findings indicate that camera viewpoint angle is the most critical factor for model training, surpassing the influence of lighting conditions. Higher model complexity does not necessarily guarantee better results; rather, performance is contingent on specific data and task requirements. For our dataset, medium complexity models generally outperformed both simpler and more complex models. Additionally, we evaluate the performance of fine-tuned models across various pre-trained weight initialization. The results demonstrate that as the amount of training samples increases, the advantage of using weight initialization diminishes. This suggests that for large datasets, it may not be necessary to invest extra effort in fine-tuning models with custom weight initialization. In summary, our study provides comprehensive insights for animal and dairy scientists to choose the optimal model for cow localization performance, considering factors such as lighting, camera angles, model parameters, dataset size, and different weight initialization criteria. These findings contribute to the field of precision livestock farming by enhancing the accuracy and efficiency of cow localization technology. The COLO dataset, introduced in this study, serves as a valuable resource for the research community, enabling further advancements in object detection models for precision livestock farming.