This study estimated genetic parameters for carcass and carbon emission-related traits in Hanwoo cattle using various genomic analytic models, and explored methods for improving the accuracy of genetic evaluation based on these estimations. The analysis results across all models showed high prediction accuracy for carcass traits. Notably, the weighted single-step genomic best linear unbiased prediction (wssGBLUP) method significantly improved the accuracy by enhancing the utilization of genomic information through the application of weights. In contrast, carbon emission intensity, which is highly influenced by environmental factors, generally exhibited a lower prediction accuracy than other carcass traits. However, the wssGBLUP model demonstrated a significant improvement in accuracy, even in predictions of carbon emission intensity, demonstrating that the weighted application of genomic information contributes to improved predictive power, even for traits with substantial environmental influence. The findings of this study present a new strategy for effectively utilizing genomic information in Hanwoo cattle improvement programs to simultaneously achieve the dual goals of enhancing productivity and reducing environmental load, thereby providing a scientific foundation for the sustainable development of the livestock industry.
Yu et al. (Thu,) studied this question.