Advancing Genomic Evaluation for Methane Efficiency in Walloon Holstein Cattle towards Implementation

Abstract

For several years, dairy cattle breeding in the Walloon Region of Belgium has increasingly focused on sustainability, including strategies for reducing methane emissions. Genetic selection provides a viable long-term approach to mitigating methane emissions while maintaining economic viability. The current study aimed to present a single-step genomic evaluation framework for methane efficiency (ME) based on predicted methane (PCH4) derived from milk mid-infrared (MIR) spectra and its integration into the existing genomic evaluation system for Holstein dairy cattle. The study incorporated data from 285 530 first-parity, 224 643 second-parity, and 160 226 third-parity Holstein cows across 1 520 herds. Genomic information from 9 631 animals, including 1 823 bulls, was integrated using a single-step GBLUP approach with a three-trait model (PCH4 across three parities). The predictive accuracy of the genomic evaluation framework was validated using a set of 2 038 youngest genotyped animals. Approximate genetic correlations (AGC) were calculated between PCH4 and 37 traits included in the Walloon breeding goal. Three methane efficiency (ME) indices were evaluated: relative ME based on production (RMEP), relative ME based on functionality (RMEF), and relative ME based on a global economic index (RMEG). The results demonstrated that the mean daily PCH4 ranged from 324 to 367 g/day, with mean daily heritability estimates between 0.20 and 0.23 for the first three lactations. The genomic prediction accuracy for PCH4-GEBV was 0.83. The AGC between PCH4 and the 37 traits ranged from -0.16 (milk yield) to 0.53 (fat percentage), highlighting the importance of balancing methane reduction with economic performance. Among the three ME indices, RMEG exhibited the most favorable balance, supporting its integration into genomic evaluations. Bulls with higher ME indices produced progeny with lower methane emissions, demonstrating the potential for genetic selection to contribute to sustainability goals. In light of these findings, we propose that INTERBULL considers methane for international genetic evaluations as many countries start to generate breeding values. These and other MACE breeding values would allow us to generate ME indices locally. Further discussions should focus on integrating reducing methane into breeding programs while maintaining productivity and functionality traits, as well as exploring strategies to incorporate direct methane measurements. Alternative thinking and use of tools like desired gain index will be required, but most important will be better knowledge about economic value of methane and its genetic relationship to other traits of interest. These initiatives will support sustainable dairy breeding strategies, aligning environmental and economic objectives for the future.