Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

Abstract

This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH4/DM: 0.232–0.317) and methane intensity per kg of corrected milk (CH4/CM: 0.177–0.279), smaller per kg cheese solids (CH4/SO: 0.093–0.165), but greater per kg fresh cheese (CH4/CU: 0.203–0.267) and for methane production (dCH4: 0.195–0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH4/DM (0.45–0.60), and very variable for dCH4 according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH4/CM, CH4/CU and dCH4, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

Effects of breed, farm intensiveness, and cow productivity on infrared predicted milk urea

“…Other traits have also been found to show greater heritabilities when predicted by infrared spectroscopy than when analyzed by wet chemistry (Cecchinato et al, 2011Bittante and Cipolat-Gotet, 2018), but this was mainly attributable to the greater repeatability of infrared spectra with respect to the wet chemistry analysis for some traits (Bittante and Cecchinato, 2013). The real usefulness of infrared predictions can be better evaluated by their genetic correla- tion with wet chemistry (gold standard) values than by their phenotypic correlation (Rutten et al, 2010;Bittante et al, 2014Bittante et al, , 2020, which, in the case of a study on MU by Mitchell et al (2005), was estimated to be moderate-low (0.38 for first and 0.23 for secondlactation milk samples). The predictive methodologies (e.g., instruments, data treatment) used for MU prediction changed and improved over time.…”

Utility of indirect phenotype measurement system on genomic prediction in Japanese Black cattle: a simulation study using public data

“…Fatty acid composition has also been studied in foreign beef cattle populations in terms of not only meat taste and flavor but also human health (Saachi et al 2013;Chen et al 2015;Chiaia et al 2017;Zhu et al 2017). One of the challenges in the efficient genetic improvement of novel traits is that large-scale data acquisition is costly and time-consuming (e.g., Jonker et al 2018;Bittante et al 2020;Lozada-soto et al 2021). Thus, the development of an alternative indirect measurement system is often performed to enable cost-effective phenotypic data collection.…”

A Longitudinal Study with a Laser Methane Detector (LMD) Highlighting Lactation Cycle-Related Differences in Methane Emissions from Dairy Cows

“…However, the diversity of animal farming systems around the globe, associated with their socioeconomic and cultural contexts, creates different GHG emissions patterns, which require regionally tailored measures for emissions reduction [ 2 ]. Several years of research on CH 4 mitigation strategies have shown promising results through genetics [ 3 ], nutritional management [ 4 ], and vaccination [ 5 ], mostly in the context of experimental studies. Nevertheless, a critical target in this search is the suitability of techniques for measuring livestock CH 4 emissions and a likely limiting factor for broadening the assessment at larger scales.…”