Methane produced by dairy cows is an important greenhouse gas that is partially responsible for global warming and impacts the sustainability of the dairy sector. Breeding for lower methane production could give a long-term and cumulative reduction of methane production, but requires methane phenotypes of many animals obtained at a relative ease. Methane production phenotypes in this study were acquired via two methods: 1) measured from breath samples in automatic milking systems (AMS) via sensors; 2) predicted from milk samples via mid-infrared (MIR) spectroscopy obtained from regular milk production registration. These methods provide efficient and cost-effective methane production phenotypes on large number of cows. The aim of this study is to explore the genetic background of phenotypes acquired via milk MIR and relate these phenotypes to phenotypes acquired via sensors in AMS. Data were collected on 11 commercial dairy farms in the Netherlands with in total 23 individual AMS. Data consisted of 123,374 AMS visits of 1,511 cows with sensor methane production phenotypes. Milk MIR methane prediction models were based on approximately 200 animals with methane measurements in climate respiration chambers and milk MIR. In total, 2,378 MIR methane phenotypes of 1,412 cows were predicted based on routine milk production registration samples of 11 farms. Heritabilities, i.e. the fraction of phenotypic variation due to genetics, of milk MIR methane phenotypes will be calculated to study its potential for breeding for reduced methane production. Furthermore, correlations between milk MIR methane phenotypes and sensor methane phenotypes will be calculated to study the relationship between both types of phenotypes. The results of the analysis of the genetic background of milk MIR methane phenotypes and their correlations with sensor methane phenotypes will be presented.
|Publication status||Published - 6 Feb 2017|
|Event||WIAS Science Day 2017 - Wageningen University, Orion building, Wageningen, Netherlands|
Duration: 6 Feb 2017 → 6 Feb 2017
|Conference||WIAS Science Day 2017|
|Period||6/02/17 → 6/02/17|