COMPARISON OF STATISTICAL MODELS FOR ESTIMATION OF METHANE EMISSION IN DAIRY SIMMENTALS BASED ON ANIMAL RECORDING DATA

Abstract

In the last decades we have witnessed increasingly pronounced climate change worldwide resulting in environment transformation in various regions by making it not convenient for agricultural and livestock production. The global livestock sector contributes to anthropogenic greenhouse gas emission, but on the other hand, it can also deliver a significant share of the necessary mitigation effort. One of the most significant greenhouse gas is methane. Mitigation methods for the methane emissions in cattle can be classified as short and long term. Short-term methods imply increase of production per animal, reduction of number of animals and feeding optimization, while long-term methods imply genetic evaluation and selection based on methane emission variation. Prerequisite for genetic evaluation is selection of optimal indicators and models with high accuracy and easy applicability in routine Animal Recording. Therefore, the objective of this study was to evaluate different models for methane emission estimation in dairy cows based on Animal Recording data. The results obtained indicate that data from regular Animal Recording could be used in estimation of methane emission of dairy Simmental cows enabling the population analysis and genetic evaluation of dairy cattle for methane emission. Given the very high variability determined in estimated methane emission values regarding the used statistical models and aiming high accuracy of genetic evaluation it is recommended to define estimation models for body weight, dry matter intake and methane emission based on parameters (type traits and test-day records) of particular dairy cattle population. The stated will enable genetic evaluation of dairy cattle for methane emission as well as selection of cows with lower methane emission intensity. Finally, this will lead to environmentally sustainable milk production.