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Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed

R Liu1, D Hailemariam2, T Yang2

  • 1Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada; Key Laboratory of Animal Breeding and Reproduction of Ministry of Education, Hauzhong Agricultural University, Wuhan 430070, China.

Animal : an International Journal of Animal Bioscience
|February 22, 2022
PubMed
Summary

Predicting dairy cattle methane emissions using milk's mid-infrared (MIR) spectra and the GreenFeed system shows moderate accuracy for methane intensity. This method could aid breeding goals by providing more accessible data on enteric methane emissions.

Keywords:
Dairy cowsMethane intensityPredictionProductionYield

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Area of Science:

  • Animal Science
  • Environmental Science
  • Agricultural Engineering

Background:

  • Methane emission from dairy cattle is a significant environmental concern but is not currently included in breeding goals due to data collection challenges.
  • Existing methods for measuring methane (e.g., respiration chambers, SF6, sniffer) are often expensive and difficult to implement for large-scale data generation.
  • The potential of using milk mid-infrared (MIR) spectra combined with data from the GreenFeed system for predicting methane emission has not been previously investigated.

Purpose of the Study:

  • To explore the feasibility of predicting methane emission traits (production, yield, intensity) in dairy cattle using milk MIR spectra and GreenFeed system data.
  • To develop and validate prediction models for daily and average methane emission metrics.
  • To assess the accuracy of these models for potential integration into dairy cattle breeding programs.

Main Methods:

  • Methane emission data were collected for 151 dairy cows using the GreenFeed system.
  • Partial least squares regression was employed to build prediction models using milk MIR spectra, parity, and dry matter intake (DMI).
  • Model performance was rigorously evaluated through 100 repeated validation cycles with an 80% training and 20% testing data split.

Main Results:

  • The best model predicted average methane intensity with a moderate validation coefficient of determination (R²val) of 0.66.
  • Predictions for average methane production (R²val = 0.28) and average methane yield (R²val = 0.12) showed poor accuracy.
  • Models using daily records exhibited lower accuracy than those using average values, with methane intensity prediction (R²val = 0.42) being better than production (R²val = 0.17).

Conclusions:

  • Predicting methane intensity in dairy cattle using milk MIR spectra and GreenFeed data is feasible with moderate accuracy.
  • Average methane emission values were predicted more accurately than daily measures.
  • This approach offers a promising, potentially more accessible method for estimating methane emissions, which could inform future breeding strategies for dairy cattle.