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Greenhouse Gas (GHG) Emission Estimation for Cattle: Assessing the Potential Role of Real-Time Feed Intake Monitoring

Author

Listed:
  • Janine I. Berdos

    (Ruminant Nutrition and Anaerobe Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea
    Department of Animal Science, College of Agriculture and Forestry, Tarlac Agricultural University, Camiling 2306, Tarlac, Philippines)

  • Chris Major Ncho

    (Ruminant Nutrition and Anaerobe Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea)

  • A-Rang Son

    (Ruminant Nutrition and Anaerobe Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea)

  • Sang-Suk Lee

    (Ruminant Nutrition and Anaerobe Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea)

  • Seon-Ho Kim

    (Ruminant Nutrition and Anaerobe Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea)

Abstract

This study investigated the impact of feeding systems on the determination of enteric methane (CH 4 ) emissions factor in cattle. Real-time feed intake data, a crucial CH 4 conversion rate ( Y m value) parameter, were obtained using a roughage intake control (RIC) unit within a smart farm system. Greenhouse gas (GHG) emissions, including CH 4 and carbon dioxide (CO 2 ), from Holstein steers were monitored using a GreenFeed (GF) 344 unit. The results revealed satisfactory body weight (383 ± 57.19 kg) and daily weight gain (2.00 ± 0.83 kg), which are crucial factors. CO 2 production exhibited positive correlations with the initial body weight (r = 0.72, p = 0.027), feed intake (r = 0.71, p = 0.029), and feed conversion ratio (r = 0.69, p = 0.036). Five different emission factors (EFs), EF A (New Equation 10.21A) and Equation 10.21 (EF B , EF C , EF D , and EF E ), were used for GHG calculations following the Intergovernmental Panel on Climate Change (IPCC) Tier 2 approach. The estimated CH 4 EFs using these equations were 69.91, 69.91, 91.79, 67.26, and 42.60 kg CH 4 /head/year. These findings highlight the potential for further exploration and adoption of smart farming technology, which has the potential to enhance prediction accuracy and reduce the uncertainty in Y m values tailored to specific countries or regions.

Suggested Citation

  • Janine I. Berdos & Chris Major Ncho & A-Rang Son & Sang-Suk Lee & Seon-Ho Kim, 2023. "Greenhouse Gas (GHG) Emission Estimation for Cattle: Assessing the Potential Role of Real-Time Feed Intake Monitoring," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14988-:d:1261840
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    References listed on IDEAS

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    1. Mahfuzul Islam & Seon-Ho Kim & A-Rang Son & Sung-Sill Lee & Sang-Suk Lee, 2022. "Breed and Season-Specific Methane Conversion Factors Influence Methane Emission Factor for Enteric Methane of Dairy Steers," Sustainability, MDPI, vol. 14(12), pages 1-9, June.
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    Cited by:

    1. Suresh Neethirajan, 2023. "Innovative Strategies for Sustainable Dairy Farming in Canada amidst Climate Change," Sustainability, MDPI, vol. 16(1), pages 1-37, December.

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