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

Janine I. Berdos, Chris Major Ncho, A-Rang Son, Sang-Suk Lee () and Seon-Ho Kim ()
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Janine I. Berdos: Ruminant Nutrition and Anaerobe Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea
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

Sustainability, 2023, vol. 15, issue 20, 1-17

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.

Keywords: emission factor; GreenFeed; greenhouse gas; roughage intake control unit; smart farming (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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