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Laser methane detector-based quantification of methane emissions from indoor-fed Fogera dairy cows

  • Kobayashi, Nobuyuki (Arid Land Research Center, Tottori University) ;
  • Hou, Fujiang (State Key Laboratory of Grassland Agroecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University) ;
  • Tsunekawa, Atsushi (Arid Land Research Center, Tottori University) ;
  • Yan, Tianhai (Agri-Food and Biosciences Institute) ;
  • Tegegne, Firew (Bahir Dar University) ;
  • Tassew, Asaminew (College of Agriculture and Environmental Sciences, Bahir Dar University) ;
  • Mekuriaw, Yeshambel (College of Agriculture and Environmental Sciences, Bahir Dar University) ;
  • Mekuriaw, Shigdaf (The United Graduate School of Agricultural Sciences, Tottori University) ;
  • Hunegnaw, Beyadglign (Andassa Livestock Research Center, Amhara Region Agricultural Research Institute) ;
  • Mekonnen, Wondimeneh (Andassa Livestock Research Center, Amhara Region Agricultural Research Institute) ;
  • Ichinohe, Toshiyoshi (Faculty of Life and Environmental Science, Shimane University)
  • 투고 : 2020.10.23
  • 심사 : 2020.12.14
  • 발행 : 2021.08.01

초록

Objective: Portable laser methane detectors (LMDs) may be an economical means of estimating CH4 emissions from ruminants. We validated an LMD-based approach and then used that approach to evaluate CH4 emissions from indigenous dairy cows in a dryland area of Ethiopia. Methods: First, we validated our LMD-based approach in Simmental crossbred beef cattle (n = 2) housed in respiration chambers and fed either a high- or low-concentrate diet. From the results of the validation, we constructed an estimation equation to determine CH4 emissions from LMD CH4 concentrations. Next, we used our validated LMD approach to examine CH4 emissions in Fogera dairy cows grazed for 8 h/d (GG, n = 4), fed indoors on natural-grassland hay (CG1, n = 4), or fed indoors on Napier-grass (Pennisetum purpureum) hay (CG2, n = 4). All the cows were supplemented with concentrate feed. Results: The exhaled CH4 concentrations measured by LMD were linearly correlated with the CH4 emissions determined by infrared-absorption-based gas analyzer (r2 = 0.55). The estimation equation used to determine CH4 emissions (y, mg/min) from LMD CH4 concentrations (x, ppm m) was y = 0.4259x+38.61. Daily CH4 emissions of Fogera cows estimated by using the equation did not differ among the three groups; however, a numerically greater milk yield was obtained from the CG2 cows than from the GG cows, suggesting that Napier-grass hay might be better than natural-grassland hay for indoor feeding. The CG1 cows had higher CH4 emissions per feed intake than the other groups, without significant increases in milk yield and body-weight gain, suggesting that natural-grassland hay cannot be recommended for indoor-fed cows. Conclusion: These findings demonstrate the potential of using LMDs to valuate feeding regimens rapidly and economically for dairy cows in areas under financial constraint, while taking CH4 emissions into consideration.

키워드

과제정보

We acknowledge the support of the Andassa Livestock Research Center, Amhara Region Agricultural Research Institute, Ethiopia, and the College of Grassland Science of Lanzhou University, China, for providing the respiration chambers (registered as LZUCKY-S-DXCLZ-001 for Institute of Grassland and Livestock Production System, Lanzhou University). This study was supported by the Marginal Region Agriculture Project of Tottori University; the Science and Technology Research Partnership for Sustainable Development (SATREPS) Project for Development of Next-generation Sustainable Land Management (SLM) Framework to Combat Desertification (JPMJSA1601) of the Japan Science and Technology Agency and Japan International Cooperation Agency; the Strategic Priority Research Program of Chinese Academy of Science (XDA20100102); the Key R & D Program of Ningxia Hui Autonomous Region (2019BBF02001); and the Program for Changjiang Scholars and Innovative Research Team at the University of China (IRT17R50).

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