Review of equations to predict methane emissions in dairy cows from milk fatty acid profiles and their application to commercial dairy farms

被引:1
作者
Massaro, S. [1 ]
Giannuzzi, D. [1 ]
Amalfitano, N. [1 ]
Schiavon, S. [1 ]
Bittante, G. [1 ]
Tagliapietra, F. [1 ]
机构
[1] Univ Padua, Dept Agron Food Nat Resources Anim & Environm DAFN, I-35020 Legnaro, PD, Italy
关键词
global warming; environmental impact; rumen fermentation; stage of lactation; dairy sustainability; TO-THE-EDITOR; ENTERIC METHANE; LACTATION-STAGE; MIDINFRARED SPECTRA; GAS-CHROMATOGRAPHY; CATTLE; YIELD; MITIGATION; PRODUCTIVITY; METAANALYSIS;
D O I
10.3168/jds.2024-24814
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 f 2.26 g/kg DMI, 7 equations), methane intensity (16.05 f 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 f 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 f 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.
引用
收藏
页码:5833 / 5852
页数:20
相关论文
共 65 条
[31]   Lactation curves and model evaluation for feed intake and energy balance in dairy cows [J].
Harder, I. ;
Stamer, E. ;
Junge, W. ;
Thaller, G. .
JOURNAL OF DAIRY SCIENCE, 2019, 102 (08) :7204-7216
[32]  
Holtenius K., 2018, Proceedings of the 9th Nordic Feed Science Conference, Uppsala, Sweden, 12-13 June 2018, P101
[33]   Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models [J].
Hristov, A. N. ;
Kebreab, E. ;
Niu, M. ;
Oh, J. ;
Bannink, A. ;
Bayat, A. R. ;
Boland, T. M. ;
Brito, A. F. ;
Casper, D. P. ;
Crompton, L. A. ;
Dijkstra, J. ;
Eugene, M. ;
Garnsworthy, P. C. ;
Haque, N. ;
Hellwing, A. L. F. ;
Huhtanen, P. ;
Kreuzer, M. ;
Kuhla, B. ;
Lund, P. ;
Madsen, J. ;
Martin, C. ;
Moate, P. J. ;
Muetzel, S. ;
Munoz, C. ;
Peiren, N. ;
Powell, J. M. ;
Reynolds, C. K. ;
Schwarm, A. ;
Shingfield, K. J. ;
Storlien, T. M. ;
Weisbjerg, M. R. ;
Yanez-Ruiz, D. R. ;
Yu, Z. .
JOURNAL OF DAIRY SCIENCE, 2018, 101 (07) :6655-6674
[34]   A meta-analysis comparing four measurement methods to determine the relationship between methane emissions and dry-matter intake in New Zealand dairy cattle [J].
Jonker, Arjan ;
Green, Peter ;
Waghorn, Garry ;
van der Weerden, Tony ;
Pacheco, David ;
de Klein, Cecile .
ANIMAL PRODUCTION SCIENCE, 2020, 60 (01) :96-101
[35]   Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions [J].
Knapp, J. R. ;
Laur, G. L. ;
Vadas, P. A. ;
Weiss, W. P. ;
Tricarico, J. M. .
JOURNAL OF DAIRY SCIENCE, 2014, 97 (06) :3231-3261
[36]   Variations in methane yield and microbial community profiles in the rumen of dairy cows as they pass through stages of first lactation [J].
Lyons, Tamsin ;
Bielak, Anita ;
Doyle, Evelyn ;
Kuhla, Bjoern .
JOURNAL OF DAIRY SCIENCE, 2018, 101 (06) :5102-5114
[37]   Multi-breed herd approach to detect breed differences in composition and fatty acid profile of cow milk [J].
Manuelian, Carmen L. ;
Penasa, Mauro ;
Visentin, Giulio ;
Benedet, Anna ;
Cassandro, Martino ;
De Marchi, Massimo .
CZECH JOURNAL OF ANIMAL SCIENCE, 2019, 64 (01) :11-16
[38]   Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions [J].
Martinez-Marin, Gustavo ;
Toledo-Alvarado, Hugo ;
Amalfitano, Nicolo ;
Gallo, Luigi ;
Bittante, Giovanni .
JOURNAL OF DAIRY SCIENCE, 2024, 107 (03) :1485-1499
[39]   Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level [J].
Martinez-Marin, Gustavo ;
Schiavon, Stefano ;
Tagliapietra, Franco ;
Cecchinato, Alessio ;
Toledo-Alvarado, Hugo ;
Bittante, Giovanni .
ITALIAN JOURNAL OF ANIMAL SCIENCE, 2023, 22 (01) :59-75
[40]   Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis [J].
Marumo, Joyce L. ;
LaPierre, P. Andrew ;
Van Amburgh, Michael E. .
ANIMALS, 2023, 13 (08)