中红外光谱分析技术在奶牛甲烷排放预测中的应用研究进展

doi:10.3864/j.issn.0578-1752.2025.09.014

Abstract

Abstract:

The emission of greenhouse gases not only leads to global warming and changes in the climate system but also may cause damage to the ozone layer, thereby exacerbating the greenhouse effect. Although the total emissions of methane gas are not as high as carbon dioxide gas, and its residence time in the atmosphere is relatively short, it possesses a higher global warming potential, making it a highly threatening greenhouse gas. Livestock farming is a major source of anthropogenic methane gas emissions, with the methane emissions from cows accounting for nearly one-fifth of the total proportion. Given this context, the measurement of individual cow methane emissions becomes crucial. Understanding the methane emissions produced by each cow can help identify cows with high emissions and implement more targeted measures to reduce emissions. Therefore, there is a need for convenient high-throughput technologies for measuring cow methane emissions. Traditional methane measurement techniques, such as respiration chambers and sulfur hexafluoride gas tracing technology, are time-consuming, labor-intensive, and costly, which hinders the monitoring of methane emissions on a large scale for individual cows. Using a combination of cow trait indicators to predict the methane emission characteristics of cows is a feasible alternative method. Numerous methane prediction equations based on factors, such as cow energy intake, dry matter intake, and daily feed composition, have been developed. However, these prediction factors are also challenging to collect on commercial farms, limiting the feasibility of these equations for large-scale applications. Considering that mid-infrared spectroscopic information of cow milk can be obtained in bulk and at low cost from routine cow production performance assessments, foreign researchers have been exploring the feasibility of predicting cow methane emissions based on mid-infrared spectroscopic information from cow milk over the past decade. It has been confirmed that using mid-infrared spectroscopy to predict cow methane emissions is feasible, biologically plausible, and moderately accurate. However, the researchers in this area have not yet begun in China. This paper elaborated on the current research status of predicting cow methane emissions using mid-infrared spectroscopic information from cow milk and emphasized the key points and challenges that need to be addressed in future research. It summarized the different strategies adopted by various studies in terms of cow methane emission measurement indicators, methane phenotype observation value determination methods, mid-infrared spectroscopy data collection, modeling methods, and validation strategies, aiming to provide insights for Chinese researchers conducting related studies.

Key words: dairy cattle, mid infrared spectroscopy, methane emissions, prediction model

YANG GuoChang, ZHENG Yue, BAO XiangNan, DAI YingChun, WANG JinGang, BAI XueFeng, SUN Wei, LI XiHe, ZHANG ShuJun. Research Progress on the Application of Mid-Infrared Spectroscopy Analysis Technology in Predicting Methane Emissions from Cows[J].Scientia Agricultura Sinica, 2025, 58(9): 1856-1866.

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Figures/Tables 1

Table 1

Summary of research on predicting methane emission based on Mid Infrared Spectroscopy"

参考文献 Reference	样本量 Sample size	甲烷排放量测定方法 CH₄emission measurement method	测定目标 Predicting target	预测因素 Predictive factor	特征区段 Characteristic band	预处理Preprocessing	模型 Model	结果 Result (optimal)
[36]	59—77	SF₆	MEP(g·d^-1) MEI(g·kg^-1 milk)	MIRS (AMS/WAMS)	972—1589 cm^-1, 1720—1782 cm^-1, 2746—2970 cm^-1	no/1D	PLS	R²_cv=0.79, RPD=2.19, SE_cv=5.14 g·kg^-1 milk
[36,38]	446	SF₆	MEP(g·d^-1)	MIRS(WAMS), DIM	968—1577 cm^-1, 1720—1809 cm^-1, 2561—2966 cm^-1	1D and DIM-LP	PLS	R²_cv=0.67, SE_cv=73 g·d^-1,
[45]	532	SF₆	MEP(g·d^-1)	MIRS(WAMS), DIM	968—1577 cm^-1, 1720—1809 cm^-1, 2561—2966 cm^-1	1D and DIM-LP	M-PLS	R²_cv=0.70, SE_cv=70 g·d^-1,
[39]	3623 MRCD; 2202 MEP	Sniffer+ CO₂	MCDR(CH₄:CO₂) MEP(L·d^-1)	MIRS(AMS), MY,H, DIM, P, S	1000—1550 cm^-1, 1705—1820 cm^-1, 2700—2955 cm^-1	no/SG-1D	PLS	R²_v=0.39, RMSE_p= 94 L·d^-1
[49]	218	RC	MEP(g·d^-1) MEY(g·kg^-1 DMI) MEI(g·kg^-1 FPCM)	MIRS	964—1581 cm^-1, 1715—1773 cm^-1, 2814—2968 cm^-1	SG-1D	PLS	RPD=1.39, R²_cv=0.49, RMSE_cv=12.8%
[46]	1150	MFA	MEP(g·d^-1) MEY(g·kg^-1 DMI) MEI(g·kg^-1 FPCM， g·kg^-1 CYCURD， g·kg^-1 CYSOLIDS)	MIRS	930—5000 cm^-1	no	BayesB	R²_cv=0.57, RMSE_cv=1.17 g·kg^-1 FPCM
[51]	584	RC	MEP(g·d^-1)	MIRS(WAMS) DIM	968—1577 cm^-1, 1720—1809 cm^-1, 2561—2966 cm^-1	1D and DIM—LP	MPLS	R²_cv=0.57, SE_cv=47 g·d^-1
[48]	801	Sniffer	MC(mg·m^-3)	MIRS	925—1584 cm^-1, 1719—1784 cm^-1, 2652—2976 cm^-1or 1623—1670 cm^-1, 3166—3254 cm^-1, 3285—3463 cm^-1, 3547—3659 cm^-1	no	PLS	R²_v=0.49, RMSE_p=0.192
[40]	1089	RC+SF₆	MEP(g·d^-1)	MIRS(WAMS), DIM,MY,P,B	968—1577 cm^-1, 1720—1809 cm^-1, 2561—2966 cm^-1	1D and DIM-LP	M-PLS	R²_cv=0. 68, SE_cv=57 g·d^-1
[41]	151	GF	MEP(g·d^-1) MEY(g·kg^-1 DMI) MEI(g·kg^-1 FPCM)	MIRS(AMS), DMI, MY, P	965—1570 cm^-1, 1702—1817 cm^-1, 2557—2998 cm^-1	SG-1D	PLS	RPD=1.7, R²_v=0.66, RMSE_v=4.7 g·kg^-1 FPCM
[42]	129	GF	MEP(g·d^-1) MEY(g·kg^-1 DMI) MEI(g·kg^-1 milk， g·kg^-1 FPCM)	MIRS(MS/AMS), DIM, MY, P, FPCM	968—1577 cm^-1, 1720—1809 cm^-1, 2561—2966 cm^-1	DIM-LP; no or 1D- SNV-DMT	M-PLS	R²_v=0.9, SE_v=2.25 g·kg^-1 FPCM
[47]	181	Sniffer+GF	MEP(g·d^-1)	MIRS(AMS), AC, SC, DIM, C, P, MY, FY, PY	1000—1550 cm^-1, 1705—1820 cm^-1, 2700—2955 cm^-1	MSC and SG-1D	PLS/BRANN/ LMANN/ SCGANN,	R_cv=0.704, RMSE_cv=70.83, RPD_cv=1.43
[43]	277	GF	MEP(g·d^-1)	MIRS(MS/JMS/ WAMS), DIM, MY, FY, PY	930—1600 cm^-1, 1710—2990 cm^-1, 3690—3822 cm^-1	no	PLS/NN	R=0.71, RPIQ=195

Table 1

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