Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions

Figures & data

Table 1. Summary of recent studies on carbon footprint (CF) and milk production in dairy cattle.

Estimated emission (kg CO2e/kg FPCM)	Production systems	Major emission contributors or hotspots			Approach	Country	References
		1st	2nd	3rd
1.23	Intensive	Enteric fermentation (46%)	Manure management (32.7%)	NA	LCA	South Dakota	23
1.13 1.24 1.52	Grazing Mixed and housed	Enteric fermentation (32–51%)	Concentrate production (housed: 29%, mixed: 24%, grazing: 13%)	Manure management (housed: 19%, mixed: 9, grazing: 5%)	LCA	Europe	24
1.45–1.81	Smallholder dairy farms	Enteric fermentation (66.8%)	Feed production (23%)	Manure management (<4.50%)	LCA	India	25
2.26	Silvo-pastoral	Enteric fermentation	Manure management	Land use and energy/transport	LCA	Peruvian Amazon	26
2.35	Smallholder dairy farms	Enteric fermentation (67%)	Manure management (16%)	NA	LCA	Ethiopia	27
0.99	Average Dutch dairy system	Enteric methane	Roughage production	Purchased resource	LCA	NL	28
2.11	Intensive	Enteric fermentation	Manure management	Feed production	Syst. Rev	Range of countries	29
0.75–0.81	Grazed pasture dairy system	Enteric CH4 from dairy cows (85–86%)	Enteric CH4 from replacement animals (12–13%)	Manure management (1–2%)	LCA	NZ	30
0.44–1.73	Intensive	Enteric fermentation (44%)	Feed production (36%)	NA	LCA	Canada	31
2.19 and 3.13	Smallholder	Enteric fermentation (55.5%)	Feed production and transport (31.6%)	Manure management (12.6%)	LCA	Kenya	32
1.75	Large-scale	Enteric fermentation and manure (58%)	Transport and processing of wheat bran (20%)	Cultivation of roughages (15%)	LCA	Ethiopia	33
2.25	Peri-urban	Enteric fermentation and manure (47%)	Transport and processing of wheat bran (18%)	Cultivation of roughages (19%)	LCA	Ethiopia	33
2.22	Rural farms	Enteric fermentation and manure (64%)	Transport and processing of wheat bran (9%)	Cultivation of roughages (22%)	LCA	Ethiopia	33
1.39	Pasture-based	Enteric fermentation	Manure management	Electricity	LCA	South Africa	34
0.89	Pasture-based	Enteric fermentation	Concentrate feed production	Fertilization	LCA	Portugal	35
0.39–1.35	Commercial	Enteric fermentation (39–60%)	Manure management (29–58%)	NA	LCA	Australia	36
1.34	Intensive	Feed production	Manure management	NA	LCA	China	37
1.12	Intensive	Raw milk production (75.27%)	Dairy processing (15.45%)	Packaging disposal (5.89)	LCA	China	38
1.11	Intensive	Enteric fermentation (57%)	Manure management (10%)	Concentrate feed production (8%)	LCA	Australia	39

LCA: life cycle assessment; NA: not available; NL: Netherland; NZ: New-Zealand; Syst. Rev: systematic review.

Table 2. Summary of recent studies on estimates of heritability for methane emission traits in dairy cattle.

Trait	Breed	Number of animals	Heritability (h^2 ± SE)	References
MeY	HF	287	0.27 ± 0.12	17
	NA	NA	0.24 ± 0.04	44
MeP	HF	451	0.36 ± 0.12	45
	HF	330	0.16 ± 0.10	17
	HF	575	0.12 ± 0.03	19
	NA	NA	0.21 ± 0.01	44
	HF	1501	0.12 ± 0.04	46
MeC	HF	4664	0.17 ± 0.04	47
	HF	647	0.15 ± 0.03	19
	HF	1501	0.11 ± 0.03	46
	HF	337	0.12 ± 0.01	48
MeIc	HF	1956	0.04 ± 0.03	19
	NA	NA	0.18 ± 0.01	44
MeIp	HF	1839	0.04 ± 0.03	19
MeI	HF	265	0.21 ± 0.14	17
PME	Walloon dairy cows	229,465	0.14 ± 0.05*	49
LMI	Walloon dairy cows	229,465	0.24 ± 0.05*	49
CH4/CO2	HF	182	0.12 ± 0.14	50
RT	HF	363	0.45 ± 0.14	45
	HF	1486	0.14 ± 0.03	51
	HF	1501	0.17 ± 0.06	46

MeY: methane yield; MeP: methane production; MeC: methane concentration; MeIc: methane intensity calculated using MeC; MeIp: methane intensity calculated using MeP; MeI: methane intensity; PME: predicted daily methane emission; LMI: log-transformed predicted CH₄ intensity; RT: rumination time; HF: Holstein Friesian. NA stands that the study did not specify the breed used in the meta-analysis, nor did it provide specific breed information and number of animals involved.

*Indicates that values are in standard deviation.

Figure 1. An overview of animal feed, rumen microbiota and enteric methane emissions in dairy cattle.

Table 3. Candidate genes and SNPs in significant regions associated with various methane emission traits in dairy cattle.

Ch	Candidate genes/SNPs	Position (bp)	Type of study	Traits associated with	Breed	Number of genotyped animals	Country	References
1	ARS-BFGL-NGS-93180	138,832,098	GWAS	RMETc, MeY	HF	1962	Denmark	19
4	4: 115,131,249	115,131,249	GWAS	PME	HF	150	Iran	11
	ARS-BFGL-NGS-24888	3,583,133	GWAS	MeC, MeP	HF	1962	Denmark	19
	Hapmap59221-rs29014908	35,939,871		MeIc, MeIp
	Hapmap44201-BTA-114510	36,842,170		MeIc, MeIp
	CYP51A1	9,306,414–9,323,252	GWAS	MeP	HF	287	Poland	7
5	5: 16,795,260	16,795,260	GWAS	Valeric acid	HF	150	Iran	11
6	Hapmap51046-BTA-75812	61,984,747	GWAS	MeC, MeP	HF	1962	Denmark	19
	Hapmap52436-rs29009653	99,732,094		MeIc, MeIp
11	ARS-BFGL-NGS-47330	44,562,022	GWAS	MeC, MeP	HF	1962	Denmark	19
	ARS-BFGL-NGS-12929	64,313,748		MeC, MeP
	Hapmap26463-BTA-159947	92,086,008		MeC, MeP
13	13: 81,673,732	81,673,732	GWAS	PME	HF	150	Iran	11
	Hapmap49571-BTA-32781	47,583,553	GWAS	MeC, MeP	HF	1962	Denmark	19
	BTB-00525367	47,915,618		MeC, MeP
	ARS-BFGL-NGS-70206	48,622,655		MeC, MeP
	BTA-115847-no-rs	48,826,815		MeC, MeP
	PPP1R16B	68,258,627–68,366,080	GWAS	MeP	HF	287	Poland	7
	SLC23A2	47,642,260	GWAS	MeP	Bos taurus, Bos indicus, and crosses	280	Mexico	18
14	14: 62,204,044	62,204,044	GWAS	MeP	Bos taurus, Bos indicus, and crosses	280	Mexico	18
	LY6D, GML, CYHR1, PPP1R16A, ARHGAP39, ZNF7, OPLAH, MAF1, and SPATC1	1.86–2.12 Mb 1.48–1.68 Mb	GWAS	PME LMI	Walloon-dairy cows	7381	Belgium	49
	TRPS1	48,890,778, 48,858,864, 48,726,666, 48,679,326	GWAS	CH4 ppm /d	HF	483	Poland	97
15	15: 25,797,132	25,797,132	GWAS	PME	HF	150	Iran	11
	BTA-37116-no-rs	57,228,610	GWAS	MeC, MeP	HF	1962	Denmark	19
18	ARS-BFGL-NGS-32691	34,159,637	GWAS	MeC, MeP	HF	1962	Denmark	19
	ARS-BFGL-NGS-54767	7,605,307		MeIc, MeIp
19	UA-IFASA-7562	49,438,164	GWAS	MeC, MeP	HF	1962	Denmark	19
	19: 24,494,923	24,494,923	GWAS	PME	HF	150	Iran	11
	RAI14	47,747,001	GWAS	MeP	Bos taurus, Bos indicus, and crosses	280	Mexico	18
24	ARS-BFGL-NGS-103202	61,455,723	GWAS	MeYc, MeYp	HF	1962	Denmark	19
25	NTHL1	1,590,252–1,595,934	GWAS	MeP	HF	287	Poland	7
	TSC2	1,596,730–1,626,967		MeP
	PKD1	1,627,978–1,666,088		MeP
	25: 37,967,076	37,967,076	GWAS	Valeric acid	HF	150	Iran	11
26	Hapmap33073-BTA-162864	21,180,893	GWAS	MeC, MeP	HF	1962	Denmark	19
	ARS-BFGL-NGS-2180	24,477,962		MeC, MeP
	ARS-BFGL-NGS-1092	24,531,763		MeC, MeP
	ARS-BFGL-NGS-18194	24,575,207		MeC, MeP
	ARS-BFGL-NGS-81009	26,491,674		MeC, MeP
	Hapmap38478-BTA-20824	28,723,721		MeC, MeP
	Hapmap40449-BTA-61103	31,213,256		MeC, MeP
27	Hapmap19519-rs29022379	19,017,466	GWAS	MeIp, MeYc	HF	1962	Denmark	19
28	28: 21,771,233	21,771,233	GWAS	PME	HF	150	Iran	11
	ARS-BFGL-NGS-60192	25,609,489	GWAS	MeC, MeP	HF	1962	Denmark	19
29	ARS-BFGL-NGS-24205	25,325,889	GWAS	MeC, MeP	HF	1962	Denmark	19
	ABS4 and DNAJC10	–	Metha-genome	Rumen microbiota composition	HF	690	Denmark	98

Ch: chromosome; RMETc: residual methane based on methane concentration (MeC); MeY: methane yield; PME: predicted daily methane emissions; MeC: methane concentration; MeP: methane production; MeIc: methane intensity based on MeC; MeIp: methane intensity based on MeP; LMI: log-transformed predicted CH₄ intensity.

Figure 2. Brief overview of integrating omics data collection and selection with genomic best linear unbased prediction (GBLUP) and microbiome best linear unbiased prediction (MBLUP) methods, and holistic breeding strategies to achieve optimal genetic progress for low methane emission in dairy cattle.

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