Relationships between methane production and milk fatty acid profiles in dairy cattle

Dijkstra, J.; Zijderveld, S.M. van; Apajalahti, Juha; Bannink, A.; Gerrits, W.J.J.; Newbold, J.R.; Perdok, H.B.; Berends, H.

doi:10.1016/j.anifeedsci.2011.04.042

Cited by 121 publications

(164 citation statements)

References 19 publications

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“…While some of the Grainger data were included in the present analysis, the current dairy dataset excludes all data where the concentrate proportion of the diet was over 30% and also includes data obtained since 2007 . The dairy MY is a little below that of 23.1 g CH 4 /kg DMI reported by Dijkstra et al (2011) for dairy cows in The Netherlands and slightly higher than the 19.1 g CH 4 /kg DMI reported by Hristov et al (2013aHristov et al ( , 2013b. The analysis by Hristov et al (2013aHristov et al ( , 2013b included high-concentrate diets and this probably contributed to the slightly lower MY than in our analysis.…”

Section: Methane Yieldcontrasting

confidence: 54%

“…Hristov et al (2013aHristov et al ( , 2013b) also demonstrated a simple relationship between MP and DMI in a meta-analysis of dairy data that included DMI over a range similar to that in the current analysis (MP (g/day) = 19.14 · DMI + 2.54). Similarly, Dijkstra et al (2011) reported that methane yield (MY, g CH 4 /kg DMI) for dairy cows in The Netherlands was 23.1, suggesting a linear relationship between MP and DMI, with an intercept of zero. In contrast, a curvilinear relationship between MP and DMI was developed by Blaxter and Clapperton (1965), and subsequently corrected by Wilkerson et al (1995).…”

Section: Methane and Dmimentioning

confidence: 86%

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A universal equation to predict methane production of forage-fed cattle in Australia

Charmley

Williams²,

Moate³

et al. 2016

Anim. Prod. Sci.

205

138

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Abstract. The methods for estimating methane emissions from cattle as used in the Australian national inventory are based on older data that have now been superseded by a large amount of more recent data. Recent data suggested that the current inventory emissions estimates can be improved. To address this issue, a total of 1034 individual animal records of daily methane production (MP) was used to reassess the relationship between MP and each of dry matter intake (DMI) and gross energy intake (GEI). Data were restricted to trials conducted in the past 10 years using open-circuit respiration chambers, with cattle fed forage-based diets (forage >70%). Results from diets considered to inhibit methanogenesis were omitted from the dataset. Records were obtained from dairy cattle fed temperate forages (220 records), beef cattle fed temperate forages (680 records) and beef cattle fed tropical forages (133 records). Relationships were very similar for all three production categories and single relationships for MP on a DMI or GEI basis were proposed for national inventory purposes. These relationships were MP (g/day) = 20.7 (AE0.28) · DMI (kg/day) (R 2 = 0.92, P < 0.001) and MP (MJ/day) = 0.063 (AE0.008) · GEI (MJ/day) (R 2 = 0.93, P < 0.001). If the revised MP (g/day) approach is used to calculate Australia's national inventory, it will reduce estimates of emissions of forage-fed cattle by 24%. Assuming a global warming potential of 25 for methane, this represents a 12.6 Mt CO 2 -e reduction in calculated annual emissions from Australian cattle.

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Section: Methane Yieldcontrasting

confidence: 54%

Section: Methane and Dmimentioning

confidence: 86%

A universal equation to predict methane production of forage-fed cattle in Australia

Charmley

Williams²,

Moate³

et al. 2016

Anim. Prod. Sci.

205

138

View full text Add to dashboard Cite

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“…Furthermore, this coefficient for dairy cows is less than the 23.1 g CH 4 /kg DMI (Fig. 4) reported by Dijkstra et al (2011) for dairy cows in The Netherlands, but more than the coefficient of 19.4 for diets based on a mixture of maize silage, maize grain and soybeans, as is typically used in the USA .…”

Section: Modelling Enteric Methane Productionmentioning

confidence: 66%

“…In Europe, research has focussed on the possibility of using the concentrations of specific fatty acids in milk as predictors of methane emissions from dairy cows (Chilliard et al 2009;Dijkstra et al 2011). Furthermore, the fatty acid composition of milk fat can influence the mid-infrared spectra of milk and researchers in Belgium have related the midinfrared spectrum of milk from individual cows to their methane emissions (Dehareng et al 2012;Vanlierde et al 2013).…”

Section: Proxy Measures Of Methane Emissionsmentioning

confidence: 99%

Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions

Moate¹,

Deighton²,

Williams³

et al. 2016

Anim. Prod. Sci.

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Abstract. This review examines research aimed at reducing enteric methane emissions from the Australian dairy industry. Calorimeter measurements of 220 forage-fed cows indicate an average methane yield of 21.1 g methane (CH 4 )/kg dry matter intake. Adoption of this empirical methane yield, rather than the equation currently used in the Australian greenhouse gas inventory, would reduce the methane emissions attributed to the Australian dairy industry by~10%. Research also indicates that dietary lipid supplements and feeding high amounts of wheat substantially reduce methane emissions. It is estimated that, in 1980, the Australian dairy industry produced~185 000 t of enteric methane and total enteric methane intensity was 33.6 g CH 4 /kg milk. In 2010, the estimated production of enteric methane was 182 000 t, but total enteric methane intensity had declined~40% to 19.9 g CH 4 /kg milk. This remarkable decline in methane intensity and the resultant improvement in the carbon footprint of Australian milk production was mainly achieved by increased per-cow milk yield, brought about by the on-farm adoption of research findings related to the feeding and breeding of dairy cows. Options currently available to further reduce the carbon footprint of Australian milk production include the feeding of lipid-rich supplements such as cottonseed, brewers grains, cold-pressed canola, hominy meal and grape marc, as well as feeding of higher rates of wheat. Future technologies for further reducing methane emissions include genetic selection of cows for improved feed conversion to milk or low methane intensity, vaccines to reduce ruminal methanogens and chemical inhibitors of methanogenesis.

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“…Importantly, Chung et al (2011) reported that dietary inclusion of linseed as a source of polyunsaturated fatty acid (PUFA) was associated with decreased CH 4 output in dairy cows when fed barley silage-, but not when fed grass hay-based diets, suggesting an important interaction with forage type, which may make prediction equations only relevant in certain scenarios. Others have reported the associations between CH 4 and milk FA across experiments using different dietary fat sources with potential negative effects on ruminal microorganisms, such as PUFA and medium-chain fatty acid (MCFA), or across different forages (Castro-Montoya et al, 2011;Dijkstra et al, 2011;van Lingen et al, 2014). It is therefore possible that these associations between CH 4 and milk FA profile might not apply when diets do not include such dietary fat supplements.…”

Section: Introductionmentioning

confidence: 99%

Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources

Rico

Chouinard

Hassanat

et al. 2016

animal

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Milk fatty acid (FA) profile has been previously used as a predictor of enteric CH 4 output in dairy cows fed diets supplemented with plant oils, which can potentially impact ruminal fermentation. The objective of this study was to investigate the relationships between milk FA and enteric CH 4 emissions in lactating dairy cows fed different types of forages in the context of commonly fed diets. A total of 81 observations from three separate 3 × 3 Latin square design (32-day periods) experiments including a total of 27 lactating cows (96 ± 27 days in milk; mean ± SD) were used. Dietary forages were included at 60% of ration dry matter and were as follows: (1) 100% corn silage, (2) 100% alfalfa silage, (3) 100% barley silage, (4) 100% timothy silage, (5) 50 : 50 mix of corn and alfalfa silages, (6) 50 : 50 mix of barley and corn silages and (7) 50 : 50 mix of timothy and alfalfa silages. Enteric CH 4 output was measured using respiration chambers during 3 consecutive days. Milk was sampled during the last 7 days of each period and analyzed for components and FA profile. Test variables included dry matter intake (DMI; kg/day), NDF (%), ether extract (%), milk yield (kg/day), milk components (%) and individual milk FA (% of total FA). Candidate multivariate models were obtained using the Least Absolute Shrinkage and Selection Operator and Least-Angle Regression methods based on the Schwarz Bayesian Criterion. Data were then fitted into a random regression using the MIXED procedure including the random effects of cow, period and study. A positive correlation was observed between CH 4 and DMI (r = 0.59, P < 0.001), whereas negative associations were observed between CH 4 and cis9-17:1 (r = − 0.58, P < 0.001), and trans8, cis13-18:2 (r = − 0.51, P < 0.001). Three different candidate models were selected and the best fit candidate model predicted CH 4 with a coefficient of determination of 0.84 after correction for cow, period and study effects and was: CH 4 (g/day) = 319.7 − 57.4 × 15:0 − 13.8 × cis9-17:1 − 39.5 × trans10-18:1 − 59.9 × cis11-18:1 − 253.1 × trans8, cis12-18:2 − 642.7 × trans8, cis13-18:2 − 195.7 × trans11, cis15-18:2 + 16.5 × DMI. Overall and linear prediction biases of all models were not significant ( P > 0.19). Milk FA profile and DMI can be used to predict CH 4 emissions in dairy cows across a wide range of dietary forage sources.

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Relationships between methane production and milk fatty acid profiles in dairy cattle

Cited by 121 publications

References 19 publications

A universal equation to predict methane production of forage-fed cattle in Australia

A universal equation to predict methane production of forage-fed cattle in Australia

Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions

Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources

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