Section 20: Proxies

Introduction

Large-scale measurements of enteric CH4 emissions from dairy cows are needed for effective monitoring of strategies to reduce the carbon footprint of milk production, as well as for incorporation of CH4 emissions into breeding programs. However, measurements on a sufficiently large scale are difficult and expensive. Proxies for CH4 emissions can provide an alternative, but each approach has limitations. Negussie et al. (2019) recently showed the potential of proxies proxies that are easy to record in the farm. These proxies can be gathered in most farms and are a realistic threshold accuracy that can be obtained without more fancy proxies. Several techniques have been developed for the measurement of CH4 emissions from ruminants, with varying degrees of accuracy (see reviews by Cassandro et al., 2013 and Hammond et al., 2016A), but routine individual measurements on a large scale (a requisite for genetic selection) have proven to be difficult and expensive (Pickering et al., 2015; Negussie et al., 2016). Therefore, identifying proxies (i.e. indicators or indirect traits) that are correlated to CH4 emission, but which are easy and relatively low-cost to record on a large scale, is a much needed alternative. Proxies might be less accurate, but could be measured repeatedly to reduce random noise. The (potential) proxies range from simple and low-cost measurements such as body weight, to high-throughput milk MIR, to more demanding measures like rumen morphology, rumen metabolites or microbiome profiling.

Combining proxies that are easy to measure and cheap to record could provide predictions of CH4 emissions that are sufficiently accurate for selection and management of cows with low CH4 emissions.

Available Proxies

A large array of CH4 proxies differing widely in accuracy and applicability under different conditions have been reported. The ideal proxy would be highly phenotypically and genetically correlated with CH4 emissions and could easily, and potentially repeatedly, be measured on a large scale. A systematic summary and assessment of existing knowledge is needed for the identification of robust and accurate CH4 proxies for future use. Table 5 summarizes proxies for CH4 production, and Table 6 summarizes results from combining proxies to improve predictability of proxies for CH4 prediction.

Proxy	Description / conclusion	Reference
(1) Feed intake and feeding behavior
Dry Matter Intake (DMI)	DMI predict MeP with R2= 0.06-0.64, and ME intake predict MeP with R2= 0.53-0,55	Ellis et al. (2007); Mills et al. (2003); Negussie et al. (2019)
Gross Energy intake (GE)	Predict MeP with RMSPE= 3.01.	Moreas et al. (2014)
Feeding behavior	Magnitude and direction of relation to MeP varies across studies	Nkrumah et al. (2006); Jonker et al., 2014
Rumination time	High rumination relates to more milk, consume more concentrate and produce more CH4, lower RMP and MeI	Watt et al. (2015); López- Paredes et al. (2020)
Rumen microbiome	The metagenome can predict DMI, and classify high vs low intakes	Delgado et al. (2019)
(2) Rumen function, metabolites and microbiome
Dietary antimethanogenic compounds	Inhibitors of the enzyme methyl coenzyme-M reductase: bromochoromethane; chloroform; 3- nitrooxypropanol (not always)	Denman et al., 2007; Knight et al., 2011; Haisan et al., 2014; Romero-Perez et al., 2014, 2015
Dietary antimicrobial compounds	Induce reductions in both MeP and methanogens numbers: nitrates, anacardic acid (cashew nut shell liquid), monensin, isobutyrate	Iwamoto et al., 2002; Kubo et al., 1993; van Zijderveld et al., 2010; Veneman et al., 2015; Shinkai et al., 2012; Wang et al., 2015
Rumen microbiome profile	High Fibrobacteres, Quinella ovalis and Veillonellaceae and low Ruminococcaceae, Lachnospiraceae and Clostridiales associate with low- CH4 phenotypes and high propionate Protozoa concentration	Kittelmann et al., 2014; Wallace et al., 2014; Sun et al., 2015; Guyader et al., 2014
Rumen microbiome profile	Predict MeP with R2 up to 0.55	Ross et al. 2013a; Ross et al. (2013b)
Microbial genes	20 (out of 3970 identified) related to CH4 emissions	Roehe et al. (2016)
Rumen volume (Xray Computed Tomography) and retention time	Low-MeY sheep had smaller rumens. Faster passage= less time to ferment substrate - explained 28% of variation in MeP	Pinares Patiño et al., 2003; Goopy et al., 2014; Okine et al. (1989)
Blood triiodothyronine concentration	Reduced MeY	Barnett et al. (2012)
Acetate to propionate ratio in ruminal fluid	Positively associated with CH4 emissions, but not confirmed in all studies, sometimes opposite relation	Mohammed et al., 2011; Fievez et al., 2012; Chung et al., 2011; Van Zijderveld et al., 2010
(3) Milk production and composition
Modelling approach	Doubling milk production only adds 5 kg to the MeP and so greatly reduces MeY	Kirchgessner et al. (1995); Hristov et al. (2014)
Milk fat content	key explanatory variable for predicting CH4: A moderate negative genetic correlation with infrared predicted MeI: correlations MeP = 0,08 and MeI = - 0.13	Moreas et al. (2014); Kandel et al., 2014A, B; Vanlierde et al. (2015)
Milk fat content	A positive relationship between VFA proportions and methanogenesis is expected as a consequence of the common biochemical pathways; Dietary unsaturated fatty acids are negatively associated with CH4 emissions	Vlaeminck et al., 2006; Van Lingen et al., 2014
Milk protein yield	Correlation with Mel = - 0.47 or -0.09, MeP = 0.53	Kandel et al. (2014); Vanlierde et al. (2015)
Lactose	Variable correlations: MeP = 0,33; MeI = - 0.21; R = 0.19 for CH4 emission	Miettinen and Huhtanen (1996); Dehareng et al. (2012)
Somatic cell score	Genetic correlation with infrared predicted MeI: R = 0.07	Kandel et al. (2014A, B)
Prediction equations Milk FA and CH4 emissions, including from MIR data	R² ranged between 47 and 95%; relationships between the individual milk FA and MeP differed considerably and the correlations between CH4 and milk FA vary throughout the lactation	Chilliard et al. (2009); Delfosse et al. (2010); Castro-Montoya et al. (2011); Dijkstra et al. (2011); Kandel et al. (2013); Mohammed et al. (2011); Van Lingen et al. (2014); Williams et al. (2014); Dijkstra et al. (2016); Rico et al. (2016); Van Gastelen and Dijkstra (2016); Vanrobays et al. (2016); Bougoin et al., (2019)
(4) Hind-gut and feces
Whole tract digestibility (potential as supporting factors in the prediction of enteric CH4 emissions)	Main effects relate to rumen (see above), but energy digestibility as a supporting factor to GE intake improved the accuracy of CH4 prediction, despite the fact that there was no direct linear relationship between energy digestibility and MeY and in % of GE intake	Yan et al., 2009 C
Ratio of acetic and butyric acid divided by propionic acid	Methane yield positive relation	Moss et al., 2000
(5) Whole animal measurements
Body weight and conformation	Prediction models; primary predictor for enteric MeP	Moraes et al. (2014); Holter and Young, 1992; Yan et al., 2009
Body weight	Relationship with MeI: r = 0.44; relationship between body weight and rumen capacity	Antunes-Fernandes et al. (2016); Demment and Van Soest, 1985
Body weight	Key explanatory variable for enteric MeP	No reference available
Conformation traits: affects enteric MeP	Indicators for rumen volume (via feed intake and rumen passage rates); BCS	Agnew and Yan, 2000
Lactation stage	Complementary proxy	Vanlierde et al. (2015)

Table 5. Available methane proxies include: (1) feed intake and feeding behaviour, (2) rumen function, metabolites and microbiome, (3) milk production and composition, (4) hind-gut and faeces, and (5) measurements at the level of the whole animal. It is evident that no single proxy offers a good solution in terms of all of these attributes, though the low cost and high throughput make milk MIR a good candidate for further work on refining methods, improving calibrations and exploring combinations with other proxies.