Letter to the Editor: A response to

We thank the journal for this opportunity to respond to the issues raised by P. Huhtanen and A. N. Hristov in their letter to the editor (Huhtanen and Hristov, 2018Huhtanen P. Hristov A.N. Letter to the Editor: Challenging one sensor method for screening dairy cows for reduced methane emissions.J. Dairy Sci. 2018; 101: 9619-962010.3168/jds.2018-14704Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). We hope this response will resolve a persistent misunderstanding that is also reflected in the statement by Hammond et al., 2016Hammond K.J. Crompton L.A. Bannink A. Dijkstra J. Yáñez-Ruiz D.R. O'Kiely P. Kebreab E. Eugène M.A. Yu Z. Shingfield K.J. Schwarm A. Hristov A.N. Reynolds C.K. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants.Anim. Feed Sci. Technol. 2016; 219: 13-30Crossref Scopus (90) Google Scholar: “… the need for high throughput methodology, e.g. for screening large numbers of animals for genomic studies, does not in itself justify the use of methods that are inaccurate, imprecise, or biased.” In this response, we will clarify that even if measurements are inaccurate, imprecise, or biased, they might provide valuable information for selective breeding. In a discussion on the use of the so-called sniffer method to obtain phenotypes for selective breeding to reduce methane emission of dairy cows, 3 effects should be distinguished: systematic environmental effects, random errors, and systematic errors. Herein, we will discuss the effects of these 3 aspects. One of the issues raised by Huhtanen and Hristov, 2018Huhtanen P. Hristov A.N. Letter to the Editor: Challenging one sensor method for screening dairy cows for reduced methane emissions.J. Dairy Sci. 2018; 101: 9619-962010.3168/jds.2018-14704Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar is that measurements of methane by using the sniffer method are significantly influenced by external conditions. Indeed, the effects of specific conditions during the day of measurement, farm, automatic milking system, and sensor explained 56% of the total variation in our data (van Engelen et al., 2018van Engelen S. Bovenhuis H. van der Tol P.P.J. Visker M.H.P.W. Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems.J. Dairy Sci. 2018; 101 (29331462): 2226-2234Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar). Consequently, we believe that the setup of the sniffer method as used in our experiments is not suited for ranking herds, for example, or for assessing the effect of diets on methane emission. However, this does not imply that sniffer measurements cannot be used for the genetic evaluation of animals for methane emission potential. A mixed model analysis in combination with the pedigree or genotypic data of cows allows for a separation of systematic environmental and genetic effects on the sniffer methane measurements (Henderson, 1984Henderson C.R. Applications of Linear Models in Animal Breeding. University of Guelph, Guelph, Ontario, Canada1984Google Scholar). Therefore, the “bias” introduced by, for example, weather conditions during the day of measurement or location of the automatic milking system in the barn is accounted for in the statistical model and not reflected in the EBV. In addition to effects of external conditions, sniffer methane measurements are affected by random errors such as those caused by differences in sampling distance and the cow's head movement (e.g., Wu et al., 2018Wu L. Groot Koerkamp P.W.G. Ogink N. Uncertainty assessment of the breath methane concentration method to determine methane production of dairy cows.J. Dairy Sci. 2018; 101 (29153523): 1554-1564Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar). These are indeed factors that will increase variation in sniffer methane measurements. However, the sniffer method allows collection of methane measurements each time a cow is being milked, and these repeated observations can be used to reduce effects of random errors. Several studies have shown that methane phenotypes derived from sniffer measurements are repeatable (Lassen et al., 2012Lassen J. Løvendahl P. Madsen J. Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows.J. Dairy Sci. 2012; 95 (22281353): 890-898Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar; Bell et al., 2014Bell M.J. Saunders N. Wilcox R.H. Homer E.M. Goodman J.R. Craigon J. Garnsworthy P.C. Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide.J. Dairy Sci. 2014; 97 (25129498): 6536-6546Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar; van Engelen et al., 2018van Engelen S. Bovenhuis H. van der Tol P.P.J. Visker M.H.P.W. Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems.J. Dairy Sci. 2018; 101 (29331462): 2226-2234Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar), demonstrating that significant differences do exist between cows. Moreover, heritabilities ranging from approximately 0.10 to 0.30 (Lassen and Løvendahl, 2016Lassen J. Løvendahl P. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods.J. Dairy Sci. 2016; 99 (26805978): 1959-1967Abstract Full Text Full Text PDF PubMed Scopus (95) Google Scholar; Pszczola et al., 2017Pszczola M. Rzewuska K. Mucha S. Strabel T. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms.J. Anim. Sci. 2017; 95 (29293701): 4813-4819Crossref PubMed Scopus (30) Google Scholar; van Engelen et al., 2018van Engelen S. Bovenhuis H. van der Tol P.P.J. Visker M.H.P.W. Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems.J. Dairy Sci. 2018; 101 (29331462): 2226-2234Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar) indicate that part of these between-cow differences are genetic in nature. These results are promising and suggest that the sniffer method provides useful data to selectively breed cows that emit less methane. A third issue is the possible existence of systematic errors due to differences between cows in, for example, behavior during milking or exhalation rate. Such systematic errors are more serious because they cannot be reduced by repeated observations or accounted for in the statistical analyses and they might be relevant for selective breeding but only if the systematic error is partly genetic. This raises questions about what is actually reflected by EBV for methane emission based on sniffer measurements. Are these only genetic differences in methane emission or do they also partly reflect genetic differences in other traits? For example, cows showing restless behavior during milking, which is partly due to genetic differences between cows, might have lower sniffer methane levels because of sampling distance. Knowledge of variation in sniffer methane measurements due to such systematic errors is limited and certainly requires further investigation, as was pointed out by Wu et al., 2018Wu L. Groot Koerkamp P.W.G. Ogink N. Uncertainty assessment of the breath methane concentration method to determine methane production of dairy cows.J. Dairy Sci. 2018; 101 (29153523): 1554-1564Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar. However, even if genetic differences in sniffer methane partly reflect differences in other traits, this does not disqualify the use of sniffer methane measurements for selective breeding. In animal breeding, we distinguish between traits we measure—sometimes referred to as index or indicator traits—and traits we would like to improve—the breeding goal traits. Sniffer methane should be considered an indicator trait for “true” methane emission, and the response to selection is a function of the genetic correlation between both. Sniffer methane, therefore, provides valuable information when the genetic correlation with true methane emission is sufficiently strong, irrespective of whether sniffer methane does or does not take biologically realistic values. To illustrate, heart girth measurements can be used as an indicator for the breeding goal trait BW (Koenen and Groen, 1998Koenen E.P.C. Groen A.F. Genetic evaluation of body weight of lactating Holstein heifers using body measurements and conformation traits.J. Dairy Sci. 1998; 81: 1709-1713Abstract Full Text PDF PubMed Scopus (52) Google Scholar), even though both traits are expressed on completely different scales. We acknowledge that there remain important outstanding issues that need to be resolved before selection on sniffer methane can be implemented. First, we need an estimate of the genetic correlation between sniffer methane and true methane emission. Global data sets on enteric methane emission, largely based on climate respiration chambers, such as described by Niu et al., 2018Niu M. Kebreab E. Hristov A.N. Oh J. Arndt C. Bannink A. Bayat A.R. Brito A.F. Boland T. Casper D. Crompton L.A. Dijkstra J. Eugène M.A. Garnsworthy P.C. Haque M.N. Hellwing A.L.F. Huhtanen P. Kreuzer M. Kuhla B. Lund P. Madsen J. Martin C. McClelland S.C. McGee M. Moate P.J. Muetzel S. Muñoz C. O'Kiely P. Peiren N. Reynolds C.K. Schwarm A. Shingfield K.J. Storlien T.M. Weisbjerg M.R. Yáñez-Ruiz D.R. Yu Z. Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database.Glob. Chang. Biol. 2018; 24 (29450980): 3368-3389Crossref PubMed Scopus (126) Google Scholar, could play an important role in estimating this genetic correlation, provided that pedigree information or DNA samples of the cows are available. Second, breeding goals for dairy cattle consist of many different traits; therefore, relationships between methane emission and other breeding goal traits, such as milk production, health, fertility, and feed intake, need to be estimated to avoid undesired correlated responses. Furthermore, all breeding programs require monitoring for possible negative effects on traits not routinely recorded. Therefore, as pointed out by Huhtanen and Hristov, 2018Huhtanen P. Hristov A.N. Letter to the Editor: Challenging one sensor method for screening dairy cows for reduced methane emissions.J. Dairy Sci. 2018; 101: 9619-962010.3168/jds.2018-14704Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar, selection for reduced methane emission requires monitoring for traits such as digesta passage rate or cell wall digestibility. When given the choice, accurate and unbiased measurements are preferred. However, such measurements are seldom available on a large scale and at reasonable cost. We agree with Huhtanen and Hristov, 2018Huhtanen P. Hristov A.N. Letter to the Editor: Challenging one sensor method for screening dairy cows for reduced methane emissions.J. Dairy Sci. 2018; 101: 9619-962010.3168/jds.2018-14704Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar that the current state-of-the-art methane measurements using the sniffer method are “noisy” and biased by environmental conditions. However, inaccurate and biased sniffer methane phenotypes do not automatically imply inaccurate and biased methane breeding values. Therefore, the disqualification of sniffer methane for selective breeding by Huhtanen and Hristov, 2018Huhtanen P. Hristov A.N. Letter to the Editor: Challenging one sensor method for screening dairy cows for reduced methane emissions.J. Dairy Sci. 2018; 101: 9619-962010.3168/jds.2018-14704Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar is premature and not supported by facts. Letter to the Editor: Challenging one sensor method for screening dairy cows for reduced methane emissionsJournal of Dairy ScienceVol. 101Issue 11PreviewSelective breeding is suggested as a mitigation strategy to reduce enteric methane (CH4) emission by dairy cows. Ranking of animals for genetic selection for low CH4 emitting potential requires reliable and cost-effective measurements from a large number of animals in farm conditions. As an international group of scientists pointed out “… the need for high throughput methodology, e.g. for screening large numbers of animals for genomic studies, does not in itself justify the use of methods that are inaccurate, imprecise, or biased” (Hammond et al., 2016). Full-Text PDF Open Archive