Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows

Abstract To measure methane (CH4) emissions from cattle on-farm, a number of methods have been developed. Combining measurements made with different methods in one data set could lead to an increased power of further analyses. Before combining the measurements, their agreement must be evaluated. We analysed data obtained with a handheld laser methane detector (LMD) and the GreenFeed system (GF), as well as data obtained with LMD and Fourier Transformed Infrared (FTIR) and Non-dispersive Infrared (NDIR) breath analysers (sniffers) installed in the feed bin of automatic milking systems. These devices record short-term breath CH4 concentrations from cows and make it possible to estimate daily CH4 production in g/d which is used for national CH4 emission inventories and genetic studies. The CH4 is released by cows during eructation and breathing events, resulting in peaks of CH4 concentrations during a measurement which represent the respiratory cycle. For LMD, the average CH4 concentration of all peaks during the measurement (P_MEAN in ppm × meter) was compared with the average daily CH4 production (g/d) measured by GF on 11 cows. The comparison showed a low concordance correlation coefficient (CCC; 0.02) and coefficient of individual agreement (CIA; 0.06) between the methods. The repeated measures correlation (rp) of LMD and GF, which can be seen as a proxy for the genetic correlation, was, however, relatively strong (0.66). Next, based on GF, a prediction equation for estimating CH4 in g/d (LMD_cal) using LMD measurements was developed. LMD_cal showed an improved agreement with GF (CCC = 0.22, CIA = 0.99, rp = 0.74). This prediction equation was used to compare repeated LMD measurements (LMD_val in g/d) with CH4 (g/d) measured with FTIR (n = 34 cows; Data Set A) or NDIR (n = 39 cows; Data Set B) sniffer. A low CCC (A: 0.28; B: 0.17), high CIA (A: 0.91; B: 0.87) and strong rp (A: 0.57; B: 0.60) indicated that there was some agreement and a minimal re-ranking of the cows between sniffer and LMD. Possible sources of disagreement were cow activity (LMD: standing idle; sniffer: eating and being milked) and the larger influence of wind speed on LMD measurement. The LMD measurement was less repeatable (0.14–0.27) than the other techniques studied (0.47–0.77). Nevertheless, GF, LMD and the sniffers ranked the cows similarly. The LMD, due to its portability and flexibility, could be used to study CH4 emissions on herd or group level, as a validation tool, or to strengthen estimates of genetic relationships between small-scale research populations.

[1]  M. Lidauer,et al.  Non-invasive individual methane measurement in dairy cows. , 2017, Animal : an international journal of animal bioscience.

[2]  G. Renand,et al.  Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision , 2016 .

[3]  M. Weisbjerg,et al.  Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants , 2010 .

[4]  E. Charmley,et al.  Comparison of open-circuit respiration chambers with a micrometeorological method for determining methane emissions from beef cattle grazing a tropical pasture , 2011 .

[5]  Anuradha Roy,et al.  An Application of Linear Mixed Effects Model to Assess the Agreement Between Two Methods With Replicated Observations , 2009, Journal of biopharmaceutical statistics.

[6]  H. Barnhart,et al.  An Overview on Assessing Agreement with Continuous Measurements , 2007, Journal of biopharmaceutical statistics.

[7]  Matthew E. Wolak,et al.  Guidelines for estimating repeatability , 2012 .

[8]  D. Falconer,et al.  Introduction to Quantitative Genetics. , 1962 .

[9]  B. Kuhla,et al.  Rapid Communication: Ranking dairy cows for methane emissions measured using respiration chamber or GreenFeed techniques during early, peak, and late lactation. , 2017, Journal of animal science.

[10]  J. McEwan,et al.  Genetic parameters for predicted methane production and laser methane detector measurements. , 2015, Journal of animal science.

[11]  A. Patra.  Recent Advances in Measurement and Dietary Mitigation of Enteric Methane Emissions in Ruminants , 2016, Front. Vet. Sci..

[12]  P. Visscher.  On the sampling variance of intraclass correlations and genetic correlations. , 1998, Genetics.

[13]  N. Nielsen,et al.  Methods for Measuring and Estimating Methane Emission from Ruminants , 2012, Animals : an open access journal from MDPI.

[14]  T. Yan,et al.  Do methane measurements from a laser detector and an indirect open-circuit respiration calorimetric , 2011 .

[15]  Merit of outliers for milk yield as indicators of accuracy of genetic evaluations of sires. , 1998, Journal of dairy science.

[16]  G. Waghorn,et al.  Measuring methane from grazing dairy cows using GreenFeed , 2016 .

[17]  D. J. Roberts,et al.  On the use of a laser methane detector in dairy cows , 2009 .

[18]  F. Kelliher,et al.  Methane emissions from dairy cows: Comparing open-path laser measurements to profile-based techniques , 2005 .

[19]  Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows. , 2016, Journal of dairy science.

[20]  G. Waghorn,et al.  The GreenFeed system for measurement of enteric methane emission from cattle , 2016 .

[21]  Frank Rosner,et al.  The agreement between two next-generation laser methane detectors and respiration chamber facilities in recording methane concentrations in the spent air produced by dairy cows , 2017, Comput. Electron. Agric..

[22]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[23]  M. Kenward,et al.  Small sample inference for fixed effects from restricted maximum likelihood. , 1997, Biometrics.

[24]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[25]  R. S. Zimmerman,et al.  The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals , 2015, Journal of visualized experiments : JoVE.

[26]  R. Desjardins,et al.  Assessment of the uncertainty of using an inverse-dispersion technique to measure methane emissions from animals in a barn and in a small pen , 2010 .

[27]  M. G. Chagunda,et al.  Opportunities and challenges in the use of the Laser Methane Detector to monitor enteric methane emissions from ruminants. , 2013, Animal : an international journal of animal bioscience.

[28]  J. Craigon,et al.  On-farm methane measurements during milking correlate with total methane production by individual dairy cows. , 2012, Journal of dairy science.

[29]  M. Lynch,et al.  Genetics and Analysis of Quantitative Traits , 1996 .

[30]  M. Pszczoła,et al.  Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. , 2017, Journal of animal science.

[31]  M. Scholtz,et al.  Methane production in different breeds, grazing different pastures or fed a total mixed ration, as measured by a Laser Methane Detector , 2014 .

[32]  L. Crompton,et al.  Methane emissions from cattle: Estimates from short-term measurements using a GreenFeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer , 2015 .

[33]  T. Yan,et al.  Measurement of enteric methane from ruminants using a hand-held laser methane detector , 2013 .

[34]  H. Clark,et al.  Reliability of the sulfur hexafluoride tracer technique for methane emission measurement from individual animals: an overview , 2008 .

[35]  T. Flesch,et al.  An approach for measuring methane emissions from whole farms. , 2006, Journal of environmental quality.

[36]  R. Roessler,et al.  Using a portable laser methane detector in goats to assess diurnal, diet- and position-dependent variations in enteric methane emissions , 2018, Comput. Electron. Agric..

[37]  Michael Haber,et al.  Evaluation of Agreement between Measurement Methods from Data with Matched Repeated Measurements via the Coefficient of Individual Agreement. , 2010, Journal of data science : JDS.

[38]  A.J.A. Aarnink,et al.  Carbon Dioxide Production in Animal Houses: A literature review , 2008 .

[39]  J. Bastiaansen,et al.  Standard error of the genetic correlation: how much data do we need to estimate a purebred-crossbred genetic correlation? , 2014, Genetics Selection Evolution.

[40]  J. Lassen,et al.  Accuracy of noninvasive breath methane measurements using Fourier transform infrared methods on individual cows. , 2012, Journal of dairy science.

[41]  V. Muchenje,et al.  Enteric methane output from selected herds of beef cattle raised under extensive arid rangelands , 2018, Pastoralism.

[42]  Comparison of Concordance Correlation Coefficient and Coefficient of Individual Agreement in Assessing Agreement , 2007, Journal of biopharmaceutical statistics.

[43]  A. Robertson.  THE SAMPLING VARIANCE OF THE GENETIC CORRELATION COEFFICIENT , 1959 .

[44]  I. Cuthill,et al.  Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research † , 2012, Osteoarthritis and Cartilage.

[45]  D. Morgavi,et al.  Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system , 2016 .

[46]  J. Hyslop,et al.  Evaluation of the laser methane detector to estimate methane emissions from ewes and steers. , 2014, Journal of animal science.

[47]  J. Lassen,et al.  Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods , 2016, Comput. Electron. Agric..

[48]  G. Bishop-Hurley,et al.  Measuring Methane Production from Ruminants. , 2016, Trends in biotechnology.