Novel statistical methods to improve analysis of laser methane detector data

Sarah Brocklehurst, PR Hargreaves, MD March*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Methane represents 16% of global greenhouse gas (GHG) emissions with enteric fermentation by ruminant animals such as livestock estimated to generate 28% of all anthropomorphic methane (Beauchemin et al, 2008). Diet digestibility and genetic merit can affect methane produced with estimates for dairy cows reported to range from 278 to 456g CH4/day (Garnsworthy et al, 2012). The LMD is a nonintrusive method used to measure CH4 from livestock in the field on the assumption that it is a sufficiently accurate approximation to more arduous methods such as use of closed chambers. Here we investigate the accuracy resulting from various statistical methods of analysis of LMD data by comparison with measurements taken simultaneously in respiration chambers. Material and methods Eighteen Holstein Friesian heifers were measured over 20 days, with each heifer placed in a chamber for 2 days during which methane in parts per million (ppm) was measured by the chamber once every 4 minutes. For 6-8 intervals per heifer, most of 5-6 minutes each (Bruder et al, 2017), methane was also measured (twice per second) using the LMD. This resulted in LMD data from 140 intervals totalling 12.9 hours over 20 days. For each interval, data from the LMD were log transformed (Figure 1) and various statistical methods were used to smooth the LMD data for each interval and remove the impact of 0 value data measurements. Some of these methods obtained smoothed estimates of the probability density function (pdf) for each interval whilst others were based on the time series (Figure 2). Each method resulted in a summary statistic for each interval (such as estimated mean or area under curve) adjusted for interval length. The resulting summaries were compared with estimates of g CH4/day from the chambers for each heifer. These methods were also applied to data from thrice-weekly LMD measurement of 28 cows over 12 weeks in another study, to see whether the LMD could be used to detect differences between two diets. Results Several of the alternative statistical methods gave very similar results, with Pearson’s correlation coefficient between the average over the LMD summaries and the average g/day measured over 2 days by the respiration chamber of 0.6 (P<0.01). Of the methods investigated there was no evidence to suggest that those that take into account the time series nature of the data outperform those that do not. Whilst these correlations are not particularly high it should be born in mind that they arise from just 6-8 LMD tests mostly between 5-6 minutes long. Furthermore, there was evidence to suggest that the LMD was sufficiently accurate to detect differences in methane emissions resulting from different experimental diets.
Original languageEnglish
Title of host publicationProceedings of the British Society of Animal Science
Subtitle of host publicationAnimal Science Fit for the Future
Number of pages1
Volume10
Edition1
DOIs
Publication statusPrint publication - Apr 2019

Publication series

NameAdvances in Animal Biosciences
PublisherCambridge University Press
ISSN (Print)2040-4700

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laser
methane
diet
livestock
respiration
method
detector
analysis
time series
ruminant
digestibility
probability density function
fermentation
greenhouse gas
animal

Cite this

Brocklehurst, S., Hargreaves, PR., & March, MD. (2019). Novel statistical methods to improve analysis of laser methane detector data. In Proceedings of the British Society of Animal Science: Animal Science Fit for the Future (1 ed., Vol. 10). [157] (Advances in Animal Biosciences). https://doi.org/10.1017/S2040470019000013
Brocklehurst, Sarah ; Hargreaves, PR ; March, MD. / Novel statistical methods to improve analysis of laser methane detector data. Proceedings of the British Society of Animal Science: Animal Science Fit for the Future. Vol. 10 1. ed. 2019. (Advances in Animal Biosciences).
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title = "Novel statistical methods to improve analysis of laser methane detector data",
abstract = "Methane represents 16{\%} of global greenhouse gas (GHG) emissions with enteric fermentation by ruminant animals such as livestock estimated to generate 28{\%} of all anthropomorphic methane (Beauchemin et al, 2008). Diet digestibility and genetic merit can affect methane produced with estimates for dairy cows reported to range from 278 to 456g CH4/day (Garnsworthy et al, 2012). The LMD is a nonintrusive method used to measure CH4 from livestock in the field on the assumption that it is a sufficiently accurate approximation to more arduous methods such as use of closed chambers. Here we investigate the accuracy resulting from various statistical methods of analysis of LMD data by comparison with measurements taken simultaneously in respiration chambers. Material and methods Eighteen Holstein Friesian heifers were measured over 20 days, with each heifer placed in a chamber for 2 days during which methane in parts per million (ppm) was measured by the chamber once every 4 minutes. For 6-8 intervals per heifer, most of 5-6 minutes each (Bruder et al, 2017), methane was also measured (twice per second) using the LMD. This resulted in LMD data from 140 intervals totalling 12.9 hours over 20 days. For each interval, data from the LMD were log transformed (Figure 1) and various statistical methods were used to smooth the LMD data for each interval and remove the impact of 0 value data measurements. Some of these methods obtained smoothed estimates of the probability density function (pdf) for each interval whilst others were based on the time series (Figure 2). Each method resulted in a summary statistic for each interval (such as estimated mean or area under curve) adjusted for interval length. The resulting summaries were compared with estimates of g CH4/day from the chambers for each heifer. These methods were also applied to data from thrice-weekly LMD measurement of 28 cows over 12 weeks in another study, to see whether the LMD could be used to detect differences between two diets. Results Several of the alternative statistical methods gave very similar results, with Pearson’s correlation coefficient between the average over the LMD summaries and the average g/day measured over 2 days by the respiration chamber of 0.6 (P<0.01). Of the methods investigated there was no evidence to suggest that those that take into account the time series nature of the data outperform those that do not. Whilst these correlations are not particularly high it should be born in mind that they arise from just 6-8 LMD tests mostly between 5-6 minutes long. Furthermore, there was evidence to suggest that the LMD was sufficiently accurate to detect differences in methane emissions resulting from different experimental diets.",
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Brocklehurst, S, Hargreaves, PR & March, MD 2019, Novel statistical methods to improve analysis of laser methane detector data. in Proceedings of the British Society of Animal Science: Animal Science Fit for the Future. 1 edn, vol. 10, 157, Advances in Animal Biosciences. https://doi.org/10.1017/S2040470019000013

Novel statistical methods to improve analysis of laser methane detector data. / Brocklehurst, Sarah; Hargreaves, PR; March, MD.

Proceedings of the British Society of Animal Science: Animal Science Fit for the Future. Vol. 10 1. ed. 2019. 157 (Advances in Animal Biosciences).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Brocklehurst, Sarah

AU - Hargreaves, PR

AU - March, MD

PY - 2019/4

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N2 - Methane represents 16% of global greenhouse gas (GHG) emissions with enteric fermentation by ruminant animals such as livestock estimated to generate 28% of all anthropomorphic methane (Beauchemin et al, 2008). Diet digestibility and genetic merit can affect methane produced with estimates for dairy cows reported to range from 278 to 456g CH4/day (Garnsworthy et al, 2012). The LMD is a nonintrusive method used to measure CH4 from livestock in the field on the assumption that it is a sufficiently accurate approximation to more arduous methods such as use of closed chambers. Here we investigate the accuracy resulting from various statistical methods of analysis of LMD data by comparison with measurements taken simultaneously in respiration chambers. Material and methods Eighteen Holstein Friesian heifers were measured over 20 days, with each heifer placed in a chamber for 2 days during which methane in parts per million (ppm) was measured by the chamber once every 4 minutes. For 6-8 intervals per heifer, most of 5-6 minutes each (Bruder et al, 2017), methane was also measured (twice per second) using the LMD. This resulted in LMD data from 140 intervals totalling 12.9 hours over 20 days. For each interval, data from the LMD were log transformed (Figure 1) and various statistical methods were used to smooth the LMD data for each interval and remove the impact of 0 value data measurements. Some of these methods obtained smoothed estimates of the probability density function (pdf) for each interval whilst others were based on the time series (Figure 2). Each method resulted in a summary statistic for each interval (such as estimated mean or area under curve) adjusted for interval length. The resulting summaries were compared with estimates of g CH4/day from the chambers for each heifer. These methods were also applied to data from thrice-weekly LMD measurement of 28 cows over 12 weeks in another study, to see whether the LMD could be used to detect differences between two diets. Results Several of the alternative statistical methods gave very similar results, with Pearson’s correlation coefficient between the average over the LMD summaries and the average g/day measured over 2 days by the respiration chamber of 0.6 (P<0.01). Of the methods investigated there was no evidence to suggest that those that take into account the time series nature of the data outperform those that do not. Whilst these correlations are not particularly high it should be born in mind that they arise from just 6-8 LMD tests mostly between 5-6 minutes long. Furthermore, there was evidence to suggest that the LMD was sufficiently accurate to detect differences in methane emissions resulting from different experimental diets.

AB - Methane represents 16% of global greenhouse gas (GHG) emissions with enteric fermentation by ruminant animals such as livestock estimated to generate 28% of all anthropomorphic methane (Beauchemin et al, 2008). Diet digestibility and genetic merit can affect methane produced with estimates for dairy cows reported to range from 278 to 456g CH4/day (Garnsworthy et al, 2012). The LMD is a nonintrusive method used to measure CH4 from livestock in the field on the assumption that it is a sufficiently accurate approximation to more arduous methods such as use of closed chambers. Here we investigate the accuracy resulting from various statistical methods of analysis of LMD data by comparison with measurements taken simultaneously in respiration chambers. Material and methods Eighteen Holstein Friesian heifers were measured over 20 days, with each heifer placed in a chamber for 2 days during which methane in parts per million (ppm) was measured by the chamber once every 4 minutes. For 6-8 intervals per heifer, most of 5-6 minutes each (Bruder et al, 2017), methane was also measured (twice per second) using the LMD. This resulted in LMD data from 140 intervals totalling 12.9 hours over 20 days. For each interval, data from the LMD were log transformed (Figure 1) and various statistical methods were used to smooth the LMD data for each interval and remove the impact of 0 value data measurements. Some of these methods obtained smoothed estimates of the probability density function (pdf) for each interval whilst others were based on the time series (Figure 2). Each method resulted in a summary statistic for each interval (such as estimated mean or area under curve) adjusted for interval length. The resulting summaries were compared with estimates of g CH4/day from the chambers for each heifer. These methods were also applied to data from thrice-weekly LMD measurement of 28 cows over 12 weeks in another study, to see whether the LMD could be used to detect differences between two diets. Results Several of the alternative statistical methods gave very similar results, with Pearson’s correlation coefficient between the average over the LMD summaries and the average g/day measured over 2 days by the respiration chamber of 0.6 (P<0.01). Of the methods investigated there was no evidence to suggest that those that take into account the time series nature of the data outperform those that do not. Whilst these correlations are not particularly high it should be born in mind that they arise from just 6-8 LMD tests mostly between 5-6 minutes long. Furthermore, there was evidence to suggest that the LMD was sufficiently accurate to detect differences in methane emissions resulting from different experimental diets.

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M3 - Conference contribution

VL - 10

T3 - Advances in Animal Biosciences

BT - Proceedings of the British Society of Animal Science

ER -

Brocklehurst S, Hargreaves PR, March MD. Novel statistical methods to improve analysis of laser methane detector data. In Proceedings of the British Society of Animal Science: Animal Science Fit for the Future. 1 ed. Vol. 10. 2019. 157. (Advances in Animal Biosciences). https://doi.org/10.1017/S2040470019000013