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Sorg, D. Livestock CH4 Emissions with the Laser Methane Detector. Encyclopedia. Available online: https://encyclopedia.pub/entry/18402 (accessed on 16 August 2024).
Sorg D. Livestock CH4 Emissions with the Laser Methane Detector. Encyclopedia. Available at: https://encyclopedia.pub/entry/18402. Accessed August 16, 2024.
Sorg, Diana. "Livestock CH4 Emissions with the Laser Methane Detector" Encyclopedia, https://encyclopedia.pub/entry/18402 (accessed August 16, 2024).
Sorg, D. (2022, January 18). Livestock CH4 Emissions with the Laser Methane Detector. In Encyclopedia. https://encyclopedia.pub/entry/18402
Sorg, Diana. "Livestock CH4 Emissions with the Laser Methane Detector." Encyclopedia. Web. 18 January, 2022.
Livestock CH4 Emissions with the Laser Methane Detector
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This entry is adapted from the peer-reviewed paper 10.3390/methane1010004

The handheld, portable laser methane detector (LMD) was developed to detect gas leaks in industry from a safe distance. Since 2009, it has also been used to measure the methane (CH4) concentration in the breath of cattle, sheep, and goats to quantify their CH4 emissions.

methane emission laser methane detector ruminants sensor

1. The Laser Methane Detector

The measurement with the Laser Methane Detector (LMD) (Figure 1a) is based on infrared absorption spectroscopy: it uses a semiconductor laser as a collimated excitation source and employs the second harmonic detection of wavelength-modulation spectroscopy for the measurement [1]. A visible guiding laser (Class 3 R laser, 532 nm) helps to direct the invisible measuring laser (Class 1 laser, 1653 nm) to the desired target. The integrated CH4 concentration between the LMD and the target is measured by detecting a fraction of the diffusely reflected laser beam [2]. The measured value is expressed as CH4 column density (ppm × m), i.e., a cumulative CH4 concentration along the laser path or the average CH4 concentration (ppm) multiplied by the length of the path (m) [3]. The LMD measures CH4 in the range of 1 to 50,000 ppm × m (up to 5 vol-%) with an accuracy of ±10%, and can be used from a distance between 0.5 and 30 m and in a temperature range from −17 to +50 °C. It autocalibrates via an internal reference cell [4]. The LMD shows the data in real time on its display and optionally issues an acoustic and visual alarm if a certain threshold is exceeded. Data can be stored in a csv file on a wirelessly connected Android device running the GasViewer app [5]. This can be, for example, a mobile phone (smartphone) worn in an armband sleeve so that one person can operate the LMD and the app at the same time. It has been originally designed to detect CH4 from gas leaks in mining, the petrochemical industry, and landfills. The studies cited here used a “LaserMethane mini-g,” a similar model or a previous model of the same series of LMD from the same manufacturer (Tokyo Gas Engineering Solutions, Tokyo, Japan).
Methane 01 00004 g001 550
Figure 1. (a) A laser methane detector (LMD, Tokyo Gas Engineering Solutions, Tokyo, Japan). (b,c) cows in different positions with the visible guiding laser pointed at their nostrils. (d) measurement with the LMD (source: D. Sorg).

2. Applications

In several areas of ruminant research, there is a need to assess CH4 emissions in order to develop mitigation measures or to obtain valuable information on the physiological and metabolic status of animals. Hence, the LMD has been successfully used for studies in various fields of animal science:

2.1. Genetics

In addition to the established and well-studied traits for animal performance, conformation, health and fertility, traits for feed efficiency and environmental impact have been studied for some time. Selective breeding for lower CH4 emissions could make a valuable contribution to the set of mitigation strategies to achieve climate targets but requires data from a sufficient number of animals that are phenotyped and genotyped [6]. Such data must be obtained under on-farm conditions from a large sample of animals. Therefore, complex or invasive techniques with high accuracy such as the respiration chamber or sulphur heaxafluoride (SF6) tracer gas methods are not suitable. The LMD is able to classify animals according to their CH4 production and has therefore been used to calculate estimates of heritability (h2) for several CH4 phenotypes [7][8][9]. The results (0.01–0.23) are in the range of those obtained with other on-farm methods [10], although the standard errors are higher. The measurements with the LMD are more influenced by environmental conditions, resulting in higher variability of the recorded values. Furthermore, the number of animals that have been included in genetic evaluations based on LMD data so far is limited. With larger samples and a more standardised protocol for measurement and data analysis, estimates of h2 are likely to improve.

2.2. Nutrition

Novel feeds and natural and synthetic feed additives are emerging CH4-mitigation technologies with high commercialisation potential and a potentially large impact on CH4 emissions in the future [11]. The effect of these feeds and feed additives needs to be validated in a large sample of animals under on-farm conditions. Among other methods, the LMD could be a valuable technique for their investigation. A few studies have successfully used the LMD to discriminate between different rations and feeding strategies at the group level [12][13][14]. Cameron et al. [15] measured the CH4 production of dairy cows with the LMD and concluded that adding fresh grass or pasture to a total mixed ration reduced it by 17% and 39%, respectively, and could be a valuable tool to reduce CH4 emissions. However, some researchers were unable to detect differences in CH4 concentration or production measured with the LMD due to dietary components [16][17] despite their well-studied effect on methanogenesis [18].
Vrancken et al. [19] demonstrated the effectiveness of a novel feed additive to decrease CH4 emissions from cows using LMD measurements. If such feed additives are to be used as mitigation measures in the future, their effect must be extensively demonstrated at the farm level. For this, the LMD could be a useful application.

2.3. Farming Systems and Breeds

Due to its relatively low cost, flexibility, and portability, the LMD can be used to systematically compare farms and farming systems in regions with limited access to research infrastructure or with animals on pasture. This was demonstrated by Pinto et al. [16], who were able to characterise access to pasture and different husbandry systems along an urban–rural gradient in India using CH4 phenotypes obtained with the LMD. Grobler et al. [20] found breed differences (p < 0.05) in the CH4 production of South African Jersey, Bonsmara, and Nguni cattle, while in the study by Mapfumo et al. [21] extensively reared African Boran and Nguni heifers showed no difference in CH4 output per DMI. If the breed differences in CH4 production are too small, the LMD may not be able to detect them.

2.4. Health and Metabolism

The correlation of physiological blood parameters with CH4 production measured with the LMD has been investigated by Reintke et al. [22]: the blood concentrations of zinc, β-hydroxybutyrate, and unesterified fatty acids had a partly breed-specific significant (p < 0.05) influence on CH4 concentrations.
For the applications described above, the LMD can be a complement to other established methods or an alternative in environments where more sophisticated and complex methods are not suitable. The LMD meets the requirement to measure variation over time (especially whole and consecutive lactation periods) in large numbers of animals without disrupting normal animal behaviour or farm management on commercial farms and limiting investment and maintenance costs [23] (pp. 34–35). It can be used for experiments where animals are to be categorized or ranked according to their CH4 concentration—especially in genetic analyses—or where average values of CH4 concentration or estimated CH4 production at a group level are sufficient. For these purposes, large samples are recommended: many replicates per animal and/or many animals. A common protocol specific to each purpose would further improve the comparability of such studies and is also suggested by other researchers [24]. If possible, external validation with another on-farm method (e.g., GF, sniffer, or SF6) or the newly developed “artificial cow” [25] should be attempted. It is not recommended to use the LMD if accurate values for absolute CH4 production of individual animals are needed.

References

  1. Chagunda, M.G.G.; Ross, D.; Roberts, D.J. On the use of a laser methane detector in dairy cows. Comput. Electron. Agric. 2009, 68, 157–160.
  2. Iseki, T. A portable remote methane detector using an InGaAsP DFB laser. Environ. Geol. 2004, 46, 1064–1069.
  3. Tokyo Gas Engineering Solutions. SA3C50A, LaserMethane Mini-g Operating Manual. Available online: https://www.crowcon.com/wp-content/uploads/2018/12/SA3C50A-Manual.pdf (accessed on 29 November 2021).
  4. Crowcon Detection Instruments. LaserMethane Mini Specifications. Available online: https://www.crowcon.com/products/portables/lmm-gen-2/#specs (accessed on 26 November 2021).
  5. Tokyo Gas Engineering Solutions. GasViewer, Laser Methane Linked Software for Android Devices, Instruction Manual. Available online: https://www.crowcon.com/wp-content/uploads/2018/12/GasViwer-Eng-manual.pdf (accessed on 26 November 2021).
  6. de Haas, Y.; Veerkamp, R.F.; de Jong, G.; Aldridge, M.N. Selective breeding as a mitigation tool for methane emissions from dairy cattle. Animal 2021, 15, 100294.
  7. Pickering, N.K.; Chagunda, M.G.G.; Banos, G.; Mrode, R.; McEwan, J.C.; Wall, E. Genetic parameters for predicted methane production and laser methane detector measurements. J. Anim. Sci. 2015, 93, 11–20.
  8. Mühlbach, S.; Sorg, D.; Rosner, F.; Kecman, J.; Swalve, H. Genetic analyses for CH4 concentrations in the breath of dairy cows measured on-farm with the Laser Methane Detector. In Proceedings of the World Congress on Genetics Applied to Livestock Production, Auckland, New Zealand, 11–16 February 2018.
  9. Reintke, J.; Brügemann, K.; Yin, T.; Engel, P.; Wagner, H.; Wehrend, A.; König, S. Assessment of methane emission traits in ewes using a laser methane detector: Genetic parameters and impact on lamb weaning performance. Arch. Anim. Breed. 2020, 63, 113–123.
  10. Lassen, J.; Difford, G.F. Genetic and genomic selection as a methane mitigation strategy in dairy cattle. Animal 2020, 14, s473–s483.
  11. Reisinger, A.; Clark, H.; Cowie, A.L.; Emmet-Booth, J.; Gonzalez Fischer, C.; Herrero, M.; Howden, M.; Leahy, S. How necessary and feasible are reductions of methane emissions from livestock to support stringent temperature goals? Philos. Trans. R. Soc. A 2021, 379, 20200452.
  12. Rooke, J.; Ricci, P.; Duthi, C.; Roehe, R.; Waterhouse, A. Measurements of methane using the laser methane detector are related to total daily methane output in beef cattle. In Emissions of Gas and Dust from Livestock; Hassouna, M., Guingand, N., Eds.; IFIP—Institut Technique du Porc: Paris, France, 2013; pp. 367–370. ISBN 978-2-85969-221-6.
  13. Ricci, P.; Chagunda, M.G.G.; Rooke, J.; Houdijk, J.G.M.; Duthie, C.-A.; Hyslop, J.; Roehe, R.; Waterhouse, A. Evaluation of the laser methane detector to estimate methane emissions from ewes and steers. J. Anim. Sci. 2014, 92, 5239–5250.
  14. Washaya, S.; Mupangwa, J.; Muchenje, V. Chemical composition of Lablab purpureus and Vigna unguiculata and their subsequent effects on methane production in Xhosa lop-eared goats. S. Afr. J. Anim. Sci. 2018, 48, 445–458.
  15. Cameron, L.; Chagunda, M.; Roberts, D.; Lee, M. A comparison of milk yields and methane production from three contrasting high-yielding dairy cattle feeding regimes: Cut-and-carry, partial grazing and total mixed ration. Grass Forage Sci. 2018, 73, 789–797.
  16. Pinto, A.; Yin, T.; Reichenbach, M.; Bhatta, R.; Malik, P.K.; Schlecht, E.; König, S. Enteric Methane Emissions of Dairy Cattle Considering Breed Composition, Pasture Management, Housing Conditions and Feeding Characteristics along a Rural-Urban Gradient in a Rising Megacity. Agriculture 2020, 10, 628.
  17. Roessler, R.; Chefor, F.; Schlecht, E. Using a portable laser methane detector in goats to assess diurnal, diet- and position-dependent variations in enteric methane emissions. Comput. Electron. Agric. 2018, 150, 110–117.
  18. Knapp, J.R.; Laur, G.L.; Vadas, P.A.; Weiss, W.P.; Tricarico, J.M. Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 2014, 97, 3231–3261.
  19. Vrancken, H.; Suenkel, M.; Hargreaves, P.R.; Chew, L.; Towers, E. Reduction of Enteric Methane Emission in a Commercial Dairy Farm by a Novel Feed Supplement. Open J. Anim. Sci. 2019, 9, 286–296.
  20. Grobler, S.; Scholtz, M.; Van Rooyen, H.; Mpayipheli, M.; Neser, F. Methane production in different breeds, grazing different pastures or fed a total mixed ration, as measured by a Laser Methane Detector. S. Afr. J. Anim. Sci. 2014, 44, 12–16.
  21. Mapfumo, L.; Grobler, S.; Mupangwa, J.; Scholtz, M.; Muchenje, V. Enteric methane output from selected herds of beef cattle raised under extensive arid rangelands. Pastoralism 2018, 8, 1–8.
  22. Reintke, J.; Brügemann, K.; Yin, T.; Wagner, H.; Wehrend, A.; Müller, A.; König, S. Associations between minerals and metabolic indicators in maternal blood pre-and postpartum with ewe body condition, methane emissions, and lamb body weight development. Animal 2021, 15, 100034.
  23. Wu, L. Measurement Methods to Assess Methane Production of Individual Dairy Cows in a Barn. Ph.D. Thesis, Wageningen University and Research, Wageningen, The Netherlands, 2016.
  24. Jonker, A.; Chagunda, M.G.G.; Grobler, M.; Waghorn, G.C.; Van Wyngaard, J.D. Hand-held laser methane detector to determine methane plume concentrations from ruminants. In Guideline for Estimating Methane Emissions from Individual Ruminants Using: GreenFeed, Sniffers, Hand-Held Laser Detector and Portable Accumulation Chambers; Jonker, A., Waghorn, G.C., Eds.; MPI Technical Paper; Ministry for Primary Industries: Wellington, New Zealand, 2020; pp. 41–47.
  25. Wu, L.; 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, 1554–1564.
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Diana Sorg
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