Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review
Abstract
:1. Introduction
2. Types and Sources of GHG and Hazardous Gases from Ruminant Production Systems
2.1. Ruminant Greenhouse and Hazardous Gases
2.2. Mechanisms of Greenhouse and Hazardous Gases Produced by Ruminant Animals
2.2.1. Carbon Dioxide (Greenhouse Gas)
2.2.2. Methane (Greenhouse Gas)
2.2.3. Ammonia (Hazardous Gas)
2.2.4. Hydrogen Sulfide (Hazardous Gas)
3. Gas Concentration Monitoring
3.1. Chemical Sensors
3.1.1. Electrochemical Detectors
3.1.2. GC
3.1.3. Semiconductor Metal Oxide Chemical Sensors
3.1.4. Pellistor Sensors
3.2. Infrared Sensors
3.2.1. FTIR
3.2.2. NDIR
3.2.3. TDLAS
3.2.4. Portable InGaAs Laser Methane Detector (LMD)
3.3. Laser Spectral Detectors
4. Gas Emissions Monitoring
4.1. Breathing Chambers
4.2. Breathing Masks
4.3. Head Breathing Chamber with Hopper
5. Discussion
6. Summary and Future Work
6.1. Summary
6.2. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Kristiansen, S.; Painter, J.; Shea, M. Animal agriculture and climate change in the us and uk elite media: Volume, responsibilities, causes and solutions. Environ. Commun. 2021, 15, 153–172. [Google Scholar] [CrossRef]
- Bekele, W.; Guinguina, A.; Zegeye, A.; Simachew, A.; Ramin, M. Contemporary methods of measuring and estimating methane emission from ruminants. Methane 2022, 1, 82–95. [Google Scholar] [CrossRef]
- Wang, K.; Xiong, B.; Zhao, X. Could propionate formation be used to reduce enteric methane emission in ruminants? Sci. Total Environ. 2023, 855, 158867. [Google Scholar] [CrossRef] [PubMed]
- Gu, S.; Qiu, Z.; Zhan, Y.; Qian, K.; Xiong, R.; Dai, H.; Yin, J.; Wei, S. Spatial-temporal characteristics and trend prediction of carbon emissions from husbandry in China. J. Agro-Environ. Sci. 2023, 42, 705–714. [Google Scholar]
- Hristov, A.N.; Oh, J.; Lee, C.; Meinen, R.; Makkar, P.S. Mitigation of Greenhouse Gas Emissions in Livestock Production—A Review of Technical Options for Non-CO2 Emissions; FAO Animal Production & Health Paper; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2013. [Google Scholar]
- Ye, Y.; Zhu, T.; Wu, Z.; Cai, X. Research progress on application of methane emission monitoring technology in ruminants. Trans. Chin. Soc. Agric. Mach. 2022, 53, 277–292. [Google Scholar]
- Liu, J.; Chen, Y. Digital technology development, temporal and spatial effects, regional carbon emissions. Stud. Sci. Sci. 2023, 41, 841–853. [Google Scholar]
- de Klein, C.A.; Ledgard, S.F. Nitrous oxide emissions from New Zealand agriculture—Key sources and mitigation strategies. Nutr. Cycl. Agroecosyst. 2005, 72, 77–85. [Google Scholar] [CrossRef]
- Chen, Y.; Li, J.; Wu, Y.; Zhang, H.; Qi, L. Emission characteristics of methane and nitrous oxide from livestock in inner Mongolia guring the past 21 years. Pratacult. Sci. 2024, 41, 527–538. [Google Scholar]
- Cardoso-Gutierrez, E.; Aranda-Aguirre, E.; Robles-Jimenez, L.E.; Castelán-Ortega, O.A.; Chay-Canul, A.J.; Foggi, G.; Angeles-Hernandez, J.C.; Vargas-Bello-Pérez, E.; González-Ronquillo, M. Effect of tannins from tropical plants on methane production from ruminants: A systematic review. Vet. Anim. Sci. 2021, 14, 100214. [Google Scholar] [CrossRef]
- Zhen, C.; Xin, L. Progress on methane mitigation in livestock gastrointestinal tract. J. Domest. Anim. Ecol. 2011, 32, 1–8. [Google Scholar]
- Li, K.; Liang, T.; Zhang, X.; Na, R. Research advances on nutritional regulation of rumen methanogenesis in ruminants. Feed. Res. 2021, 44, 139–144. [Google Scholar] [CrossRef]
- Yi, L.; Guo, S.; Shi, B. Research progress on effect of plant extracts on the emission of harmful gases from animals. Feed. Res. 2023, 46, 154–156. [Google Scholar]
- Yu, S.; Dong, S.; Ou, J. Quantitative identification of harmful gases NH3 and H2S by electronic nose in pig houses. Trans. Chin. Soc. Agric. Eng. 2009, 25, 153–157. [Google Scholar]
- Lan, Y. Carbon dioxide: The “main culprit” of greenhouse gases. Earth 2013, 9, 34–37. [Google Scholar]
- Kun, H. Digestion and metabolism of sugars, proteins and fats in the rumen of ruminants. Tech. Advis. Anim. Husb. 2022, 12. [Google Scholar]
- Della Rosa, M.M.; Waghorn, G.C.; Vibart, R.E.; Jonker, A. An assessment of global ruminant methane-emission measurements shows bias relative to contributions of farmed species, populations and among continents. Anim. Prod. Sci. 2023, 63, 201–212. [Google Scholar] [CrossRef]
- Niu, H.; Hu, Z.; Chang, J.; Zhang, Y.; Wang, Y. Effects of rumen microorganisms on feed efficiency and methane emission of ruminants and their nutritional regulation research progresses. Chin. J. Anim. Sci. 2020, 56, 50–56, 62. [Google Scholar]
- He, Y.; Wang, Q.; Li, J. Advance in strategies of decrease methane emission in ruminant. J. Yellow Cattle Sci. 2001, 5, 47–50. [Google Scholar]
- Li, Y.; Xu, X. Environmental hazards of methane gas emissions from ruminants and response measures. China Dairy Cattle 2011, 5, 3. [Google Scholar]
- Sun, K.; Xu, B.; Xu, Y.; Gao, T. Factors influencing the methane emissions from the stored ruminant manure. J. Domest. Anim. Ecol. 2015, 12, 1–511. [Google Scholar]
- Aubert, T. Protein efficiency and ammonia excretion in dairy cows. Int. Dairy Top. 2022, 21, 9–10. [Google Scholar]
- Hui, D. How to prevent the dangers of ammonia in winter sheep housing. HU Bei Nong Ji Hua 2021, 9, 84–85. [Google Scholar]
- Lutnicki, K.; Madej, E.; Riha, T.; Kurek, U. Polioencephalomalacia in ruminants caused by excessive amount of sulphur—A review. Bull. Vet. Inst. Pulawy 2014, 58, 321–326. [Google Scholar] [CrossRef]
- Hu, W.; Du, Y. Hazards of hydrogen sulphide gas and its detection methods. Mater. Prot. 1996, 29, 2. [Google Scholar]
- Yue, C.; Sun, M.; Zhang, C.; Kong, X.; Hao, R.; Yang, C. Concentrations of carbon dioxide, ammonia and hydrogen sulphide inside and outside fattening cowsheds in different months of the cold season. North. Anim. Husb. Mag. 2019, 24, 18–19. [Google Scholar]
- Fazio, E.; Spadaro, S.; Corsaro, C.; Neri, G.; Leonardi, S.G.; Neri, F.; Lavanya, N.; Sekar, C.; Donato, N.; Neri, G. Metal-oxide based nanomaterials: Synthesis, characterization and their applications in electrical and electrochemical sensors. Sensors 2021, 21, 2494. [Google Scholar] [CrossRef] [PubMed]
- Dey, A. Semiconductor metal oxide gas sensors: A review. Mater. Sci. Eng. B 2018, 229, 206–217. [Google Scholar] [CrossRef]
- Brahim, K.A.B.; Bendany, M.; Hamdouni, Y.E.; Abbi, K.; Bakkouche, C.; Fattoumi, H.; Hermouche, L.; Labjar, N.; Dalimi, M.; Hajjaji, S.E. Electrochemical detection of sulfadiazine by sensors based on chemically modified carbon electrodes: A review. Curr. Top. Med. Chem. 2023, 23, 1464–1476. [Google Scholar] [CrossRef]
- Nguyen, D.N.; Yoon, H. Recent advances in nanostructured conducting polymers: From synthesis to practical applications. Polymers 2016, 8, 118. [Google Scholar] [CrossRef]
- Qin, H.; Wang, Y.; Yang, Y.; Liu, Y.; Liu, X.; Cheng, Z. Research and fabrication of open type fast response electrochemical ammonia sensor. Transducer Microsyst. Technol. 2018, 9, 81–83. [Google Scholar]
- Samaroo, S.; Hickey, D.P. Electrochemical ammonia production from nitrates in agricultural tile drainage: Technoeconomic and global warming analysis. AIChE J. 2023, 69, e17969. [Google Scholar] [CrossRef]
- Chatterjee, D.; Paz-Pujalt, G.R.; Marrese, C.A. Fabrication and evaluation of thin-film solid-state sensors for hydrogen sulfide detection. Sens. Actuators B Chem. 1998, 53, 155–162. [Google Scholar]
- Lawrence, N.S.; Deo, R.P.; Wang, J. Electrochemical determination of hydrogen sulfide at carbon nanotube modified electrodes. Anal. Chim. Acta 2004, 517, 131–137. [Google Scholar] [CrossRef]
- Qu, J.; Cao, X.; Gao, L.; Li, J.; Li, L.; Xie, Y.; Zhao, Y.; Zhang, J.; Wu, M.; Liu, H. Electrochemical carbon dioxide reduction to ethylene:From mechanistic understanding to catalyst surface engineering. Nano-Micro Lett. 2023, 15, 382–415. [Google Scholar] [CrossRef] [PubMed]
- Yan, Y.; Lin, J.; Xu, Y.; Chen, X.; Zhang, S. Research progress in ion conducting membranes for electrochemical reduction of carbon dioxide. Membr. Sci. Technol. 2023, 43, 191–211. [Google Scholar]
- Sun, L.; Hong, J.; Wang, M.; Hirata, M. Solid electrolyte CO2 sensors using BaCO3-Li2CO3 carbonate electrode. Transducer Microsyst. Technol. 1994, 1, 9–14. [Google Scholar]
- Liu, Z.; Pan, Y.; Zhou, X.; Wang, W.; Dong, L.; Fang, Z. Application of sulfur chemiluminescence gas chromatography in gas analysis. Chem. Res. Appl. 2019, 9, 1623–1628. [Google Scholar]
- Brannon, E.Q.; Moseman-Valtierra, S.M.; Rella, C.W.; Martin, R.M.; Chen, X.; Tang, J. Evaluation of laser-based spectrometers for greenhouse gas flux measurements in coastal marshes. Limnol. Oceanogr. Methods 2016, 14, 466–476. [Google Scholar] [CrossRef]
- Harvey, M.J.; Sperlich, P.; Clough, T.J.; Kelliher, F.M.; Moss, R. Global research alliance n2o chamber methodology guidelines: Recommendations for air sample collection, storage and analysis. J. Environ. Qual. 2020, 49, 1110–1125. [Google Scholar] [CrossRef]
- Rodiawan; Shengchang, W. Production of H2S gas sensor by a facile fabrication of au-decorated ZnO. Mod. Phys. Lett. B 2022, 36, 2242044. [Google Scholar] [CrossRef]
- Kim, Y.; Jeon, Y.; Na, M.; Hwang, S.-J.; Yoon, Y. Recent trends in chemical sensors for detecting toxic materials. Sensors 2024, 24, 431. [Google Scholar] [CrossRef]
- Witkiewicz, Z.; Jasek, K.; Grabka, M. Semiconductor gas sensors for detecting chemical warfare agents and their simulants. Sensors 2023, 23, 3272. [Google Scholar] [CrossRef]
- Dadkhah, M.; Tulliani, J.-M. Green synthesis of metal oxides semiconductors for gas sensing applications. Sensors 2022, 22, 4669. [Google Scholar] [CrossRef] [PubMed]
- Liu, N.; Shu, Z.; Sui, R.; Wang, T.; Cao, Z. Research progress of methane catalytic combustion sensor based on mems technology. Coal Chem. Ind. 2022, 45, 131–135. [Google Scholar]
- Liu, X.; Yang, N.; Sun, Y.; Pi, Q.; Gu, Y. Research on method for accelerating stability of catalytic combustion gas sensor. Transducer Microsyst. Technol. 2021, 40, 52–54, 58. [Google Scholar]
- Müller, S.; Zimina, A.; Steininger, R.; Flessau, S.; Osswald, J.; Grunwaldt, J.-D. High stability of rh oxide-based thermoresistive catalytic combustion sensors proven by operando X-ray absorption spectroscopy and X-ray diffraction. ACS Sensors 2020, 5, 2486–2496. [Google Scholar] [CrossRef]
- Hyodo, T.; Shimizu, Y. Adsorption/combustion-type micro gas sensors: Typical VOC-sensing properties and material-design approach for highly sensitive and selective VOC detection. Anal. Sci. 2020, 36, 401–411. [Google Scholar] [CrossRef] [PubMed]
- Goikhman, B.; Avraham, M.; Bar-Lev, S.; Stolyarova, S.; Blank, T.; Nemirovsky, Y. A novel miniature and selective cmos gas sensor for gas mixture analysis—Part 3: Extending the chemical modeling. Micromachines 2023, 14, 270. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Bao, L.; Guo, J. Design of infrared methane detection system based on WIFI technology. Transducer Microsyst. Technol. 2018, 37, 74–76. [Google Scholar]
- Zhao, X.; Chen, X. Application status and development prospect of coal mine methane sensor. Coal 2024, 33, 5–9. [Google Scholar]
- Liu, H.; Xu, R.; Wang, Z.; Zhao, T.; Zhao, C.; Shi, Y.; Chen, L. Near- infrared methane gas detection tchnology based on tdlas with high sensitivity. Acta Photonica Sin. 2024, 53, 250–257. [Google Scholar]
- Yu, Z.; Pan, Y.; Wang, Q.; Zhang, H. Application of fourier infrared spectroscopy in detection of malodorous gases. Appl. 2019, 2, 38–40. [Google Scholar]
- Teng, Y. Application of partial least square regression in spectral analysis. Appl. IC 2020, 37, 16–17. [Google Scholar]
- Hussain, I.; Ganiyu, S.A.; Alasiri, H.; Alhooshani, K. Highly dispersed Cu-anchored nanoparticles based mordenite zeolite catalyst (Cu-MOR): Influence of the different preparation methods for direct methane oxidation (DMTM) to methanol. J. Energy Inst. 2023, 109, 101269. [Google Scholar] [CrossRef]
- Svedberg, U.; Galle, B. Assessment of terpene levels and workers’ exposure in sawmills with Long Path FTIR. Appl. Occup. Environ. Hyg. 2000, 15, 686–694. [Google Scholar] [PubMed]
- Lv, Y.; Zhang, T.; Fan, G.; Xiang, Y.; Cheng, J.; Lv, L. Monitoring of pollution characteristics of atmospheric greenhouse gases using fourier infrared system. Chin. J. Lasers 2023, 6, 156–164. [Google Scholar]
- Bush, D. Drones will help measure methane leaks from shipping. Lloyd’s List 2022, 1–3. [Google Scholar]
- Vinković, K.; Andersen, T.; de Vries, M.; Kers, B.; van Heuven, S.; Peters, W.; Hensen, A.; van den Bulk, P.; Chen, H. Evaluating the use of an Unmanned Aerial Vehicle (UAV)-based active AirCore system to quantify methane emissions from dairy cows. Sci. Total Environ. 2022, 831, 154898. [Google Scholar] [CrossRef] [PubMed]
- He, Z.; Li, Z.; Fan, C.; Zhang, Y.; Shi, Z.; Zheng, Y.; Gu, H.; Man, J.; Zuo, J.; Han, Y. Satellite sensors and retrieval algorithms of atmospheric methane. Acta Opt. Sin. 2023, 43, 17–63. [Google Scholar]
- Staebell, C.; Sun, K.; Samra, J.; Franklin, J.; Wofsy, S. Spectral calibration of the methaneair instrument. Atmos. Meas. Tech. 2021, 14, 3737–3753. [Google Scholar] [CrossRef]
- Bertrand, R.-L.; Hulbert, C. Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer. Nat. Commun. 2024, 15, 3081. [Google Scholar]
- Feng, S.; Li, X.; Ren, L.; Xu, S. Reinforcement learning with parameterized action space and sparse reward for UAV navigation. Intell. Robot. 2023, 3, 161–175. [Google Scholar] [CrossRef]
- Zhang, L.; Xu, D.; Xian, D.; Yang, D. Construction of China’s greenhouse gas observation satellites and typical data applications. Satell. Appl. 2023, 7, 8–12. [Google Scholar]
- Teng, T.P.; Chen, W.J. A compensation model for an NDIR-based CO2 sensor and its energy implication on demand control ventilation in a hot and humid climate. Energy Build. 2023, 281, 112738. [Google Scholar] [CrossRef]
- Sun, Z.; Guo, Y.; Jiao, M.; Yan, Z.; Wan, T.; Liu, B. Design of multi-component gangerous gas online monitoring system based on ndir. Laser J. 2024, 45, 40–45. [Google Scholar]
- Gutierrez, G.R.; Perez, A.O.; Palzer, S. Integrated, selective, simultaneous multigas sensing based on nondispersive infrared spectroscopy-type photoacoustic spectroscopy. ACS Sens. 2024, 9, 23–28. [Google Scholar] [CrossRef] [PubMed]
- Zhou, L.; He, Y.; Zhang, Q.; Zhang, L. Carbon Dioxide Sensor Module Based on NDIR Technology. Micromachines 2021, 12, 845. [Google Scholar] [CrossRef]
- Liu, Y.; Yin, S.; Cheng, Y.; Zou, X. Algorithm fusion based full range high accuracy laser methane sensor. Laser Optoelectron. Prog. 2024, 61, 1. [Google Scholar]
- Zhang, Q.; Zhang, T.; Wei, Y.; Liu, T. Highly sensitive and reliable optical fiber TDLAS gas detection system for methane in situ monitoring in near space. Appl. Opt. 2023, 62, 4409–4414. [Google Scholar] [CrossRef]
- Hammond, K.J.; Humphries, D.J.; Crompton, L.A.; Green, C.; Reynolds, C.K. Methane emissions from cattle: Estimates from short-term measurements using a greenfeed system compared with measurements obtained using respiration chambers or sulphur hexafluoride tracer. Anim. Feed Sci. Technol. 2015, 203, 41–52. [Google Scholar] [CrossRef]
- Zhang, Z.; Xia, H.; Sun, P.; Yu, R.; Yang, X.; Lin, Y.; Wu, B.; Pang, T.; Guo, Q.; Li, Z.; et al. Stable gaseous isotope measurement method based on highly sensitive laser absorption spectroscopy and its applications (invited). Acta Photonica Sin. 2023, 106, 119–135. [Google Scholar]
- Zhang, Y.; Wang, Y.; Li, L.; Zheng, C.; An, Y.; Song, Z. The principle and technical analysis of methane detection using infrared absorption spectroscopy. Spectrosc. Spectr. Anal. 2008, 28, 2515–2519. [Google Scholar]
- Pereira, A.M.; Peixoto, P.; Rosa, H.J.D.; Vouzela, C.; Madruga, J.S.; Borba, A.E.S. A longitudinal study with a laser methane detector (lmd) highlighting lactation cycle-related differences in methane emissions from dairy cows. Animals 2023, 13, 974. [Google Scholar] [CrossRef] [PubMed]
- Chagunda, M.G.G.; Yan, T. Do methane measurements from a laser detector and an indirect open-circuit respiration calorimetric chamber agree sufficiently closely? Anim. Feed. Sci. Technol. 2011, 165, 8–14. [Google Scholar] [CrossRef]
- Divya, Y.; Sanjeevi, S.; Ilamparuthi, K. A study on the hyperspectral signatures of sandy soils with varying texture and water content. Arab. J. Geosci. 2014, 7, 3537–3545. [Google Scholar] [CrossRef]
- Fu, M.; Zhang, H.; Wang, S.; Shui, Y. Rotating 3D laser mapping system for multi-rotor drones. Intell. Robot. 2023, 3, 632–646. [Google Scholar] [CrossRef]
- Lambert-Girard, S.; Babin, F.; Allard, M.; Piche, M. Enhancements to INO’S broadband SWIR/MWIR spectroscopic lidar. In Lidar Remote Sensing for Environmental Monitoring XIV; SPIE: Bellingham, WA, USA, 2013; 88720K-88720K-88710. [Google Scholar]
- Wu, D.; Liang, Z.; Chen, G. Deep learning for LiDAR-only and LiDAR-fusion 3D perception: A survey. Intell. Robot. 2022, 2, 105–129. [Google Scholar] [CrossRef]
- Huang, Y. Development of a methane-detection system using a distributed feedback laser diode and hollow-core photonic crystal fiber. Electronics 2023, 12, 838. [Google Scholar] [CrossRef]
- Gao, X.; Zhang, W.; Fang, X.; Xiao, J.; Luo, Y. Design and research of self-calibration NH3 gas detection device. J. Chin. Agric. Mech. 2017, 38, 82–86. [Google Scholar]
- Powers, W.; Capelari, M. Analytical methods for quantifying greenhouse gas flux in animal production systems. J. Anim. Sci. 2016, 94, 3139–3146. [Google Scholar] [CrossRef]
- Li, Y. Aircraft measure arctic methane. Int. Aviat. 2021, 12, 67. [Google Scholar]
- Park, H.; Park, C.; Kim, H.J.; Jeong, S.; Park, H.; Kim, Y.; Sim, S.; Kim, J.; Park, J.; Choi, J.S. Unexpected urban methane hotspots captured from aircraft observations. ACS Earth Space Chem. 2022, 6, 755–765. [Google Scholar] [CrossRef]
- Abbadi, S.H.E.; Chen, Z.; Burdeau, P.M.; Rutherford, J.S.; Chen, Y.; Zhang, Z.; Sherwin, E.D.; Brandt, A.R. Technological maturity of aircraft-based methane sensing for greenhouse gas mitigation. Environ. Sci. Technol. 2024, 58, 9591–9600. [Google Scholar] [CrossRef] [PubMed]
- Hristov, A.N.; Oh, J.; Giallongo, F.; Frederick, T.; Weeks, H.; Zimmerman, P.R.; Harper, M.T.; Hristova, R.A.; Zimmerman, R.S.; Branco, A.F. The use of an automated system (greenfeed) to monitor enteric methane and carbon dioxide emissions from ruminant animals. J. Vis. Exp. JoVE 2015, 103, e52904. [Google Scholar]
- Gardiner, T.D.; Coleman, M.D.; Innocenti, F.; Tompkins, J.; Connor, A.; Garnsworthy, P.C.; Moorby, J.M.; Reynolds, C.K.; Waterhouse, A.; Wills, D. Determination of the absolute accuracy of UK chamber facilities used in measuring methane emissions from livestock. Measurement 2015, 66, 272–279. [Google Scholar] [CrossRef]
- Sakita, G.Z.; Lima, P.D.M.T.; Abdalla Filho, A.L.; Bompadre, T.F.V.; Ovani, V.S.; Bizzuti, B.E.; da Costa, W.D.S.; do Prado Paim, T.; Campioni, T.S.; de Oliva Neto, P.; et al. Treating tropical grass with fibrolytic enzymes from the fungus Trichoderma reesei: Effects on animal performance, digestibility and enteric methane emissions of growing lambs. Anim. Feed Sci. Technol. 2022, 286, 115253. [Google Scholar] [CrossRef]
- Tedeschi, L.O.; Abdalla, A.L.; Álvarez, C.; Anuga, S.W.; Arango, J.; Beauchemin, K.A.; Becquet, P.; Berndt, A.; Burns, R.; De Camillis, C.; et al. Quantification of methane emitted by ruminants: A review of methods. J. Anim. Sci. 2022, 100, skac197. [Google Scholar] [CrossRef] [PubMed]
- Sun, G.; Sun, P.; Hao, L.; Liu, S.; Niu, J. Review of determination of ruminants produce methane quantitative methods. Heilongjiang Anim. Sci. Vet. Med. 2017, 8, 59–64. [Google Scholar]
- Zhao, G. Models for predicting rumen methane emission in ruminants. Chin. J. Anim. Nutr. 2014, 26, 3135–3139. [Google Scholar]
- Alemu, A.W.; Vyas, D.; Manafiazar, G.; Basarab, J.A.; Beauchemin, K.A. Enteric methane emissions from low– and high–residual feed intake beef heifers measured using greenfeed and respiration chamber techniques. J. Anim. Sci. 2017, 95, 3727–3737. [Google Scholar] [CrossRef]
- Pinares-Patiño, C.S.; Waghorn, G.C.; Machmüller, A.; Vlaming, B.; Molano, G.; Cavanagh, A.; Clark, H. Methane emissions and digestive physiology of non-lactating dairy cows fed pasture forage. Can. J. Anim. Sci. 2007, 87, 601–613. [Google Scholar] [CrossRef]
- Fernández, C.; Gomis-Tena, J.; Hernández, A.; Saiz, J. An open-circuit indirect calorimetry head hood system for measuring methane emission and energy metabolism in small ruminants. Animals 2019, 9, 380. [Google Scholar] [CrossRef] [PubMed]
- Van Breukelen, A.E.; Aldridge, M.N.; Veerkamp, R.F.; Koning, L.; Sebek, L.B.; de Haas, Y. Heritability and genetic correlations between enteric methane production and concentration recorded by greenfeed and sniffers on dairy cows. J. Dairy Sci. 2023, 106, 4121–4132. [Google Scholar] [CrossRef] [PubMed]
- Liu, Z.; Jia, P.; Lai, Q.; Dong, L.; Wu, Q.; Gao, Y.; Tian, Z.; Diao, Q. Characteristics of rumen fermentation and methane production in different parity dry cows. Acta Vet. Zootech. Sin. 2022, 53, 4296–4305. [Google Scholar]
- Coppa, M.; Vanlierde, A.; Bouchon, M.; Jurquet, J.; Musati, M.; Dehareng, F.; Martin, C. Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system. J. Dairy Sci. 2022, 105, 9271–9285. [Google Scholar] [CrossRef]
- Morrisonn, S.J.; Mcbride, J.; Gordon, A.W.; Wylie, A.R.G.; Yan, T. Methane emissions from grazing holstein-friesian heifers at different ages estimated using the sulfur hexafluoride tracer technique. Engineering 2017, 3, 753–759. [Google Scholar] [CrossRef]
- Jia, P.; Diao, Q.; Dong, L.; Liu, Z. A cow feeding channel for the greenfeed system. Chinese Patent CN20212052 3058.X, 2021.
- Jian, R.; Jia, S.; Xing, M.; Yang, Y.; Zhi, Y. Sources of greenhouse gas emissions from the livestock sector and measures to reduce them. Mod. Anim. Husb. 2022, 6, 62–64. [Google Scholar]
Compounds | Average Concentration (g/d/Animal) |
---|---|
CH4 | 70–120 |
CO2 | 700–900 |
NOx | 5–10 |
NH3 | 200–400 |
CO | 20–40 |
Categories | Principle | Types | Advantages | Disadvantages |
---|---|---|---|---|
Gas concentration monitoring | Measurement of the concentration of gas expelled from the animal. | Electrochemical detectors | Multi-gas non-specific detection High sensitivity | Long response time |
High precision [29] | Short service life [37] | |||
GC | High efficiency Reliable | Professionally operated | ||
Multi-sample collection [38] | Higher operating cost | |||
Gas leakage during use | ||||
SMOxs sensors | High sensitivity | Short service life | ||
small volume | Sensitivity to environmental factors [44] | |||
Lower operating costs | ||||
Pellistor sensors | High sensitivity | Short service life | ||
Lower operating costs | Sensitivity to environmental factors | |||
Higher selectivity [47] | ||||
FTIR | Multi-gas non-specific detection [50] | Higher operating costs | ||
NDIR | Multi-sample collection [50] | Inability to monitor enteric emissions | ||
Long-term continuous monitoring | Circadian rhythms need to be considered | |||
time-consuming [68] | ||||
TDLAS | High sensitivity | Higher operating costs [99] | ||
High precision | ||||
High anti-interference capability [70] | ||||
LMD | High sensitivity | Professionally operated | ||
No individual differences | Sensitivity to environmental factors | |||
Remote real-time monitoring | Need to keep a distance from animals [72] | |||
Laser spectral detectors | High sensitivity | Professionally operated | ||
High precision | ||||
Lower operating costs | Not tested in the mass market | |||
Gas emission monitoring | Measurement of the amount of gas emitted from environmental and animal fecal deposits | Breathing chambers | Most accurate results [87] | Professionally operated |
time-consuming [86] | ||||
Collection of all gases | Sensitivity to environmental factors | |||
Influence on animal behavior | ||||
Less efficient experiments | ||||
Higher operating costs [92] | ||||
Breathing mask | Easy to use | Higher error | ||
Influence on the daily behavior of animals [94] | ||||
Low cost | Inability to monitor enteric emissions | |||
Head breathing chamber with hopper | Automatic animal recognition [86] | Higher operating costs [92] | ||
High sensitivity |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ma, W.; Ji, X.; Ding, L.; Yang, S.X.; Guo, K.; Li, Q. Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review. Sensors 2024, 24, 4423. https://doi.org/10.3390/s24134423
Ma W, Ji X, Ding L, Yang SX, Guo K, Li Q. Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review. Sensors. 2024; 24(13):4423. https://doi.org/10.3390/s24134423
Chicago/Turabian StyleMa, Weihong, Xintong Ji, Luyu Ding, Simon X. Yang, Kaijun Guo, and Qifeng Li. 2024. "Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review" Sensors 24, no. 13: 4423. https://doi.org/10.3390/s24134423
APA StyleMa, W., Ji, X., Ding, L., Yang, S. X., Guo, K., & Li, Q. (2024). Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review. Sensors, 24(13), 4423. https://doi.org/10.3390/s24134423