Modelling enteric methane emissions from milking dairy cows with Bayesian networks

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

    1 Citation (Scopus)

    Abstract

    As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.

    LanguageEnglish
    Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
    Pages1635-1640
    Number of pages6
    ISBN (Electronic)978-1-5090-1611-2
    DOIs
    Publication statusPublished - 17 Jan 2017
    Event2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
    Duration: 15 Dec 201618 Dec 2016

    Conference

    Conference2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
    CountryChina
    CityShenzhen
    Period15/12/1618/12/16

    Fingerprint

    Dairies
    Methane
    Bayesian networks
    Northern Ireland
    Ruminants
    Greenhouse gases
    Gases
    Food

    Keywords

    • Bayesian networks
    • Causal influence
    • Methane emission

    Cite this

    Zheng, H., Wang, H., & Yan, T. (2017). Modelling enteric methane emissions from milking dairy cows with Bayesian networks. In Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1635-1640). [7822764] https://doi.org/10.1109/BIBM.2016.7822764
    Zheng, Huiru ; Wang, Haiying ; Yan, Tianhai. / Modelling enteric methane emissions from milking dairy cows with Bayesian networks. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. 2017. pp. 1635-1640
    @inproceedings{63010558e2f3457a85c11374d84fdb2a,
    title = "Modelling enteric methane emissions from milking dairy cows with Bayesian networks",
    abstract = "As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.",
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    Zheng, H, Wang, H & Yan, T 2017, Modelling enteric methane emissions from milking dairy cows with Bayesian networks. in Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822764, pp. 1635-1640, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822764

    Modelling enteric methane emissions from milking dairy cows with Bayesian networks. / Zheng, Huiru; Wang, Haiying; Yan, Tianhai.

    Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. 2017. p. 1635-1640 7822764.

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

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    N2 - As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.

    AB - As one of the potent greenhouse gases, methane emission from ruminants has been intensively studied over the past decades. Various regression-based models have been applied to examine factors affecting enteric methane emission. Based on Bayesian networks, this paper proposes an alternative network-based approach to model the relationship among factors affecting enteric methane emissions from milking cows. It was evaluated on the dataset consisting of 934 milking dairy cows collected at Agri-Food and Biosciences Institute, Northern Ireland. The preliminary results demonstrated that the proposed model has a great potential to capture the complex relationship among factors and establish causal influence among predictors. To the best of our knowledge, this is the first study to use Bayesian networks to model causal influence among factors associated with enteric methane emission from milking cows.

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    Zheng H, Wang H, Yan T. Modelling enteric methane emissions from milking dairy cows with Bayesian networks. In Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. 2017. p. 1635-1640. 7822764 https://doi.org/10.1109/BIBM.2016.7822764