Modelling enteric methane emissions from milking dairy cows with Bayesian networks

Huiru Zheng; Haiying Wang; Tianhai Yan

doi:10.1109/BIBM.2016.7822764

Modelling enteric methane emissions from milking dairy cows with Bayesian networks

Huiru Zheng
, Haiying Wang
, Tianhai Yan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (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.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Publisher	IEEE
Pages	1635-1640
Number of pages	6
ISBN (Electronic)	978-1-5090-1611-2
DOIs	https://doi.org/10.1109/BIBM.2016.7822764
Publication status	Published (in print/issue) - 17 Jan 2017
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

Bayesian networks
Causal influence
Methane emission

Access to Document

10.1109/BIBM.2016.7822764

Cite this

@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.",

keywords = "Bayesian networks, Causal influence, Methane emission",

author = "Huiru Zheng and Haiying Wang and Tianhai Yan",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822764",

language = "English",

pages = "1635--1640",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "IEEE",

address = "United States",

note = "2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

}

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, IEEE, 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

TY - GEN

T1 - Modelling enteric methane emissions from milking dairy cows with Bayesian networks

AU - Zheng, Huiru

AU - Wang, Haiying

AU - Yan, Tianhai

PY - 2017/1/17

Y1 - 2017/1/17

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.

KW - Bayesian networks

KW - Causal influence

KW - Methane emission

UR - https://www.scopus.com/pages/publications/85013347493

U2 - 10.1109/BIBM.2016.7822764

DO - 10.1109/BIBM.2016.7822764

M3 - Conference contribution

AN - SCOPUS:85013347493

SP - 1635

EP - 1640

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

PB - IEEE

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Modelling enteric methane emissions from milking dairy cows with Bayesian networks

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this