A Novel Mixed Effects Random Forest Approach for Predicting Dairy Cattle Methane Emissions

Stephen Ross, Haiying Wang, Huiru Zheng, Tianhai Yan, Masoud Shirali

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

Methane (CH4) emissions produced by dairy cattle (DC) are a key contributor to global warming. To assess the effectiveness of strategies designed to mitigate CH4 emissions, complex and expensive recording equipment is required. Therefore, the use of predictive models based on animal information provides a more accessible alternative. Traditionally, Statistical (SA) methods have been employed in the prediction of DC CH4 emissions. However due to the smart farming revolution, the scale and variety of complex animal information now available for the prediction of DC CH4 emissions has grown exponentially, and within them are likely to exist non-linear relationships which these traditional SA models may struggle to capture. Therefore, this research aims to explore if Machine Learning (ML) models are a viable alternative for the prediction of DC CH4 emissions, as they can handle and extract these inevitable non-linear relationships present within today's large, heterogeneous datasets. In this research, we compared a traditional SA method, a Linear Mixed Effects (ME) model, with an original ML method, a Random Forest (RF) model, as well as a novel SA/ML hybrid method, a Mixed Effects Random Forest (MERF) model, in the prediction of CH4 emissions (CH4 g/d) produced by DC across 32 experiments. The ML RF model was able to challenge the traditional SA ME model in the prediction of DC CH4 emissions, achieving a Root Mean Square Prediction Error (RMSPE) and Concordance Correlation Coefficient (CCC) of 52.73 CH4 g/d and 0.70 respectively, compared to a ME model’s 53.90 CH4 g/d and 0.71. When both the ME and RF models were combined within the novel SA/ML hybrid MERF model, a lower RMSPE and higher CCC were achieved than by each of its composite parts in isolation, 51.87 CH4 g/d and 0.73 respectively. These results demonstrate the potential of ML in the prediction of DC CH4 emissions, particularly when hybridised alongside traditional SA methods.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsXingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
PublisherIEEE
Pages3125-3132
Number of pages8
ISBN (Electronic)979-8-3503-3748-8
ISBN (Print)979-8-3503-3749-5
DOIs
Publication statusPublished online - 18 Jan 2024
EventIEEE BIBM Workshop on Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM 2023) - Istanbul, Istanbul, Turkey
Duration: 5 Dec 20238 Dec 2023
https://computing.ulster.ac.uk/ZhengLab/MABM2023/index.html

Publication series

NameProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

ConferenceIEEE BIBM Workshop on Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM 2023)
Abbreviated titleMABM
Country/TerritoryTurkey
CityIstanbul
Period5/12/238/12/23
Internet address

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Dairy Cattle
  • Methane
  • Linear Mixed Effects Model
  • Machine Learning
  • Random Forest
  • Mixed Effects Random Forest
  • Prediction
  • Statistical Analysis

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