A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

R Munoz-Tamayo, JF Ramirez Agudelo, RJ Dewhurst, G Miller, T Vernon, H Kettle

Research output: Contribution to journalArticle

Abstract

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.
Original languageEnglish
Pages (from-to)1180-1187
Number of pages8
JournalAnimal
Volume13
Issue number6
Early online date18 Oct 2018
DOIs
Publication statusPrint publication - Jun 2019

Fingerprint

Methane
Sensors
Time measurement
Beef
Bins
Ordinary differential equations
Agriculture
Animals
Mathematical models

Keywords

  • Greenhouse gas
  • Methane
  • Modelling
  • Precision farming
  • Ruminant

Cite this

Munoz-Tamayo, R ; Ramirez Agudelo, JF ; Dewhurst, RJ ; Miller, G ; Vernon, T ; Kettle, H. / A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle. In: Animal. 2019 ; Vol. 13, No. 6. pp. 1180-1187.
@article{9f1c9086477545969b944871d813d7b1,
title = "A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle",
abstract = "Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.",
keywords = "Greenhouse gas, Methane, Modelling, Precision farming, Ruminant",
author = "R Munoz-Tamayo and {Ramirez Agudelo}, JF and RJ Dewhurst and G Miller and T Vernon and H Kettle",
year = "2019",
month = "6",
doi = "10.1017/S1751731118002550",
language = "English",
volume = "13",
pages = "1180--1187",
journal = "Animal",
issn = "1751-7311",
publisher = "Cambridge University Press",
number = "6",

}

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle. / Munoz-Tamayo, R; Ramirez Agudelo, JF; Dewhurst, RJ; Miller, G; Vernon, T; Kettle, H.

In: Animal, Vol. 13, No. 6, 06.2019, p. 1180-1187.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

AU - Munoz-Tamayo, R

AU - Ramirez Agudelo, JF

AU - Dewhurst, RJ

AU - Miller, G

AU - Vernon, T

AU - Kettle, H

PY - 2019/6

Y1 - 2019/6

N2 - Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.

AB - Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.

KW - Greenhouse gas

KW - Methane

KW - Modelling

KW - Precision farming

KW - Ruminant

U2 - 10.1017/S1751731118002550

DO - 10.1017/S1751731118002550

M3 - Article

C2 - 30333069

VL - 13

SP - 1180

EP - 1187

JO - Animal

JF - Animal

SN - 1751-7311

IS - 6

ER -