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

Published online by Cambridge University Press:  18 October 2018

R. Muñoz-Tamayo Open the ORCID record for R. Muñoz-Tamayo [Opens in a new window] ,
J. F. Ramírez Agudelo ,
R. J. Dewhurst ,
G. Miller ,
T. Vernon  and
H. Kettle
R. Muñoz-Tamayo*
Affiliation:
UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005Paris, France
J. F. Ramírez Agudelo
Affiliation:
Universidad de Antioquia – UdeA, Facultad de Ciencias Agrarias, Grupo de Investigación en Ciencias Agrarias – GRICA, Ciudadela de Robledo, Carrera 75N° 65·87, Medellín, Colombia
R. J. Dewhurst
Affiliation:
Future Farming Systems, SRUC, West Mains Road, EdinburghEH9 3JG, UK
G. Miller
Affiliation:
Future Farming Systems, SRUC, West Mains Road, EdinburghEH9 3JG, UK
T. Vernon
Affiliation:
Biomathematics and Statistics Scotland (BioSS), Kings Buildings, EdinburghEH9 3FD, UK
H. Kettle
Affiliation:
Biomathematics and Statistics Scotland (BioSS), Kings Buildings, EdinburghEH9 3FD, UK
*
†E-mail: [email protected]
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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 gasmethanemodellingruminantprecision farming
Type
Research Article
Information
animal , Volume 13 , Issue 6 , June 2019 , pp. 1180 - 1187
Copyright
© The Animal Consortium 2018 

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