A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2019-01-01
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| Series: | Animal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731118002550 |
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| author | R. Muñoz-Tamayo J.F. Ramírez Agudelo R.J. Dewhurst G. Miller T. Vernon H. Kettle |
| author_facet | R. Muñoz-Tamayo J.F. Ramírez Agudelo R.J. Dewhurst G. Miller T. Vernon H. Kettle |
| author_sort | R. Muñoz-Tamayo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T06:09:49Z |
| format | Article |
| id | doaj.art-a89d5cafc37f47d38d293e08a1f81c86 |
| institution | Directory Open Access Journal |
| issn | 1751-7311 |
| language | English |
| last_indexed | 2024-12-17T06:09:49Z |
| publishDate | 2019-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj.art-a89d5cafc37f47d38d293e08a1f81c862022-12-21T22:00:40ZengElsevierAnimal1751-73112019-01-0113611801187A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattleR. Muñoz-Tamayo0J.F. Ramírez Agudelo1R.J. Dewhurst2G. Miller3T. Vernon4H. Kettle5UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, 75005 Paris, FranceUniversidad de Antioquia – UdeA, Facultad de Ciencias Agrarias, Grupo de Investigación en Ciencias Agrarias – GRICA, Ciudadela de Robledo, Carrera 75N° 65·87, Medellín, ColombiaFuture Farming Systems, SRUC, West Mains Road, Edinburgh EH9 3JG, UKFuture Farming Systems, SRUC, West Mains Road, Edinburgh EH9 3JG, UKBiomathematics and Statistics Scotland (BioSS), Kings Buildings, Edinburgh EH9 3FD, UKBiomathematics and Statistics Scotland (BioSS), Kings Buildings, Edinburgh EH9 3FD, UKLarge 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.http://www.sciencedirect.com/science/article/pii/S1751731118002550greenhouse gasmethanemodellingruminantprecision farming |
| spellingShingle | R. Muñoz-Tamayo J.F. Ramírez Agudelo R.J. Dewhurst G. Miller T. Vernon H. Kettle A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle Animal greenhouse gas methane modelling ruminant precision farming |
| title | A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle |
| title_full | A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle |
| title_fullStr | A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle |
| title_full_unstemmed | A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle |
| title_short | A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle |
| title_sort | parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle |
| topic | greenhouse gas methane modelling ruminant precision farming |
| url | http://www.sciencedirect.com/science/article/pii/S1751731118002550 |
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