Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses
The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sust...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/5/1288 |
| _version_ | 1817990308963549184 |
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| author | Philip Shine John Upton Paria Sefeedpari Michael D. Murphy |
| author_facet | Philip Shine John Upton Paria Sefeedpari Michael D. Murphy |
| author_sort | Philip Shine |
| collection | DOAJ |
| description | The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg<sup>−1</sup> Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg<sup>−1</sup> Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices. |
| first_indexed | 2024-04-14T00:57:44Z |
| format | Article |
| id | doaj.art-b8948a465c384c0a8c32cbcd3cbb9888 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T00:57:44Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b8948a465c384c0a8c32cbcd3cbb98882022-12-22T02:21:32ZengMDPI AGEnergies1996-10732020-03-01135128810.3390/en13051288en13051288Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and AnalysesPhilip Shine0John Upton1Paria Sefeedpari2Michael D. Murphy3Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork T12 P928, IrelandAnimal and Grassland Research and Innovation Centre, Teagasc Moorepark Fermoy, Cork P61 C996, IrelandWageningen Livestock Research, Wageningen University and Research, 6708 WD Wageningen, The NetherlandsDepartment of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork T12 P928, IrelandThe global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg<sup>−1</sup> Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg<sup>−1</sup> Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices.https://www.mdpi.com/1996-1073/13/5/1288dairyenergyreviewmodellingefficiencysustainable agriculturemachine-learning |
| spellingShingle | Philip Shine John Upton Paria Sefeedpari Michael D. Murphy Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses Energies dairy energy review modelling efficiency sustainable agriculture machine-learning |
| title | Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses |
| title_full | Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses |
| title_fullStr | Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses |
| title_full_unstemmed | Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses |
| title_short | Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses |
| title_sort | energy consumption on dairy farms a review of monitoring prediction modelling and analyses |
| topic | dairy energy review modelling efficiency sustainable agriculture machine-learning |
| url | https://www.mdpi.com/1996-1073/13/5/1288 |
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