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Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

Author

Listed:
  • Philip Shine

    (Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork T12 P928, Ireland)

  • John Upton

    (Animal and Grassland Research and Innovation Centre, Teagasc Moorepark Fermoy, Cork P61 C996, Ireland)

  • Paria Sefeedpari

    (Wageningen Livestock Research, Wageningen University and Research, 6708 WD Wageningen, The Netherlands)

  • Michael D. Murphy

    (Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork T12 P928, Ireland)

Abstract

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 −1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg −1 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.

Suggested Citation

  • Philip Shine & John Upton & Paria Sefeedpari & Michael D. Murphy, 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses," Energies, MDPI, vol. 13(5), pages 1-25, March.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1288-:d:330869
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    References listed on IDEAS

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    1. Kraatz, Simone, 2012. "Energy intensity in livestock operations – Modeling of dairy farming systems in Germany," Agricultural Systems, Elsevier, vol. 110(C), pages 90-106.
    2. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    3. Breen, M. & Murphy, M.D. & Upton, J., 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms," Applied Energy, Elsevier, vol. 242(C), pages 1697-1711.
    4. Giuseppe Todde & Lelia Murgia & Maria Caria & Antonio Pazzona, 2018. "A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 2: Investigation and Modeling of Indirect Energy Requirements," Energies, MDPI, vol. 11(2), pages 1-13, February.
    5. Thomassen, M.A. & van Calker, K.J. & Smits, M.C.J. & Iepema, G.L. & de Boer, I.J.M., 2008. "Life cycle assessment of conventional and organic milk production in the Netherlands," Agricultural Systems, Elsevier, vol. 96(1-3), pages 95-107, March.
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    7. Philip Shine & Michael D. Murphy & John Upton, 2020. "A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming," Sustainability, MDPI, vol. 12(17), pages 1-20, September.
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