Energy consumption on dairy farms: A review of monitoring, prediction modelling, and analyses

Philip Shine, John Upton, Paria Sefeedpari, Michael D. Murphy*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Article number1288
JournalEnergies
Volume13
Issue number5
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • Dairy
  • Efficiency
  • Energy
  • Machine-learning
  • Modelling
  • Review
  • Sustainable agriculture

Fingerprint

Dive into the research topics of 'Energy consumption on dairy farms: A review of monitoring, prediction modelling, and analyses'. Together they form a unique fingerprint.

Cite this