A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration

J.R. Upton, M. Murphy, L. Shallo, P.W.G. Groot Koerkamp, I.J.M. de Boer

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)

Abstract

Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1 yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.
Original languageEnglish
Pages (from-to)4973-4984
JournalJournal of Dairy Science
Volume97
Issue number8
DOIs
Publication statusPublished - 2014

Keywords

  • artificial neural-networks
  • greenhouse-gas emissions
  • lactation curve
  • milk-production
  • cows
  • systems
  • yield

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