Review: Dairy Farm Electricity Use, Conservation, and Renewable Production—A Global Perspective
Bibliographic record
Abstract
Highlights Studies of electricity use were reviewed, representing five continents. Considering all farm types, electricity use averaged 7.7 kWh 100 kg-1 milk and 612 kWh cow-1 y-1. Pasture-based dairy systems used less electricity than barn-based systems (475 vs. 769 kWh cow-1 y-1). By combining several conservation technologies there is potential to reduce electricity demand by one-third. Dairy farms can reach net zero electricity by combining renewable energy production with conservation. Abstract. This review summarizes electricity use on dairy farms, with a focus on how energy is used, energy use indices (EUI), conservation strategies, and generation of renewable energy to reach net zero. EUI of electricity consumption varied between the identified studies primarily based on farm management system (confined, pasture-based), housing type (tie-stall, free-stall), and region (North America, Europe, Asia, Africa, Oceania). The highest electricity usage was associated with milking and milk cooling systems, which, on average, accounted for 23% and 22% of total electricity use, respectively. Energy use scaled per cow (EUIc) was lower, on average, for pasture-based dairy systems than for confined systems (475 vs. 769 kWh cow-1 y-1). Considering milk production, the average EUI scaled to milk (EUIm) was lower for pasture-based systems (6.6 kWh 100 kg-1) than for confined systems 9.2 kWh 100 kg-1. Considering all non-irrigated farm types, EUIm averaged 7.7 kWh 100 kg-1 and EUIc averaged 612 kWh cow-1 y-1. There was a large range of EUI, with higher values associated with automated milking systems and irrigation. Electricity consumption by the global dairy sector (excluding irrigation) was estimated using the average EUIm at approximately 64.2 TWh y-1. The main conservation technologies include variable speed drives (milk vacuum pumps, milking systems, fans), pre-cool heat exchangers, refrigeration heat recovery systems, energy-efficient light fixtures (compact fluorescents, light emitting diodes), and efficient ventilation (high-volume low-speed fans). Theoretical savings of up to 32% overall could be achieved by combining several technologies. Feedback from electricity monitoring can inform dairy farmers of their energy use pattern to guide decisions to reduce consumption. Tools for predicting energy use and related costs on dairy farms, which can indicate potential energy savings from operational changes, were reviewed. By combining conservation methods with renewable energy from biogas or solar, many dairy farms can produce enough electricity to reach net zero electricity. For example, a hypothetical barn-based 250 milking-cow dairy farm consumed 1021 kWh cow-1 y-1, on average, and could produce approximately 1095 kWh cow-1 y-1 using a biodigester or 960 kWh cow-1 y-1 using rooftop photovoltaic solar panels. Keywords: Conservation, Dairy footprint, Electricity use, Electricity partitioning, Energy utilization index, Renewable energy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".