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Record W3202008421 · doi:10.13031/aea.14621

Review: Dairy Farm Electricity Use, Conservation, and Renewable Production—A Global Perspective

2021· article· en· W3202008421 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Engineering in Agriculture · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsCanadian Wood CouncilCanadian Animal Health InstituteUniversity of WindsorAgriculture and Agri-Food Canada
Fundersnot available
KeywordsElectricityRenewable energyAgricultural scienceEnvironmental scienceMilkingAutomatic milkingPastureBarnAgricultural economicsBusinessEngineeringGeographyEconomicsBiologyForestryCivil engineeringElectrical engineering

Abstract

fetched live from OpenAlex

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 (EUI c ) 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 (EUI m ) 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, EUI m averaged 7.7 kWh 100 kg -1 and EUI c 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 EUI m 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.196
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it