Considering the role of the energy grid mix on indirect water use in dairy barns
Bibliographic record
Abstract
• Water use associated with electricity generation is substantial contributor to the water footprint of dairy farms. • Canada was used as a case study because its provinces have a wide range of electricity grid mixes. • Water use related to electricity was 3.5 L kg -1 milk on average (range: 1.4 – 5.8 L kg -1 ). • Energy conservation in barns could reduce water use by 1.0 L kg -1 milk, on average. • On-farm solar array can lower grid-electricity-related water use substantially. Water use is an important environmental concern for the dairy sector. There are two kinds of water use in the dairy sector, direct and indirect. Electricity generation (e.g., cooling water, evaporation, etc.) is an indirect use of water and a significant contributor to the overall water budget depending on how electricity is generated. In Canada, the dairy industry is distributed across 10 provinces each with a wide range of electricity generation sources in their grid mix, making it an interesting case study. For a dairy farm that uses 1021 kWh cow -1 y -1 (9.4 – 10.6 kWh per 100 kg milk, depending on the province), the average water use related to generating electricity was estimated to be 3.48 L kg -1 milk (range: 1.40 – 5.77 L kg -1 , depending on the electricity grid). Energy conservation technologies could reduce electricity use by as much as 30 % and thus reduce water use by 1.04 L kg -1 milk on average (range: 0.42 – 1.73 L kg -1 ). Installing an on-farm solar array (0.40 kWp cow -1 ; i.e. one 400-watt solar panel per cow) could lower grid-electricity-related water use by 35 – 51 % (or by 0.57 – 2.71 L kg -1 ). Solar array sized with the capacity to reach net-zero electricity is feasible and can eliminate grid-electricity-related water use. This study highlights that dairy farms can achieve substantial water savings by strategically using electricity conservation and renewables, with the magnitude depending on the electricity grid mix, a relationship that has yet to be analyzed in current literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".