Water use dynamics with conventional and automated milking systems on a dairy farm
Bibliographic record
Abstract
An increasing proportion of dairy farms are adopting automated milking systems (AMS).At the same time, the dairy industry is actively exploring strategies to reduce the water footprint of milk production.Automated milking systems have different cleaning procedures than traditional conventional milking systems (CMS), so the effect on water use is a potentially important consideration.Previous studies of AMS approximately a decade ago showed ~50% more direct water use compared with CMS; however, those studies were based on older technology and compared different farms.The current study measured whole-farm water use partitioned into drinking (for consumption) and service water (for cleaning) on a dairy farm in Eastern Canada.The dairy farm milked 110 to 120 cows initially using CMS and then changing to AMS. Results showed that the pattern of water use changed with the AMS to increased peak drinking water and decreased peak service water use.Cows produced more milk and consumed more water with the AMS.Overall service water use per cow decreased from 30.9 ± 7.7 L•d -1 with the CMS to 22.5 ± 4.0 L•d -1 with the AMS, and overall service water use per unit of milk decreased from 0.98 ± 0.25 L•L -1 with the CMS to 0.68 ± 0.13 L•L -1 with the AMS.Daily service water use was also more consistent with the AMS (CV = 17.9%) versus the CMS (CV = 24.8%).With the AMS, the farm used significantly more water, produced significantly more milk, and achieved significantly better water use efficiency per liter of milk.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".