The effect of water quality on cattle performance on pasture
Bibliographic record
Abstract
In western Canada, dugouts are the primary source of water for beef cattle during the summer months. Over time, drought conditions and/or direct access by grazing animals have a negative effect on water constituents and overall water quality. A study was conducted to determine the effects of improvements in water quality on cattle performance. The effect of pasture water quality on weight gain of beef cattle was assessed with 44 Hereford yearling steers over 5 years and 40 Angus cow–calf pairs over 3 years. From 1999 to 2003, cattle were allocated to 1 of 4 treatments, which comprised untreated dugout water pumped to a trough, aerated water pumped to a trough, and coagulated and chlorinated water pumped to a trough, all compared with direct access by livestock to the water source. Data were collected on livestock weight gains, water consumption, fecal parasites, environmental conditions, water chemistry, biological constituents, and forage production and quality. Water treatment by aeration or coagulation tended to improve steer weight gains (P < 0.05) over untreated water from a dugout in 3 of 5 years. Daily weight gains tended to be improved slightly by simply pumping water to a trough without treatment. Water aerated and pumped to a trough in early summer tended to produce greater (P < 0.05) weight gains in calves than those drinking directly from the dugout. The effect of treatment on improving cattle weight gains appeared to be related to improved water palatability, which increased water and feed consumption. Water chemistry and biological constituents analysed did not identify significant differences among treatments. These results suggest that improving water quality with aeration and pumping to a trough will improve weight gain 9–10% over a 90-day grazing period in most years.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".