Surface irrigation of dairy farm effluent. Part II : System design and operation
Bibliographic record
Abstract
Dairy farms with as many as 200 cows still handle their wastewaters and solid manures separately. Because of the large volume produced and their low nutrient load, these dairy farm effluents (DE) are costly and time consuming to land spread using conventional equipment, such as the tanker. The purpose of this study was to test the equipment and cost to determine the cost of the equipment adapted for the simplified surface irrigation of DE and to establish best management practices to reduce risks of groundwater contamination. The project was conducted on two dairy farms in South Western of Montreal, Canada, where typical DE were applied to irrigated plots of 0.5 and 0.3 ha, respectively, and the groundwater quality was compared to a control plot of the same size. Groundwater quality was monitored for nutrients (total nitrogen, total phosphorus, total potassium and pH) and bacterial counts (total coliforms, faecal coliforms, and faecal streptococci). A manure pump and conventional water irrigation pipes were satisfactory in irrigating with the DE without clogging as long as the DE was collected in a tank separate from that of the solid manure. During all applications, subsurface seepage losses occurred, but these would not be lost to the watercourse when applied in quantities respecting irrigation guidelines and on soils where the groundwater table was at or below the depth of the subsurface drains. Nevertheless, these seepage losses represented less than 1% of the total volume of DE applied, and the seepage nutrient and bacterial load was generally less than half of that of the irrigated DE.\nThe surface irrigation system reduced the cost of land spreading DE from CAN $3.25m3 (conventional tanker) to CAN $1.10m3 (surface irrigation). The heavy total potassium load of the DE requires the rotation of the irrigation plot, on an annual basis.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".