Surface irrigation adapted to the land spreading of dairy farm effluent
Bibliographic record
Abstract
An important number of Canadian dairy farms manage their manure as solids and in doing so, must handle large volumes of manure seepages and milk house wastewater (dairy farm effluent-DFE). The present project adapted surface irrigation as a more economical and sustainable method of disposing of this large volume of DFE on cropped land near their storage facility. The experimental surface irrigation system consisted of a gated pipe installed perpendicular to the slope of the field allowing the discharged DFE to run down the slope. The adaptation of the system and the measurement of its environmental impact were conducted on two dairy farms, A and B, in the region South West of Montreal where their DFE were characterized. In 2003 and 2004, DFE was applied on one of two 0.5 and 0.3ha plots, on each farm, to observe losses through the subsurface drainage system, by means of sampling wells, and effects on soil nutrient levels. The DFE collected in 2002 and 2003 had a lower nutrient content than that collected in 2004 because of higher precipitations. The DFE generally contained between 150-500 mg/L of TKN, 15 to 40 mg/L of TP and 500 to 700 mg/L of TK. DFE losses through the subsurface drainage system were observed on both farms during each irrigation test. Nevertheless, outlet losses were observed only when irrigating under wet soil conditions or when applying more than 50mm of DFE. Outlet losses represented at the most 1.2% of the total DFE volume applied and 0.32% of the nutrient and bacterial loads. Although only 65 to 75% of the soil surface was covered by the applied DFE, the irrigation sessions did provide some additional soil moisture for crops, increasing yield by 31% in 2004. Once absorbed by the soil, the applied DFE did not increase the soil nutrient level and variability in the presence of crop. Thus, the DFE contributed to the irrigation and fertilization of the plots. Surface irrigation to spread low nutrient DFE, as compared to the conventional tanker system reduced the application costs from $3.05/m3, to $0.95/m3.
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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 | 0.001 |
| 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".