The environmental risks linked to different manure application periods /
Bibliographic record
Abstract
More intensive production of hogs and cattle in Quebec during the past decade has benefited local economies, but led to over-fertilization of agricultural soils and eutrophication of waterways. Provincial ministries responded to this issue by developing regulations to control manure applications. The objective of this thesis was to determine the environmental risk associated with applying farm manure in the late fall. Spreading manure in fall after harvesting corn was a common practice for many agricultural producers in Quebec, but this period is now viewed as very risky, having more negative environmental consequences than other manure application periods. This two-year study used common diagnostic tools to compare the fertilization efficiency of solid dairy farm manure (DFM) spread in early fall, late fall and spring on a heavy clay soil used for corn production. In the first year, when DFM was the only nutrient source, there was no difference in corn yield that could be attributed to the manure application period. In the second year, each DFM plot was split and six levels of inorganic fertilizer (from 55 to 240 kg N ha-1) were applied ("Strip Split Plot") after planting. Corn tissue analysis (chlorophyll content, leaf N content at silking, cornstalk NO3 concentration) indicated that more residual N was supplied from late fall manure application than other manure application periods. Monitoring of soil NO3-N concentrations indicated that most of the NO3-N migration through the soil profile occurred after the early fall manure application. Late fall manure application appears to be the most efficient at supplying N for corn production, without deleterious environmental impacts, when DFM is applied to a heavy clay soil.
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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 | 0.001 | 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 | 0.000 |
| 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".