Fertilization and Mowing Effects on Unimproved Mixed‐Species Hayfields in Quebec, Canada
Bibliographic record
Abstract
The objective of this experiment was to evaluate the response of two unimproved mixed‐species hayfields in Quebec, Canada, to management regimes of high fertilization and intensive mowing. Dry matter and nutrient yields, tissue nutrient concentrations, and soil nutrient pools were determined in Bromus – Poa and Pleum – Lolium hayfields fertilized with inorganic fertilizer, liquid dairy manure, or no fertilizer and mown at different intensities (every 2 wk or unmown, during a 12‐wk period). Dry matter and nutrient yields, and tissue nutrient concentrations were similar in plots receiving inorganic and dairy manure fertilizers, but the dry matter yield was 0.6 to 2.8 Mg ha −1 higher in fertilized plots than the unfertilized control. Repeated mowing reduced dry matter yield by 1.5 to 2.7 Mg ha −1 , however, tissue nutrient concentration and nutrient yield were greater in mown than unmown plots. The apparent N recovery in a mown, fertilized Pleum – Lolium hayfield was greater than 100%, as the nutrient yields (143 kg N ha −1 ) were greater than the N fertilizer input of 75 kg N ha −1 Soil NO 3 − and microbial biomass N concentration were significantly ( P < 0.05, Tukey test) lower in fertilized plots that were mown every 2 wk than unmown during the study period. Frequent mowing may be a management option that can reduce soil residual N and thus limit NO 3 − leaching from mixed‐species hayfields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".