Fertilizing with composted cattle manure to meet agro-environmental targets in continuous corn and corn–soybean agroecosystems in southern Quebec
Bibliographic record
Abstract
Balancing nutrient inputs from manure and supplemental inorganic fertilizers with crop requirements should achieve crop yield goals and agro-environmental targets. We asked if composted dairy cattle manure, alone or in combination with inorganic fertilizers, could sustain the corn and soybean yields, without increasing the residual soil nitrogen (N) and soil phosphorus (P) saturation of a sandy-loam soil in southern Quebec, Canada. Cropping systems were continuous silage corn and corn–soybean rotation, with each phase of the rotation grown every year, for 5 yr. Cropping systems were amended with compost at rates of 0, 15, 30, and 45 Mg (wet weight)·ha−1, and they received supplemental inorganic fertilizer to supply 200 kg N·ha−1·yr−1 to corn while assuring that corn and soybean received 45 kg P·ha−1·yr−1 and 125 kg potassium (K)·ha−1·yr−1 from compost and inorganic fertilizer. Crop yields were similar in compost-amended and inorganically fertilized plots. Corn yield was limited by N availability, and there was no P or K limitation in corn and soybean. Soil P saturation in the 0–15 cm layer exceeded the agro-environmental limit of 13.1% P/Al for a sandy-loam soil in Quebec, indicating that the P input exceeded crop uptake. Residual soil N concentration was affected more by the crop than the fertilizer source, with greater residual soil N in plots with higher grain production. Crop-induced priming can explain how N fertilizer cycles through the soil microbial community and is gradually mineralized during the growing season, and why crop senescence may trigger residual soil N release after harvest.
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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 | 0.001 | 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) | 0.001 | 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".