Soil organic carbon and nitrogen pools as affected by compost applicationsto a sandy-loam soil in Québec
Bibliographic record
Abstract
Compost contributes plant-available nutrients for crop production and adds partially decomposed carbon (C) to the soil organic carbon (SOC) pool. The effect of compost applications and other agricultural practices on SOC and total nitrogen (N) pools was determined in a sandy-loam Humic Gleysol at the Research Farm of McGill University, Ste-Anne-de-Bellevue, Quebec. Experimental plots with continuous silage corn (Zea mays L.) and silage corn-soybean (Glycine max L. Merr.) production were under conventional tillage (CT) or no-tillage (NT) management. Composted cattle manure was applied each spring at rates of 0, 5, 10 and 15 Mg (dry weight) ha -1 and supplemental NPK fertilizers were added to meet crop requirements. The C input from crop residues was affected by tillage, crop rotations and compost application, but differences in the SOC and total N pools were due to compost applications. After 5 yr, compost-amended plots gained 1.35 to 2.02 Mg C ha -1 yr -1 in the SOC pool and 0.18 to 0.24 Mg N ha -1 yr -1 in the total N pool, as compared with initial pool sizes when the experiment was initiated. These gains in SOC and total N were achieved with agronomic rates of compost and supplemental NPK fertilizers, selected to match the phosphorus requirements of silage corn. Such judicious use of compost has the potential to increase the SOC and total N pools in agroecosystems under annual crop production. Key words: Composted cattle manure, corn silage, mineral fertilizer, plant-available nitrogen, soil organic carbon
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".