Nitrogen release during decomposition of crop residues under conventional and zero tillage
Bibliographic record
Abstract
The litter-bag method was used in field experiments to determine nitrogen (N) loss patterns from decomposing red clover (Trifolium pratense) green manure (GM), field pea (Pisum sativum), canola (Brassica rapa) and monoculture wheat (Triticum aestivum) residues under conventional and zero tillage. Nitrogen contained in crop residues ranged from 10 kg ha-1 in wheat under both tillage systems to 115 kg ha-1 in clover GM under zero tillage. The patterns of N loss (i.e., release), particularly from GM residues, over 52-wk periods varied with tillage, i.e., residues lost N more rapidly under conventional tillage than under zero tillage in the first 5 to 10 wk after residue placement. Net N immobilization was sometimes observed, particularly under zero tillage. Where net N release occurred, it ranged from 22% of wheat N under conventional tillage to 71% for clover N under conventional tillage; it was positively correlated with residue N concentration and microbial activity, and negatively correlated with C:N and lignin:N ratios in one study period. The amounts of N released were 2 kg ha-1 from wheat, 10 to 25 kg ha-1 from canola, 4 to 18 kg ha-1 from pea, and 46 to 69 kg ha-1 from GM residues. Therefore, when grain is harvested, the remaining crop residues do not release much N to the soil in the first year of decomposition, but the N stored in soil is presumably released in subsequent years. Key words: Crop residues, crop rotation, N mineralization, organic soil amendments
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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 | 0.000 | 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 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".