Decomposition of crop residues under conventional and zero tillage
Bibliographic record
Abstract
Field experiments were conducted to determine decomposition patterns of red clover (Trifolium pratense), field pea (Pisum sativum), canola (Brassica rapa) and wheat (Triticum aestivum) residues under zero and conventional tillage. Crop residue amounts produced in 2 trial years ranged from 1.6 t ha-1 for monoculture wheat to 6.05 t ha-1 for peas, and tillage had no effect. The extent of dry matter (DM) decomposition was usually less under zero than under conventional tillage, e.g., 31 to 41% of canola DM decomposed under zero tillage while 41 to 50% decomposed under conventional tillage in 12 mo. Corresponding percentages for other residues under zero and conventional tillage, respectively, were: 65 and 75% for clover, 43 and 45 to 55% for pea, and 27 and 40% for wheat. The rate and extent of DM decomposition were positively correlated with N and P concentrations, and negatively correlated with C/N, C/P, lignin/P and lignin/K ratios. These decomposition patterns have implications for nutrient release and soil cover. Rapid decomposition is not necessarily desirable because the nutrients released are subject to losses in soil. Key words: Biological soil quality, crop residue quality, crop rotation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".