No‐Till Green Manure Termination Influences Soil Organic Carbon Distribution and Dynamics
Bibliographic record
Abstract
Core Ideas Soil organic C pools increased under no‐till green manure termination, and this increase persisted over 3 yr following the green manure termination method. Total organic C and particulate organic matter C showed a stronger response to the green manure termination method than permanganate oxidizable C. Tillage effects in labile soil organic C pools may be harder to detect in organic systems with high levels of C inputs. There is continued interest in adopting no‐till practices in organic agriculture. One way to decrease tillage is to implement no‐till termination of green manures. Studies on this novel approach have focused primarily on cash crop performance and weed control. The objective of this study was to examine soil organic C distribution and dynamics as influenced by different methods of green manure termination of a spring‐planted green manure. In this green manure–wheat (Triticum aestivum L.)–fall rye (Secale cereal L.)–soybean [Glycine max (L.) Merr.] rotation three green manure termination methods were compared (no‐till crop roller, spring tillage, and fall tillage). A mixture of hairy vetch (Vicia villosa Roth)–oat (Avena sativa L.) was seeded in late May in two trials, the first commencing in 2013 and the second in 2014. Soil organic C was monitored for 3 yr after green manure termination. In Trial 2, total organic C was greater under no‐till green manure termination compared to fall and spring‐till termination (by 2.4 ± 1.2 Mg C ha−1 and 2.3 ± 1.3 Mg C ha−1, respectively) and the effect remained significant in all 3 yr. In Trial 1, particulate organic matter C was stratified in no‐till and concentrated at the soil surface (0–5cm) and higher overall in no‐till in Trial 2. Overall soil organic C was higher under no‐till green manure termination and the more labile pool of POXc appeared less responsive to termination treatments.
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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 | 0.001 | 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".