Effect of green manure crops, termination method, stubble crops, and fallow on soil water, available N, and exchangeable P
Bibliographic record
Abstract
Green manure crops may have a role to play in the development of sustainable agricultural systems in the semiarid northern Great Plains of North America. This study determined the benefits of different green manure crops, seeding dates, and termination methods on soil nitrogen, phosphorus, and moisture, as well as the performance of durum wheat following green manures the following year. Field experiments were conducted at Swift Current, Saskatchewan, from 2006 to 2009. Three green manure crops [forage pea (Pisum sativum L.), chickling vetch (Lathyrus sativus L.), and black lentil (Lens culinaris Medik.)] were seeded in May, June, and July, and terminated at full bloom using glyphosate, rototilling or by frost. Other treatments included summerfallow and stubble of selected crops harvested for grain or silage. Different green manure crops, seeding dates or termination methods had similar effects on soil moisture, available N, and exchangeable P at termination or the following spring. These effects of green manure management on soil residual characteristics were comparable to those observed under summerfallow, but higher than those on grain or silage stubble. Therefore, green manure is a viable alternative to summerfallowing and could be seeded any time during the growing season. If seeded late, green manure could be terminated by frost, thus saving on costs.
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 | 0.001 | 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".