The influence of rotation, tillage and row spacing on near-surface soil temperature for winter wheat in southern Alberta
Bibliographic record
Abstract
Soil and crop management practices and their effects on surface residue levels can modify soil temperature. Our study investigated the effect of rotation, tillage and row spacing on near-surface (0.025 m) soil temperature under winter wheat (Triticum aestivum L.) in 1993-1994 and 1994-1995. The main treatment was winter wheat rotation: continuous winter wheat (WW); winter wheat-canola (Brassica campestris L.) (WC) or winter wheat-fallow (WF)] with tillage sub-treatments of conventional tillage (CT) vs. zero tillage (ZT) and row spacing treatments of uniform row (UR) vs. paired row (PR) spacing. From fall 1993 to spring 1994, ZT was cooler than CT by 1.2°C on the WC rotation, 1.1°C on WW and 0.4°C on the WF rotation. From fall 1994 to spring 1995, the magnitude of tillage differences was lower on all three rotations with ZT being cooler than CT by 0.1–0.9°C. The magnitude of the row spacing effect on soil temperature was less than that of the tillage effect. Extreme differences in soil temperature due to tillage were generally higher (1.0–4.9°C) on the WW and WC than the WF rotation (0.6–2.5°C) due to the presence of more crop residue. Results demonstrate that while ZT promotes overall cooler soils under winter wheat from fall to late spring, especially on continuously cropped (WW, WC) rotations, it also allows moderation of soil temperatures during extremely cold periods. Key words: Soil temperature, winter wheat, rotation, tillage, row spacing
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".