Soil thermal properties: influence of no-till cover crops
Bibliographic record
Abstract
Soil thermal properties, which determine heat transport, can influence soil health parameters and crop productivity. The objective of this study was to evaluate the 2-year effects of no-till cover crops (CCs) and no-till no cover crop (NC) on soil thermal properties (thermal conductivity (λ), volumetric heat capacity ( CV), and thermal diffusivity ( D)). Two levels of CCs were used for this study: CC versus NC. The CCs included crimson clover ( Trifolium incarnatum L.), hairy vetch ( Vicia villosa Roth.), winter peas ( Lathyrus hirsutus L.), oats ( Avena sativa), winter wheat ( Triticum aestivum L.), triticale ( Triticale hexaploide Lart.), flax ( Linum usitassimum L.), and barley ( Hordeum vulgare L.). Soil samples were collected at 0–10, 10–20, and 20–30 cm depths and their λ, CV, and D were measured in the laboratory. Additionally, soil organic carbon, bulk density (BD), and volumetric water content (ϴ) at saturation, −33 kPa, and −100 kPa soil water pressures were measured. Results showed that BD was 18% and 14% higher under CC compared with NC management during 2021 and 2022, respectively. Furthermore, ϴ at all measured soil water pressures was slightly higher under CC compared with NC management during both years. As a result, λ and D were significantly higher under NC compared with CC management, while CV was significantly higher under CC compared with NC management, during both years and at all measured soil water pressures. Generally, soil thermal properties were directly proportional to ϴ, suggesting that ϴ may be the most important factor influencing soil thermal properties.
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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) | 0.001 | 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".