Soil temperature and water contents among vertical tillage, strip tillage, and chisel plowing in the Upper Great Plains
Bibliographic record
Abstract
Long winters in the Red River Valley (RRV) of eastern North Dakota and western Minnesota result in short growing seasons for corn (Zea mays L.)–soybean (Glycine max L. Merr.) systems. Historically, producers implement aggressive tillage to warm the soil, which has caused hesitation when considering reduced tillage systems. The association between soil warming–drying and crop yields via research at farmer-operated, production-scale systems practicing reduced tillage is lacking. A two-year study was conducted at three producer-operated, corn–soybean fields in the RRV with five soil series ranging from sandy loams to silty clays. Soil temperature (T), soil volumetric water content (θ), and crop yields were measured to evaluate the effects of soil warming and drying and crop performance under reduced tillage practices of vertical tillage (VT), strip tillage with shanks (STS), strip tillage with coulters (STC), and chisel plow (CP). Results showed that daytime soil T and θ in the tilled strips for STC and STS (i.e., in the plant rows) were similar to CP and significantly different from VT. The ST treatments demonstrated added benefits of higher soil θ between the tilled strips (i.e., between the plant rows) during the mid-growing season when plant water needs are at their highest. However, daily averaged soil T and θ demonstrated little to no significant differences among the tillage treatments. Moreover, crop yields were inconsistent with soil T and θ, indicating weak to no practical association in these on-farm settings.
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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) | 0.000 | 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".