Impact of Land Rolling on Wind-Eroded Sediment in Soybean Production
Bibliographic record
Abstract
Wind-driven soil erosion is a major environmental issue that can lead to decreased soil productivity by eroding nutrient-rich fine soil particles away from agricultural lands. Many Manitoba soybean fields are routinely rolled shortly after planting. There are concerns that the practice of land rolling in soybean production may increase the potential for wind erosion through breakdown of soil aggregates into smaller unstable aggregates and reducing the roughness of the soil surface. Therefore, an experiment was conducted as an on-farm trial in eight different locations in in the Red River Valley of southern Manitoba during 2018 and 2019. The experimental trials were established with two treatments (rolled and non-rolled) arranged using a randomized complete block design. Sediment traps, specially designed and fabricated for this study, were used to collect wind-eroded sediment moving over the soil surface along the length of each treatment. The results of this study did not show that land rolling increased wind erosion risk by reducing soil surface roughness. With respect to the experimental evidence on amount of sediment collected by wind erosion samplers, the results show that there is a significant difference among samplers with collection opening at 5cm and 20cm, which indicates that most of the particles transported at 5 cm height. Detailed particle size distribution showed, the wind-eroded particles collected by the sediment traps with collection openings at 20 cm were slightly finer than the traps with openings at 5cm.
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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 | 0.001 | 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".