Erosion-preventive crop rotations and water erosion rates on undulating slopes in Lithuania
Bibliographic record
Abstract
A combination of perennial grass species and selected crop rotations can help prevent soil erosion in upland regions and minimize the risk of soil erosion and associated water pollution (to both terrestrial and aquatic ecosystems). Research data were obtained on sandy loam Eutric Albeluvisols at the Kaltinenai Research Station of the Lithuanian Institute of Agriculture on the undulating hilly topography of the Zemaiciai Uplands of Western Lithuania. The aim was to identify crops and crop rotations that would minimize soil erosion. Measured water erosion rates over 18 yr of field experiments were: 3.2–8.6 m3 ha-1 yr-1 under winter rye, 9.0–27.1 m3 ha-1 yr-1 under spring barley and 24.2–87.1 m3 ha-1 yr-1 under potatoes. Perennial grasses completely prevented water erosion, while the erosion-preventive grass-grain crop rotations (>50% grass) decreased soil losses on arable slopes of 2–5°, 5–10° and 10–14° by 75–80%. The grain-grass crop rotation (<50% grass) decreased rates by 23–24% compared to the field crop rotation. The main attributes of the proposed soil conservation systems were the careful selection of optimum erosion-preventive ecosystems (sod-forming perennial grasses or erosion-preventive crop rotations) with high erosion-resisting capabilities. These selected systems varied in response to slope gradient and thus assist erosion control and ecological stability of the undulating topography of Lithuania. These results may have wider applicability on the undulating landscapes of the temperate agricultural zone. Key words: Undulating upland topography, water erosion rates, erosion-preventive crop rotations, temperate climate
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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 | 0.001 | 0.001 |
| 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".