The Impact of Vineyard Abandonment on Soil Properties and Hydrological Processes
Bibliographic record
Abstract
Core Ideas A Guelph permeameter was used to quantify water movements. The active vineyard had higher permeability and saturated conductivity than the abandoned one. Vineyard soils showed a higher variability among seasons and slope positions. In the abandoned plot, there was a high correlation with gravel, OM, and clay content. In the vineyard, human impacts on the soil did not allow any key factor to be obtained. Soil and water resources are affected by land use changes such as land abandonment in vineyards. Changes in water resources and soil water dynamics can result in sudden alterations in erosion rates and trigger land degradation. In this study, we examined the impact of land abandonment on soil properties and hydrological processes in two paired plots: an active and an abandoned vineyard. Laboratory analyses were performed to assess texture, antecedent soil moisture, stoniness, soil water content, and organic matter (OM). A Guelph permeameter was used to quantify the permeability (Per) and saturated hydraulic conductivity (Kfs) at different slope positions and seasons (before and after harvest). Results showed that soil texture and OM were highly modified, and as consequence soil hydrology, after abandonment. We observed a significantly higher Per (26.8 mm h −1 ) and Kfs (8.4 mm h −1 ) in the active vineyard soil than in the abandoned one (Per of 14.2 mm h −1 and a Kfs of 5.4 mm h −1 ). The soils of the active vineyard showed a greater variability among seasons and slope positions. In the abandoned vineyard, the results showed a high correlation of Per and Kfs with gravel content and OM and a negative correlation with the clay fraction. Natural revegetation improved the soil water retention capacity. In the active vineyard, we could not find any relevant factor to explain the soil hydrological properties, and this may be attributed to the disturbance induced by human activities, such as machinery, trampling, and the use of herbicides.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".