Vineyard soil microbial community under conventional, sustainable and organic management practices in a Mediterranean climate
Bibliographic record
Abstract
Conventional, sustainable or organic farming practices are assumed to have distinct effects on soil fertility and health. This is often supported by arguments linking management and resulting soil parameters to crop yield and produce quality. Soil microbial communities are sensitive to management practices that alter soil water fluxes and the pools and fluxes of nutrients. These effects might be accentuated in arid or semiarid agriculture. Conversion to vineyard use, under Mediterranean conditions, and the subsequent application of different management types creates the conditions for divergent soil microbial communities. An off-season survey of variably managed vineyards located in a Mediterranean climate showed that both organic and conventional vineyard management had the most distinct impact on soil abiotic parameters, and on the bacterial and fungal communities; both organic and sustainable management enhanced soil organic carbon, water holding capacity and nitrogen availability. The sustainable management led to soil microbial communities most similar to the natural conditions. Fungal diversity was better than bacterial diversity at discriminating between soils under different management types. Classes of the dominant Ascomycota phylum had best discriminating power; Mucoromycota declined significantly after conversion to vineyard use and was a key taxonomic indicator for such conversion. Regarding bacterial communities, a focus on functional categories, e.g. nitrogen-fixing taxa, may be more informative than total diversity assessments.
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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 | 0.001 | 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".