Gestione del suolo in sistemi viticoli e olivicoli marginali: evidenze sui co-benefici ecosistemici
Bibliographic record
Abstract
Oliveti e vigneti del Sud Italia si trovano spesso in aree marginali con suoli degradati da frequenti lavorazioni. Questo studio ha valutato l’effetto dell’inerbimento (spontaneo in oliveto, seminato in vigneto) su biomassa, permeabilità ed erosione. L’indagine è stata condotta in un oliveto (100 alberi ha−1, pendenza 9.5%) e in un vigneto (4115 viti ha−1, pendenza 16%) con due gestioni del suolo: lavorato (L) vs inerbito (I). Sono stati misurati: conducibilità idraulica a saturazione (Kfs) e potenziale di flusso matriciale (Φm) mediante permeametro di Guelph (n = 6), ruscellamento ed erosione con simulatore di pioggia (n = 6), e biomassa su parcelle di 1 m2 (n = 5). In oliveto, né Kfs (2.96 ± 1.00 e 2.49 ± 0.80 cm min−1 in L e I) né Φm (0.41 ± 0.14 e 0.35 ± 0.11 cm2 min−1 in L e I) hanno mostrato variazioni significative. Tuttavia, l’inerbimento ha ridotto il ruscellamento dell’88% (168.92 ± 76.96 g in L vs 19.61 ± 5.75 g in I) e l’erosione del 64% (1.48 ± 0.11 t ha−1 in L vs 0.53 ± 0.24 t ha−1 in I). La biomassa è risultata maggiore in I rispetto a L in primavera (3.98 ± 1.86 vs 0.30 ± 0.09 t ha−1) e autunno (1.32 ± 0.42 vs 0.55 ± 0.10 t ha−1). In vigneto, l’inerbimento non ha modificato Kfs (0.46 ± 0.09 vs 0.75 ± 0.33 cm min−1) e Φm (0.06 ± 0.01 vs 0.10 ± 0.05 cm2 min−1), ma ha ridotto il ruscellamento del 90% (232.16 ± 54.23 g in L vs 22.98 ± 12.40 g in I) e l’erosione del 99% (2.58 ± 0.71 t ha−1 in L vs 0.09 ± 0.00 t ha−1 in I). Questi risultati dimostrano come la gestione del suolo incida su perdite di suolo e aumento della biomassa, con potenziali effetti benefici sull’ecosistema.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".