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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".