Organic No-Till with Roller Crimpers: Agro-ecosystem Services and Applications in Organic Mediterranean Vegetable Productions
Bibliographic record
Abstract
In sustainable/organic farming systems, Agro-ecological Service Crops (ASC) may provide many beneficial ecosystem services, when they are introduced as buffer zones, living mulches or break crops. This outlook paper focuses on: i) the role of ASC genotype and mixtures as catch crops for soil mineral nitrogen (NO3-) surplus, which is returned to the system after their termination; ii) living mulches and break crops management strategies, particularly comparing ploughing under (green manure) with termination by roller crimper; iii) summary of three recent case studies that have assessed the effectiveness of ASC management by no-till with roller crimper for tomato, zucchini and melon crops, under Mediterranean conditions. Recently, in central Italy yield and quality results on organic tomato indicated that this crop was suitable following termination of leguminous ASC by roller crimping. Similarly, this ASC management increased yield by about 70% compared to green manure in zucchini crop. In southern Italy, no substantial differences were found in the ASC management and organic fertilizer interactions in organic melon, confirming the suitability of matching these strategies to sustain crop production. More studies should be encouraged to further empower the use of ASC in a wide range of agro-climatic conditions. Furthermore, additional studies on the roller crimper should be performed, mainly to understand the dynamic of N mineralisation in the soil-mulch interface and synchronisation of N release with cash crop N requirements. Finally, Decision Supporting Systems (DSS) for ASC introduction into vegetable cropping systems should be developed.
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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 | 0.000 | 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".