D7.2. MANAGEMENT PRACTICES GUIDELINES MANUAL
Bibliographic record
Abstract
One of the main objectives of Work Package 7 (WP7) is to develop guidelines for optimal cropping systems and agricultural practices. Accordingly, this deliverable (Deliverable 7.2) presents, in the form of an appendix, a manual outlining the outcomes and key benefits of the most effective agriculturalpractices that foster soil biodiversity, while simultaneously providing advantages for both farmers and society more broadly. This manual showcases the most promising agricultural practices designed to address major agronomic challenges identified across six distinct pedoclimatic regions in Europe: Mediterranean South, Lusitanian, Atlantic Central, Continental, Nemoral, and Boreal. Depending on the specific agronomic challenges or environmental concerns prevalent in each region, the proposed practices contribute to: Promoting soil biodiversity. Reducing the incidence of pests and diseases. Enhancing plant growth and development. Decreasing input use (e.g., fertilisers, pesticides, water, fuel). Increasing soil fertility. Reducing soil and water pollution. Lowering greenhouse gas emissions. Increasing carbon sequestration. Maintaining or even improving farmers’ economic returns. The agricultural practices and cropping systems proposed in this manual are thus expected to enhance both the genetic and functional diversity of soil biota and, consequently, reduce dependence on external inputs (such as fertilisers and pesticides). At the same time, they aim to improve crop yield and quality, reinforce soil ecosystem services, and increase the overall stability and resilience of agricultural systems. This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.145 |
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".