Multidimensional assessment demonstrates sustainability of new low-input viticulture systems in north-eastern France
Bibliographic record
Abstract
Current demand for more environmentally, socially, and economically sustainable farming practices requires multicriteria assessment of innovative agricultural systems to help enhance cropping systems. We assessed the sustainability of 11 systems intended to substantially reduce inputs, especially pesticides, to viticulture systems. Pesticide use was reduced using three methods: (i) technical (e.g., essential oils instead of some copper use, 100 % soil covering applied); (ii) organizational (e.g., new spraying decision rules, decision aid tools); and (iii) redesign of the entire system (e.g., resistant grape varieties planted). Sustainable performance of the new low-input vine systems (NLIVS) was assessed over 6 years (2013–2018) based on 25 indicators. They covered all aspects of system sustainability: environmental, agronomic, economic, and social. This included the evaluation of human capital and the quality of the wines produced. The results showed that it is possible to combine good environmental performance, such as reducing treatment frequency by up to 89 %, with good agronomic and wine quality performances. The socio-economic performance of these NLIVS was more moderate. Nonetheless, the results demonstrated that the NLIVS are sustainable according to many diversified indicators. NLIVS are also resilient, as shown by the consistent sustainability results over six consecutive years. They are a realistic tool enabling winegrowers to consider reducing viticulture inputs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".