D6.4. FARMERS' ADOPTION OF SUSTAINABLE SOIL MANAGEMENT PRACTICES
Bibliographic record
Abstract
Change in the current farming practices is crucial for improving soil conditions and the ecosystemservices. Beyond economic and environmental aspects, the adoption of new management practicesby farmers depends on several social and psychological factors at the farm level. The objective of thisdeliverable is to obtain a better understanding of decision-making by farmers when it comes to theadoption of sustainable soil management practices. In this deliverable, we apply the theory of plannedbehavior. We present barriers and drivers for different sustainable soil management practices takinginto account the context of the five pedoclimatic regions included in our study. Furthermore, wepresent an econometric analysis of farmers compensation requests. The survey results from the regions showed that the most commonly used indicators to evaluate soilquality were soil structure, pH levels, and water retention capacity. Generally, 46% of respondentsrated soil quality as rather good, while 38% rated it as neither good nor bad. Popular practices toimprove soil quality included incorporating crop residue and implementing long rotation periods, withstrong interest in less frequent ploughing and reducing tillage depth. Attitudes towards improving soilquality were positive, and the intention to invest in soil quality was strongly linked to past behaviorand moral obligation. Varying levels of intention were shown related to regional practices, being highest in Atlantic Spain(crop diversification) and in Finland (plant cover, shallow tillage). The importance of attitude and pastbehavior in explaining intention to apply the practices were consistent across the regions, with themost positive attitudes in Mediterranean Spain t 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".