Non-Market Ecosystem Services of Agricultural Land and Priorities Towards a More Sustainable Agriculture in Italy
Bibliographic record
Abstract
Ecosystems provide a range of services, commonly called Ecosystem Services (ESs), which are of funda-mental importance to human well-being, health, livelihoods, and survival. One of the largest and most productiveecosystems is agricultural land. Agro-Ecosystems provide a range of services directly linked to the market (e.g. food and raw materials production) but also many others of high ecological value, which have an indirect economical contribution in the GDP (non-market ESs). These non-market services are not usually taken into account in the development of agricultural management strategies while their contribution is reduced due to non-sustainable agricultural practices. The aim of the study is to assess the potential economic contribution of the non-market services at national, regional, and provincial level in Italy and to propose a simplified index-based method for setting priorities at different scales of administration units for the implementation of sustainable agricultural practices (SAPs). The results of the study showed that the potential non-market value of agro-ecosystem services (AESs) in Italy can reach ~46.2 billion USD$2007. This estimate shows that non-market AESs can play an important role in the national GDP if SAPs are followed. Finally, a priority ranking scheme for the implementation of SAPs was proposed at regional and provincial level which can be a valuable decision support tool for promoting sustainable agriculture policies.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".