Addressing the Role of Landraces in the Sustainability of Mediterranean Agroecosystems
Bibliographic record
Abstract
Wheat yields are predicted to decrease over the next decades due to climate change (CC). Mediterranean regions are characterized by low soil fertility and stressful conditions that limit the effect of technological improvements on increasing yield gains, while worsening the negative CC impacts. Additionally, organic farming (OF) lacks specifically adapted genetic material. Accordingly, there is a need to search for varieties adapted to these conditions and whose cultivation may help semi-arid agroecosystems sustainability, focusing on specific agronomic and functional traits. To this purpose, wheat landraces and modern wheat varieties were evaluated under Mediterranean rainfed conditions during three growing seasons under contrasting situations: A conventional farm and an organic farm. Results regarding straw production, weed biomass and biodiversity, and grain N concentration suggest that the cultivation of landraces under Mediterranean rainfed conditions can enhance agroecosystem sustainability through positive effects on ecosystem services such as soil quality, functional biodiversity, or grain protein content, without significant reductions in grain yield. Results highlight the relevant role of wheat landraces as genetic resources for the development of cultivars adapted to Mediterranean agroecosystems conditions, especially for organic farming, but also for conventional agriculture.
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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".