Assessment of the phenotypic diversity and agronomic performance of a Mediterranean lentil collection under rainfed conditions: towards efficient use in breeding programs for adaptation to Mediterranean-type environment
Bibliographic record
Abstract
Abstract The improvement of lentil productivity and resilience to climate change requires the deployment of breeding approaches and sustainable agronomic practices. Germplasm from the Mediterranean region could be an important source of useful traits for lentil breeding programs. Additionally, no-tillage could also contribute to maintaining lentil productivity in drought-prone environments. However, there are few studies on breeding for adaptation to no-tillage in lentil, as this practice can create growing conditions that differ from those under conventional tillage. The main objectives of this study were to assess the phenotypic diversity of a lentil collection in different environments and to select promising accessions that can be used in lentil breeding programs. A Mediterranean lentil collection of 119 accessions was evaluated in Morocco (under no-till and conventional tillage) and in Turkey (during two growing seasons) under rainfed conditions. There was significant phenotypic variation among accessions for traits assessed. In addition, significant genotype-by-environment interaction effects were observed for grain yield and time to flowering. Moroccan landraces were the earliest to flower compared to landraces from Italy, Turkey, and Greece. Greek landraces displayed the highest mean values of hundred-seed weight. Landraces outperformed advanced lines in low-yielding environment (Turkey in 2022 season) in which higher yield was recorded in Turkish landraces, followed by Moroccan landraces. The high-yielding accessions identified in different environments could be used as donors in breeding programs.
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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 | 0.000 |
| 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".