Newly-identified lentil genotypes adapted to Mediterranean agro-ecosystems
Bibliographic record
Abstract
Abstract Lentil cultivation and consumption promote human health and sustainable agriculture, making a significant contribution to the transition toward a plant-based diet. In Europe, lentil yields are still unstable, and the lack of breeding efforts limits the choice of farmers to few varieties. Here, we characterized 46 lentil genotypes, including local cultivars and landraces from diverse geographic origins, in Mediterranean agro-environments for flowering, architectural and production traits in seven field trials, over 3 years (2019-2021), in two localities (central and southern Italy) and during two sowing seasons (autumn and spring). We estimated the genetic merit of each genotype and identified outperforming genotypes for all traits. Indian ILL 11557AGL and Argentinian IL 4605AGL domesticated varieties resulted superior for earliness. Italian landraces and French cultivars achieved the highest values for first pod height, while landraces and breeding materials from Ethiopia, Syria and Iran were the best-yielding. Data from all seven trials were available for 16 genotypes, so we analyzed the genotype, environment and genotype × environment interaction (GEI) to identify specific genotypic adaptations. European cultivars performed well for architectural traits, whereas the best-yielding genotypes were Middle Eastern and Ethiopian landraces. Environmental effect on yield related to sowing season and locality was detected, with an overall higher yield in autumn compared to spring sowing trials and in central rather than southern Italy. By dissecting the GEI structure using additive main effect and multiplicative interaction (AMMI) analysis and Weighted Average of Absolute Scores (WAASB) index, we identified a group of Iranian landraces (PI 431633 AGL and PI 432033 LSP AGL) adapted to both autumn and spring sowing and one Ethiopian landrace (IG 1959 AGL) showing high yield stability across all environmental conditions. These findings provide a foundation to unlock the full potential of lentil cultivation in European and Mediterranean systems by identifying adapted, high-performing genotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".