Genotypic variability in root length in pea (<i>Pisum sativum</i> L.) and lentil (<i>Lens culinaris</i> Medik.) cultivars in a semi‐arid environment based on mini‐rhizotron image capture
Bibliographic record
Abstract
Abstract Physiological breeding is an approach that complements conventional breeding by providing characterizations of traits present in breeding populations. This allows breeders the ability to choose crosses based on desirable and adaptive traits, an approach that may be more reliable than selection on yield alone. In this study, we determined how much genotypic variability was present in selected lines of modern field pea ( Pisum sativum L.) and lentil ( Lens culinaris Medik.) cultivars from Montana, North Dakota, Washington, and Saskatchewan, Canada, and if root growth, particularly at depth, improves the fitness of lines to semi‐arid environments. We conducted experiments at the Northern Agricultural Research Center of Montana State University from 2017 to 2019 inclusively to investigate root growth with mini‐rhizotrons in 29 field pea lines and 25 lentil lines. Results suggest there is large genotypic variability in root length across the soil profile and the proportion of root length found below 30 cm in both crops, and these root traits appear independent of each other. In field pea, the highest yielding cultivars were intermediary in both total root length and the proportion of root length below 30 cm, suggesting large root systems and/or deeper root profiles are not necessarily beneficial in this environment. By contrast, in lentil, total root length and root length found below 30 cm was well correlated with biomass and yield. For breeders interested in in improved adaptation to semi‐arid environments, it may be too early to optimize root systems, and above‐ground traits may still yield a better return on investment.
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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 | 0.002 | 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 | 0.001 |
| 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".