Broad environmental adaptation is associated with root anatomical phenotypes in maize landraces: an <i>in silico</i> study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Root phenotypes contribute to environmental adaptation. We hypothesized that root phenotypes of maize (Zea mays L. ssp. mays) landraces reflect their adaptation to edaphic limitations in their native soil environments, and that some root phenotypes may confer broad edaphic adaptation. METHODS: We phenotyped the roots of maize landraces and used the functional-structural plant/soil model OpenSimRoot_v2 to simulate landraces and their native environments to analyse how root phene states interact with each other and with environment variables to regulate edaphic adaptation. KEY RESULTS: Landraces from low phosphorus regions have root phenotypes with shallow growth angles and greater nodal root numbers, allowing them to adapt to their native environments by improved topsoil foraging. We used machine learning algorithms to detect the most important phenotypes responsible for adaptation to multiple environments. The most important phene states responsible for stability across environments are large cortical cell size and reduced diameter of roots in nodes 5 and 6. When we dissected the components of root diameter, we observed that large cortical cell size improved growth by 28, 23 and 114 %, while reduced cortical cell file number alone improved shoot growth by 137, 66 and 216 %, under drought, nitrogen and phosphorus stress, respectively. Functional-structural analysis of 96 maize landraces from the Americas, previously phenotyped in mesocosms in the glasshouse, suggested that parsimonious anatomical phenotypes, which reduce the metabolic cost of soil exploration, were the main phenotypes associated with adaptation to multiple environments, while root architectural phenotypes were related to adaptation to specific environments. CONCLUSIONS: These results indicate that integrated root phenotypes with anatomical phene states that reduce the metabolic cost of soil exploration increase tolerance to edaphic stress across multiple environments and therefore would improve yield stability, regardless of their root architecture.
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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 | 0.001 |
| 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".