Diversity in stomatal and hydraulic responses to post‐flowering drought in common (<i>Phaseolus vulgaris</i>) and tepary (<i>P. acutifolius</i>) beans
Bibliographic record
Abstract
Abstract Plants differ widely in how soil drying affects stomatal conductance ( g s ) and leaf water potential ( ψ leaf ), and in the underlying physiological controls. Efforts to breed crops for drought resilience would benefit from a better understanding of these mechanisms and their diversity. We grew 12 diverse genotypes of common bean ( Phaseolus vulgaris L.) and four of tepary bean ( P. acutifolius; a highly drought resilient species) in the field under irrigation and post‐flowering drought, and quantified responses of g s and ψ leaf , and their controls (soil water potential [ ψ soil ], evaporative demand [Δ w ] and plant hydraulic conductance [ K ]). We hypothesised that (i) common beans would be more “isohydric” (i.e., exhibit strong stomatal closure in drought, minimising ψ leaf decline) than tepary beans, and that genotypes with larger ψ leaf decline (more “anisohydric”) would exhibit (ii) smaller increases in Δ w , due to less suppression of evaporative cooling by stomatal closure and hence less canopy warming, but (iii) larger K declines due to ψ leaf decline. Contrary to our hypotheses, we found that half of the common bean genotypes were similarly anisohydric to most tepary beans; canopy temperature was cooler in isohydric genotypes leading to smaller increases in Δ w in drought; and that stomatal closure and K decline were similar in isohydric and anisohydric genotypes. g s and ψ leaf were virtually insensitive to drought in one tepary genotype (G40068). Our results highlight the potential importance of non‐stomatal mechanisms for leaf cooling, and the variability in drought resilience traits among closely related crop legumes.
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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 | 0.001 | 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) | 0.000 | 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 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".