The Physiology and Stability of Leaf Carbon Isotope Discrimination as a Measure of Water-Use Efficiency in Barley on the Canadian Prairies
Bibliographic record
Abstract
Temporal and seasonal water deficit is one of the major factors limiting crop yield on the Canadian prairie. Selection for low carbon isotope discrimination (Δ13C) or high water-use efficiency (WUE) can lead to improved yield in some environments. To understand better the physiology and WUE of barley under drought conditions on the Canadian prairie, 12 barley (Hordeum vulgare L.) genotypes with contrasting levels of leaf Δ13C were investigated for performance stability across locations and years in Alberta, Canada. Four of those genotypes (‘CDC Cowboy’, ‘Niobe’, ‘170011’ and ‘Kasota’) were also grown in the greenhouse under well-watered and water-deficit conditions to examine genotypic variations in leaf Δ13C, WUE, gas exchange parameters and specific leaf area (SLA). The water-deficit treatment was imposed at the jointing stage for 10 days followed by re-watering to pre-deficit level. Genotypic ranking in leaf Δ13C was highly consistent, with ‘170011’, ‘CDC Cowboy’ and ‘W89001002003’ being the lowest and ‘Kasota’‘160049’ and ‘H93174006’ being the highest leaf Δ13C. Under field and greenhouse (well-watered) conditions, leaf Δ13C was significantly correlated with stomatal conductance (gs). Water deficit significantly increased WUE, with ‘CDC Cowboy’– a low leaf Δ13C genotype with significantly higher WUE and lower percentage decline in assimilation rate (A) and gs than the other three genotypes (‘Niobe’, ‘170011’ and ‘Kasota’). We conclude that leaf Δ13C is a stable trait in the genotypes evaluated. Low leaf Δ13C of ‘CDC Cowboy’ was achieved by maintaining a high A and a low gs, with comparable biomass and grain yield to genotypes showing a high gs under field conditions; hence, selection for a low leaf Δ13C genotype such as ‘CDC Cowboy’ maybe important for maintaining productivity and yield stability under water-limited conditions on the Canadian prairie.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".