Vertical patterns of photosynthesis and related leaf traits in two contrasting agricultural crops
Bibliographic record
Abstract
To include within-canopy leaf acclimation responses to light and other resource gradients in photosynthesis modelling, it is imperative to understand the variation of leaf structural, biochemical and physiological traits from canopy top to bottom. In the present study, leaf photosynthetic traits for top and bottom canopy leaves, canopy structure and light profiles, were measured over one growing season for two contrasting crop types, winter barley (Hordeum vulgare L.) and rape seed (Brassica napus L.). With the exception of quantum yield, other traits such as maximum photosynthetic capacity (Amax), dark respiration, leaf nitrogen and chlorophyll contents, and leaf mass per area, showed consistently higher (P<0.05) values for top leaves throughout the growing season and for both crop types. Even though Amax was higher for top leaves, the bottom half of the canopy intercepted more light and thus contributed the most to total canopy photosynthesis up until senescence set in. Incorporating this knowledge into a simple top/bottom-leaf upscaling scheme, separating top and bottom leaves, resulted in a better match between estimated and measured total canopy photosynthesis, compared with a one-leaf upscaling scheme. Moreover, aggregating to daily and weekly temporal resolutions progressively increased the linearity of the leaf photosynthetic responses to light for top leaves.
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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 | 0.000 | 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) | 0.000 | 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".