Canopy Structure, Light Interception, and Photosynthesis in Maize
Bibliographic record
Abstract
The amount and distribution of leaf area and leaf angles in a crop canopy determine how photosynthetically active radiation (PAR) is intercepted and consequently influences canopy photosynthesis and yield. Factors such as plant shape, plant populations, and row width will affect these leaf distributions and can occur in an almost infinite number of different combinations. To supplement experimentation, a mathematical model was developed to use measurements of leaf area and leaf angles in two dimensions (with height and across the row) to calculate PAR interception and canopy photosynthesis. Maize (Zea mays L.) hybrids with phenotypic differences were planted at several plant populations to produce a wide range of two‐dimensional leaf area and leaf angle patterns. The extreme phenotypes, leafy and reduced stature, were included to vary plant height and number of leaves above the ear. Measurements of average PAR at various levels were made in seven different canopies and compared with calculations from the model (R2 of 0.68 and 0.92 for two sets of data). As well, measurements of PAR at 20‐cm increments on transects perpendicular to the row were made in three canopy types at three levels and compared with theoretical calculations (R2 = 0.74). A simple numerical experiment was run to demonstrate the utility of the model where daily canopy photosynthesis was calculated for two row widths and seven plant types. One result was that depending on row widths, plants with very upright leaves can have both the smallest and largest daily canopy photosynthesis.
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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".