Effect of Drought Stress on Leaf and Whole Canopy Radiation Use Efficiency and Yield of Maize
Bibliographic record
Abstract
Drought stress reduces yield of maize ( Zea mays L.) and other grain crops by (i) reducing canopy absorption of incident photosynthetically active radiation (PAR), (ii) reducing radiation use efficiency (RUE), and (iii) reducing harvest index (HI). The primary objective of this work was to quantify yield losses attributable to each of these components for maize exposed to drought stress in a 2‐yr field study. A second objective was to examine the relationship between RUE at the single leaf level (estimated using chlorophyll fluorescence techniques) and RUE at the whole crop level. Two levels of soil water deficit and a control treatment were established using drip tape irrigation, and dry matter harvests were taken at midseason and at physiological maturity. Mild and severe water stress treatments reduced final grain yield by 63 and 85%, respectively, in 2000, and by 13 and 26%, respectively, in 2001. Reduction of intercepted PAR (IPAR) was generally a very minor yield loss component. Yield losses attributable to reduced RUE and reduced HI were of similar magnitude. Weekly chlorophyll fluorescence measurements were used to estimate the average quantum efficiency of photosystem II at a photosynthetic photon flux density of 1200 μmol m −2 s −1 (Φ II1200 ) for each plot. Crop dry matter accumulation was not linearly related to IPAR, due to decreased RUE in the water stress treatments. However, the linear relationship was restored when daily IPAR was multiplied by the current estimate of Φ II1200 , suggesting that Φ II1200 can be used as an indicator of whole‐crop RUE.
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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".