Estimation of Drought Tolerance Based on Leaf Temperature in Upland Rice Breeding
Bibliographic record
Abstract
The evaluation of drought tolerance based on the leaf temperature was examined in upland rice breeding. The relationships of the leaf temperature with the transpiration and photosynthetic rates were observed in upland and lowland rice varieties under upland cultivation in 1995, 1996 and 1997. The leaf temperature of the upland rice varieties was lower than that of the lowland rice varieties. Their transpiration and photosynthetic rates were highly correlated with the leaf temperature, measured using an infrared radiation thermometer or portable photosynthesis gas analyzing system in all the three years. The leaf temperature also showed a significant relationship with the grain yield tested in 1995. It is considered that rice varieties with a lower leaf temperature can maintain high transpiration and photosynthetic rates as well as produce a high yield under upland conditions. In addition, there was a significant positive correlation between the leaf temperature and root growth recorded by the trench method. Upland rice varieties with deep rooting showed lower leaf temperatures than those with a shallower root system. On the other hand, in the comparison of leaf temperature in varietal groups of breeding materials, the upland rice lines with medium-late maturity showed the lowest temperature, followed by the early maturing lines and lines for cultivation with sprinkler irrigation. This tendency was in agreement with the general degree of drought tolerance of individual varietal groups. To analyze the mode of inheritance of the leaf temperature, the parent-offspring correlation of leaf temperature measured using an infrared radiation thermometer was examined in breeding materials. Leaf temperature was compared between the progeny lines (F4 generation) of the upland rice variety Kantomochi168 with a high drought tolerance and the upland rice variety Norinmochi4 with a medium drought tolerance. Kantomochi168 progeny showed a lower leaf temperature than Norinmochi4. A similar tendency was confirmed in the F5 generation in the following year. Significant parental-offspring correlation (r = 0.812**) was observed between F4 and F5. Since the leaf temperature of the upland rice progeny may display a relatively higher inheritance, the leaf temperature is, therefore, considered to be a useful indicator to estimate the drought tolerance for line selection in upland rice breeding.
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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".