Characterization of Heat Tolerance in Two Apple Rootstocks Using Chlorophyll Fluorescence as a Screening Method
Bibliographic record
Abstract
High temperature has an adverse effect on apple production worldwide. Photosynthesis is a process especially vulnerable to heat stress, which can reduce photosynthetic efficiency, plant growth, development, and ultimately yield. Although the effects of heat stress on apples have been partially examined, the photochemical reactions and heat tolerance of specific rootstocks have still not been sufficiently investigated. Identification of rootstocks with better photosynthetic performance and adaptation to heat stress enables the selection of rootstocks, which could contribute to stable yields and good fruit quality even at elevated temperatures. In this study, chlorophyll a fluorescence (ChlF) induction kinetics was used to investigate the heat tolerance between two apple rootstocks (M.9 and G.210). In addition, we employed lipid peroxidation measurements, hydrogen peroxide quantification, proline content, and total phenolic and flavonoid assessments. Analysis of chlorophyll fluorescence parameters and OJIP curves (different steps of the polyphasic fluorescence transient; O–J–I–P phases) revealed significant differences in their responses, with higher values of the PIABS parameter indicating better PS II stability and overall photosynthetic efficiency in M.9 rootstock. The higher contents of chlorophyll, carotenoids, proline, and significant increase in the accumulation of phenolics, and flavonoids in this rootstock also contributed to its better adaptation to heat stress. Oxidative stress was more pronounced in G.210 through higher H2O2 and MDA levels, which could point to its lower capacity to adjust to heat stress conditions. This research can provide a scientific basis for further breeding programs and growing plans due to climate change and the occurrence of extremely high temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".