Sustainable Strategies for Sunburn Mitigation in Gala Apple Orchards: Effects on Yield, Fruit Quality, and Plant Physiology
Bibliographic record
Abstract
Fruit sunburn is a major abiotic stress limiting apple production worldwide, with losses potentially reaching 50% due to climate change-driven heat events. This study aimed to evaluate sustainable strategies to mitigate or reduce sunburn on ‘Gala Galaxy Selecta’ apple trees. Field trials conducted in summer 2021 compared eight treatments: silicon-based application (Eckosil®), foliar fertilization with algae extracts, macro- and micronutrients, and amino acids, increased irrigation (+35% ETc), mineral particle films (Surround®, Vegepron Sun®, Agrowhite®, Sunstop®), and an untreated control. Randomized block designs with replicates were used. Agronomic parameters, including particle film coverage, trunk cross-sectional area, yield, and fruit quality (color, sunburn incidence, firmness, soluble solids content, dry matter, starch), were measured at harvest. Physiological responses, such as net photosynthesis, maximum quantum yield of Photosystem II, specific leaf area, fruit surface temperature, photoprotective pigments, antioxidants, and heat shock protein gene expression, were also assessed. Foliar fertilization, Agrowhite®, and water reinforcement produced the highest yield per trunk cross-sectional area, with increased soluble solids content and enhanced red pigmentation. Surround® minimized sunburn incidence but reduced photosynthetic activity, as did Vegepron Sun®. Agrowhite® balanced sunburn protection with maintenance of fruit quality and physiological function. These findings provide practical guidance for growers to select effective treatments, balancing sunburn mitigation, fruit quality, and tree physiological performance, while offering researchers insights into integrating agronomic and physiological strategies for climate-resilient apple production.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".