Water stress and crop load effects on yield and fruit quality of Elegant Lady peach [ <i>Prunus persica (L.)</i> Batch]
Bibliographic record
Abstract
Introduction . Fruit production is faced with water shortage, especially in areas with a Mediterranean climate characterized by a very long, dry and hot summer. Thus, the growers under such conditions must manage irrigation carefully by finding new strategies, including water stress management. Materials and methods . Effects of water stress (WS) and crop load (CL) on the carbon assimilation rate, fruit growth, crop yield and fruit quality (size and soluble solids content) were evaluated in a 7-year-old ‘Elegant Lady’ peach orchard (Winters, California, USA). The experimental design consisted of a completely randomized block factorial design with 2 × 3 factors: irrigation with two levels (control and WS trees) and crop load with three levels (light, commercial and heavy). Results and discussion . Both CL and WS affected fruit growth during the last stages but not early on. Crop load did not affect trunk water potential (TrWP) which, however, was significantly reduced by WS throughout the day and the season. The stomatal conductance (gs ), transpiration rate (E) and CO2 assimilation rate (A) were not affected by CL, but they were reduced by WS. There were poor correlations between TrWP and either gs or A in control trees, indicating relatively poor coordination between leaf functions in peach trees under optimal conditions. Both WS and CL delayed the harvest date through their effect on ripening. Water stress significantly reduced the average crop fresh yield but hardly affected crop dry yield. Both WS and CL affected the distribution of fruit size categories, with the proportion of large fruit decreasing with the increase in crop load and the severity of WS. Conclusion . Water stress reduced fruit fresh weight and fruit fresh yield but not fruit dry weight or dry yield. Crop load reduced fruit fresh and dry weights and yields. Crop load had a negative effect on soluble solids content, while WS had a positive effect. Thus, CL reduced fruit size and soluble solids content, while WS reduced size but improved soluble solids concentration.
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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) | 0.001 | 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".