The Effect of Irrigation Frequency, Phosphorus Fertigation, and Cultivar on Levels of Phenolic Compounds in Sweet Cherries
Bibliographic record
Abstract
This work examined the effect of irrigation frequency and phosphorus (P) fertigation on the levels of phenolic compounds present in two sweet cherry cultivars, ‘Skeena’ and ‘Cristalina’, over three growing seasons (2012–14). Two irrigation treatments were tested: a high irrigation frequency (I1) and a low irrigation frequency (I2). Both irrigation treatments applied the same quantities of water [100% evapotranspiration (ET)], but the high irrigation frequency applied water four times daily (0300, 0900, 1500, and 2100 hr ) whereas the low irrigation frequency was applied at one time (0900 hr ) every second day. Three soil management treatments were investigated, including 1) an unmulched control receiving no P, 2) a 10-cm waste wood mulch receiving no P, and 3) a treatment involving annual fertigation of 20 g P/tree at full bloom as ammonium polyphosphate. It was determined that cultivar was the most important factor affecting levels of phenolic compounds in sweet cherries, with generally greater levels associated with ‘Skeena’. The effect of different irrigation and fertilization strategies showed less promising results in terms of influencing levels of phenolic compounds. Both severe and mild water stress did not show an appreciable influence on increasing levels of phenolic compounds in cherries. Furthermore, severe water stress, which occurred during 2012, was associated with the lowest annual concentration of phenolic compounds and an economically unacceptable reduction in fruit size. Phosphorus fertigation influenced cherry phosphorus status positively by increasing leaf and fruit P concentrations consistently, yet these fruit exhibited lower levels of phenolic compounds.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".