Interaction of Irrigation and Soil Management on Sweet Cherry Productivity and Fruit Quality at Different Crop Loads that Simulate Those Occurring by Environmental Extremes
Bibliographic record
Abstract
‘Cristalina’ and ‘Skeena’ sweet cherry cultivars ( Prunus avium L.) on Gisela 6 ( Prunus cerasus × Prunus canescens ) rootstock planted in 2005 were maintained since 2006 in a randomly blocked split-split plot experimental design with six blocks of two irrigation frequency main plot treatments within which two cultivar subplots and three soil management sub-subplots were randomly applied. The focus of this study was the growth, yield, and fruit quality response of sweet cherry to water and soil management over three successive fruiting seasons, 2009–11, in a cold climate production area. The final 2 years of the study period were characterized by cool, wet springs resulting in low yield and yield efficiency across all treatments. Soil moisture content (0- to 20-cm depth) during the growing season was often higher in soils that received high-frequency irrigation (HFI) compared with low-frequency irrigation (LFI). HFI and LFI received the same amount of water, but water was applied four times daily in the HFI treatment but every other day in the LFI treatment. Consequently, larger trunk cross-sectional area (TCSA) and higher yield were found on HFI compared with LFI trees. Soil management strategies involving annual bloom time phosphorus (P) fertigation and wood waste mulching did not affect tree vigor and yield. Increased soluble solids concentration (SSC) occurred with LFI. Decreased SSC occurred with delayed harvest maturity in trees receiving P fertigation at bloom. The largest fruit size was correlated for both cultivars with low crop loads ranging from 100 to 200 g fruit/cm 2 TCSA. Overall cool, wet spring weather strongly affected annual yield and fruit quality, often overriding cultivar and soil and water management effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".