Effects of postharvest deficit irrigation on sweet cherry (<i>Prunus avium</i>) in five Okanagan Valley, Canada, orchards: II. Phenology, cold hardiness, fruit yield, and quality
Bibliographic record
Abstract
Irrigated agriculture in semi-arid regions is expected to increase in the future, which puts greater demands on scarce water resources. Sustainable irrigation strategies in semi-arid regions will support agricultural resilience to climatic change. The response of “Sweetheart”/Mazzard sweet cherry trees ( Prunus avium L.) to postharvest deficit irrigation (PDI), as a water conservation method, was studied over three seasons (2019–2022) in the semi-arid Okanagan Valley of British Columbia, at five commercial orchards. The following irrigation treatments were applied; ( i) a control of full irrigation, irrigated according to conventional growers’ practice at each orchard, ( ii) PDI-30: 27%–33% reduction in irrigation volume, after harvest (67%–73% of control), and ( iii) PDI-50: 47%–52% reduction in irrigation volume, after harvest (48–53% of control). Spring phenology (the timing of flower bud development, from side green to full bloom), flower bud moisture content and cold hardiness, and fruit yield and quality (before and after cold storage and shelf-life conditions) were assessed to determine if PDI altered fruit development over the subsequent growing season. Neither PDI-30 nor PDI-50 caused changes in the timing of flower bud phenology, cold hardiness or moisture content relative to the control. PDI treatments also had no effect on fruit yield or fruit quality at harvest or after storage and shelf-life conditions. These results suggest PDI could be used to reduce irrigation water use in semi-arid regions, like the Okanagan Valley, without affecting sweet cherry production or fruit quality.
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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 | 0.001 | 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 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".