Effects of postharvest deficit irrigation on sweet cherry (<i>Prunus avium</i>) in five Okanagan Valley, Canada, orchards: I. Tree water status, photosynthesis, and growth
Bibliographic record
Abstract
The timing and availability of water supply are changing in the Okanagan Valley, and the availability of irrigation water in the late summers is a growing concern. Postharvest deficit irrigation (PDI) is a strategy that can be used to reduce water demands in sweet cherry orchards; previous studies in this region have reported no change in plant physiology or tree growth with irrigation volume reductions of up to 25%, postharvest. However, the effects of more severe postharvest reductions in irrigation volume remain unknown. We compared the effects of full irrigation (100% of conventional grower practice through the growing season) with 27%–33% reductions in irrigation postharvest (∼70% of conventional grower practice) and 47%–52% reductions in irrigation postharvest (∼50% of conventional grower practice) over a 3-year period (2019–2021) in five commercial sweet cherry orchards that ranged in elevation and latitude across the Okanagan Valley, BC, Canada. In the growing season following treatment application, PDI had no effect on stem water potential or photosynthesis in any year and at any site; there were also no effects of PDI treatment on tree growth. Findings from this study suggest that postharvest stem water potentials from −0.5 to −1.3 MPa, and one-time stem water potentials as low as −2.0 MPa, have no lasting effects on future plant water status, rates of photosynthesis, or plant growth. PDI shows potential as an effective water-saving measure in sweet cherry orchards in the Okanagan Valley.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".