Mulching improved soil fertility, plant growth and productivity, and postharvest deficit irrigation reduced water use in sweet cherry orchards in a semi-arid region
Bibliographic record
Abstract
In the Okanagan Valley, sweet cherry production has expanded to higher latitudes due to climate change, but the availability of irrigation water is limited in this semi-arid region. Postharvest deficit irrigation (PDI) and organic mulches can reduce water use in orchards, but their interactive effects on soil fertility, water relations, and crop performance in new orchard environments are unknown. In a randomized block split-plot design, full irrigation (100%) or PDI (72–76% of full irrigation) was applied to the main plots, and mulches (compost, woodchips, bare) were subplots at three sites. Compost increased soil organic matter, nutrients, pH, and electrical conductivity over three seasons at all sites. Woodchips increased tree growth and foliar P and Mn, while compost increased some fruit quality attributes, and foliar P compared to bare soil. Relative to full, PDI saved 24–28% irrigation water after harvest per season at each site without affecting soil moisture and chemical properties, stem water potential, or crop performance, or interacting with mulch effects. These results suggest that in this semi-arid cherry growing region mulches are a promising strategy to maintain soil moisture and improve soil fertility and crop performance, and PDI can reduce water use after harvest without affecting commercial production.
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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) | 0.000 | 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".