Irrigation scheduling strategies to reduce the environmental impact of Ontario’s ornamental nurseries
Bibliographic record
Abstract
In typical ornamental nursery operations, irrigation schedules are predominantly determined by the nursery manager's subjective assessment of crop water requirements. In lieu of quantitative assessment strategies, managers tend to err on the side of caution and water to excess. This results in poor water use efficiency and significant fertilizer leaching that negatively impacts local watersheds. Using innovative water potential sensors that directly measure plant water stress/status in near real time, we characterized the relationships between crop water stress, prevailing environmental conditions, and species-specific water stress tolerance thresholds. Irrigation scheduling algorithms that predict cumulative plant water potential (cWP) thresholds based on cumulative daily environmental conditions (i.e., cumulative vapour pressure deficit - cVPD) were tested on Chanticleer pear (Pyrus calleryana) trees grown in a pot-in-pot production system equipped with drip irrigation. Three water restriction treatments were applied: 1) control (nursery irrigation schedule), 2) moderate restriction/stress cVPD threshold, and 3) high restriction/stress cVPD threshold. The moderate and high stress treatments resulted in 46 and 63% water savings respectively relative to the control. Trees grown under the moderate treatment showed no significant difference in growth compared to the control trees. Trees grown under high water stress did exhibit reduced growth, as determined by caliper diameter differentials, but otherwise appeared healthy. This cVPD/cWP approach has potential to dramatically reduce water consumption and environmental impact of nursery operations if adopted by the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".