Using Pulsed Water Applications and Automation Technology to Improve Irrigation Practices in Strawberry Production
Bibliographic record
Abstract
Quebec, Canada, is the third largest strawberry ( Fragaria × ananassa ) producer in North America, behind Florida and California. In view of increasing global water scarcity and the high water requirements of strawberry production, there is a critical need for growers to optimize irrigation practices to improve crop water productivity (CWP). In Quebec, pulsed irrigation has been shown to increase yields in strawberry crops while using the same volume of water as standard (nonpulsed) irrigation, thus improving CWP. However, more frequent and shorter-duration water applications (pulsed irrigation) might be more complex to manage manually; therefore, it could be of interest to automate the irrigation process at the farm scale. The first objective of our study was to assess the economic impact of pulsed irrigation compared with the standard irrigation procedure (nonpulsed irrigation) in a strawberry crop grown in a highly permeable clay loam soil in Quebec. The second aim was to determine whether pulsed irrigation would generate enough benefits to offset the cost of an automated irrigation system. We used data from three sites to determine the effect of pulsed irrigation on marketable yields and gross revenues compared with nonpulsed irrigation. We conducted a cost–benefit analysis to assess the cost-effectiveness of an automated irrigation system based on net gains associated with pulsed irrigation. Our results showed that pulsed irrigation was appropriate in strawberry crops grown in a highly permeable soil because it led to significant gross revenue increases relative to the standard irrigation procedure. Our results also revealed that pulsed irrigation generated enough additional benefits to cover the cost of an automated irrigation system, with a short payback period of about 1 year.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".