Assessing On-Farm Irrigation Water Use Efficiency in Southern Ontario
Bibliographic record
Abstract
For high-value horticultural crop production in southern Ontario, irrigation is an essential ingredient in overcoming insufficient rainfall and achieving stabilized crop production. In a context where competition for limited water resources intensifies due to the expansion of the agricultural sector, increasing urban development and tourism, and potential climate change impacts, conserving water through efficient irrigation has become a key solution in addressing this growing challenge. The implementation of advanced soil water monitoring technologies and water budgeting methods for improved irrigation scheduling is examined with regard to water conservation and thus as a means to cope with competing demands for limited water supplies. During the 2007 growing season, soil moisture was measured using two sensors at four field sites (comprising a total of six irrigated zones as two sites include two different irrigation/production systems) in southern Ontario. Irrigation water consumption was measured by flow meters at three sites. In addition, a survey was administered to collect information on growers’ current irrigation scheduling practices. On-farm irrigation performance was assessed by comparing calculated tomato, green bell pepper, strawberry and peach water requirements (using the water budget method) with growers’ estimates of irrigation water use and with soil moisture measurements taken during the growing season. Four out of the six irrigated zones were excessively irrigated, while in one zone, water was insufficiently applied. The crop water requirements were met efficiently exclusively in one zone where tomatoes were grown. Overall, the results of this research show that by implementing advanced soil moisture monitoring technologies, growers can increase precision in water application and reduce the uncertainty in their current irrigation scheduling practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".