Financial Analysis of Water Management Practices in Cranberry Production in Quebec, Canada
Bibliographic record
Abstract
At a global scale, Canada is the second largest cranberry producer, with Quebec being the largest producing region within Canada. Efficient water use in agricultural production has long been a topic of outmost importance to agricultural producers, and governing bodies. The immediacy of climate change effects sped up the need to find solutions that conserve water. One such promising technology is irrigation using real-time tensiometers, which provides rapidly critical irrigation needs information to producers. Adoption of improved technologies by farmers is dependent on the effect it has on the farms’ bottom line. In this study, we examine the financial performance of real-time tensiometer based irrigation, and compare it to evaporation needs based irrigation (baseline), in the context of a Quebec-based cranberry farm. Our findings show that irrigating using real-time tensiometers technology generated higher economic returns. With a net present value of $96,847, this technology increased returns by nearly 53% compared to the baseline technology. Subsequent sensitivity analyses confirmed the robustness of these findings, even when changing important farming parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".