Economic Impact of Water Management Practices for Onion Production in Quebec, Canada
Bibliographic record
Abstract
On-farm adaptation strategies for climate change can constitute effective ways to increase a farm’s resilience and inherently ensure its sustainability of production. Across Canada, supplemental water use is essential in agricultural production. Eastern Canada (including Quebec) may experience dwindling water supplies in the future due to climate change and increased competition from other users. To understand the degree to which adoption of an improved water management practice can lead to a more sustainable production, we focus on a Canadian case study -- a farm growing onions, located in the province of Quebec. Existing technology of surface irrigation system was compared against the new technology of a sprinkler irrigation system, in terms of their financial, social and environmental impacts. By adopting such a irrigation system, the onion grower increased crop yields, and reduced annual operating costs by reducing energy and water costs. In addition to these results, the grower reduced greenhouse gas emissions and increased the efficiency of nutrient usage. While, there were more hired employees required with the new technology, there was an increase in time allocated to managerial decision making. Net present value calculations indicate that the new technology was desirable from an economic standpoint. The multi-criteria analysis provided additional insights into higher sustainability of sprinkler irrigation in onion production in Quebec. However, further work is needed to develop information on the spill-off costs or benefits for the new technology to the rest of the society, which may assist policy makers develop appropriate policies for sustainable agricultural systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".