Development of decision support tools for strawberry production in Quebec
Bibliographic record
Abstract
Over the last decades, strawberry production in Quebec has evolved in response to consumers' demand for high quality fruits all year round. Growers are gradually adopting new production techniques such as plasticulture, winter row covers or high tunnels. Due to this new complexity of the production, growers need to make numerous short, medium and long-term decisions. Daily decision-making is often related to weed, insect and disease management. Decision support tools, such as prediction models for diseases, can help growers make their decision, in addition to allow a more efficient use of fungicides. The first objective of this study was to develop a weather-based index to predict the development of strawberry powdery mildew. This disease caused by the ascomycete Podosphaera aphanis (Wallr.) is now considered as a major constraint in strawberry production. Several studies show that the development of strawberry powdery mildew is enhanced by new production techniques such as plasticulture systems with day-neutral varieties and the use of high tunnels. Weather data (air temperature, relative humidity and rainfall), disease severity and airborne conidia concentration assessments were used to develop the indices. Their development showed that weather conditions alone are not sufficient to predict disease development. Further studies on strawberry powdery mildew should include additional parameters related to the field (i.e., the phenological stage of the crop or the field disease history). Complexity of the strawberry production can be overwhelming when growers have to make medium and long-term decisions concerning different aspects of their production. Tools for assessing the impacts of decisions on the environment, on the financial aspect of the production and on society can allow a better consideration of all elements involved in the decision. The second objective of this study was to develop a framework for assessing the sustainability of different cropping systems of strawberry. This framework was developed using a qualitative multi-criteria analysis model which is a hierarchical structure that divides a complex problem into smaller elements easier to assess. As part of the framework, the economic, environmental and social dimensions of sustainability were divided into 11, 11 and 4 basic criteria respectively. For validating the framework, two different scenarios were assessed: an integrated pest management (IPM) strategy and another strategy based on an inappropriate use of pesticides. Assessment results were consistent with our expectations since the IPM strategy showed a better environmental sustainability compared to the pesticide-based strategy. Ultimately, the assessment framework could be used as a tool for growers and stakeholders to promote sustainable practices within the strawberry industry.
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 | 0.001 |
| 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) | 0.000 | 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".