Enabling responsible AI-driven agri-food innovation in Ontario: A framework for analysis of adoption challenges and opportunities
Bibliographic record
Abstract
AI adoption in the agri-food sector offers significant gains in productivity and competitiveness, but responsible implementation is essential to avoid stakeholder resistance and ethical concerns. This study examines the adoption of artificial intelligence (AI) technologies in Ontario’s horticultural and livestock sectors. Applying a systems perspective and responsible innovation, it identifies and categorizes emerging AI applications, develops a conceptual framework to capture technological, social, environmental, individual, and institutional factors, and proposes practical strategies to promote adoption. A structured literature review of peer-reviewed articles, government reports, and industry publications was conducted to manually classify AI technologies into content layer classifications: descriptive, diagnostic, predictive, and prescriptive, and map them to a framework. Diagnostic and prescriptive technologies dominate in horticulture, while AI applications in livestock are fewer and more evenly distributed across functional layers. Out of the 24 technologies identified, only four technologies, three in horticulture and one in livestock, demonstrated all analytical functions, highlighting the need for more integrated AI solutions. Key barriers include high cost, interoperability challenges, data privacy concerns, technical skill gaps, and limited digital infrastructure. Recommendations include promotion of targeted institutional support, operational efficiency, and ethical data governance. The framework provides practical guidance for responsible AI adoption and a foundation for future empirical research.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".