Assessing AI Adoption in Ontario’s Livestock and Horticulture Sectors: Challenges and Opportunities for Responsible Innovation
Bibliographic record
Abstract
As artificial intelligence (AI) becomes increasingly integrated into Canada’s agri-food sector, its broader adoption remains limited by the absence of standardized practices, regulatory frameworks, and ethical guidelines. Despite these challenges, significant potential exists, as many agricultural operations have yet to fully leverage AI-driven technologies. This review draws on scholarly databases, reports, and government publications to examine emerging AI technologies in Ontario's horticulture and livestock sectors as well as the factors influencing their adoption. As part of the exploratory phase of a larger research project, this review proposes a framework for analyzing AI adoption that incorporates systemic perspectives on capacity development and responsible innovation. It also applies technology content-layer classifications to examine AI technologies such as crop and livestock disease detection, breeding, and feeding efficiency systems. The framework is then applied to identify and prioritize key factors influencing AI adoption in the livestock and horticulture sectors, bridging practical experiences with theoretical insights into AI technology adoption in agriculture.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".