A scoping review of the digital agricultural revolution and ecosystem services: implications for Canadian policy and research agendas
Bibliographic record
Abstract
The application of technologies such as artificial intelligence, robotics, blockchain, cellular agriculture, and big data analytics to food systems has been described as a digital agricultural revolution with the potential to increase food security and reduce agriculture’s environmental footprint. Yet, the scientific evidence informing how these technologies may impact or enhance ecosystem services has not been comprehensively reviewed. In this scoping review, we examine how digital agricultural technologies may enhance agriculture’s support of ecosystem services. Keyword searches in academic databases resulted in 2337 records, of which 74 records met review criteria and were coded. We identify three clusters of digital agricultural technologies including those that make farm management more precise, increase connectivity, and create novel foods. We then examine modelling and empirical evidence gaps in research linking these technologies to ecosystem services. Finally, we overview barriers to implementing digital agricultural technologies for better ecosystem services management in the Canadian context including economic and political systems; lack of policies on data management, governance, and cybersecurity; and limited training and human resources that prevents producers from fully utilizing these technologies.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".