Determinants of adoption of automation and robotics technology in the agriculture sector–A mixed methods, narrative, interpretive knowledge synthesis
Bibliographic record
Abstract
Automation and robotics technologies in agriculture promise to increase productivity with a smaller environmental footprint. However, adoption of agri-innovations is rarely a simple decision. The decision to adopt is determined by numerous factors. Employing a mixed methods narrative, interpretive knowledge synthesis, we review 72 unique studies between 2017–2021, and conduct a thematic analysis. Noting the innate complexity of agriculture, we identify 13 determinants of adoption of automation and robotic technologies in agriculture: data; farm characteristics and surrounding physical environment; farmer characteristics; policy and regulation; labour’s absorptive capacity; social elements; interoperability; standards; access to information; operational benefits; public infrastructure; technological characteristics; and uncertainty and risk. We conclude with seven observations. First, while automation and robotics are promising agri-innovations, they will not be appropriate or beneficial for all farms. There are other forms of agricultural innovation, and their uptake likely will always vary even within the same commodity and region. Second, taking a reductive approach to understanding adoption of agri-innovations may hinder the transformation to sustainable agriculture production systems; it is important to understand the role of complexity in shaping the dynamic interplay among determinants. Third, public infrastructure is more than just the Internet, yet there was little reference to other forms of public infrastructure in the dataset. Fourth, while many papers argue public policy is important for increasing the adoption of these innovations, few provide concrete policy suggestions or scalable examples. Fifth, trust and transparency are central to adoption. Technology developers need to take farmers concerns and needs seriously. Sixth, technology developers must offer practical solutions to real problems. Seventh, automation and robotics encompasses many technologies, and yet no standard or consistent terminology exists. This makes communication about these innovations more difficult. We propose a typology under the rubric of data-driven agricultural 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 | 0.038 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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 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".