Understanding Farmers’ Readiness to Develop a Succession Plan: Barriers, Motivators, and Preliminary Recommendations
Bibliographic record
Abstract
Although succession planning benefits workforce development, rural economic stability, and the sustainability of a farm, few farmers in Canada have a written succession plan. As the farming population ages and fewer people enter the profession, understanding what promotes farmers to prepare succession plans is essential. Our study aimed to understand (a) the priorities farm operators have for developing a succession plan, (b) the factors that delay or motivate succession planning, and (c) the resources that would be helpful for creating a succession plan. Using dyadic multiple case study methodology, we interviewed 35 participants from 16 farms in Alberta, Canada. The thematic analysis revealed seven themes influencing decisions to develop a succession plan: legacy and identity, physical health, government policies, farm growth, professional guidance and expertise, family dynamics, and farm culture norms. From the themes, two overarching variables—risk perception and self-efficacy—shaped farmers’ readiness for succession planning and informed the development of the Farm Succession Readiness Framework. This framework categorizes farmers into four types: Active Planners, Succession Avoiders, Back Burners, and End-of-the-Line Farmers. Farm succession planning is complex and multifaceted, and our findings may assist advisors, policymakers, and researchers in understanding farmers and tailoring interventions to meet their needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".