Lessons for Succession Planning in Rural Canada: A Review of Farm Succession Plans & Available Resources in Haldimand County, Ontario
Bibliographic record
Abstract
For local economic developers, succession planning is becoming a more prominent issue due to the aging population and workforce. This is especially true in rural communities like Haldimand County where the economic base is farming, as the average age of farmers continues to increase while fewer youth are entering the profession. Promoting workforce development through succession planning will increase the likelihood that capable and skilled farmers are filling retiree’s positions which will improve economic stability and reduce the risk of farm business failure. This research uses qualitative methods to assess whether or not farmers in Haldimand County are aware of the succession planning process, determine if adequate resources are available to help farmers with the succession planning process, and identify any challenges farmers experience during the succession planning process that are not addressed through the available resources. A list of recommendations on how to mitigate these obstacles is also provided, in hopes that succession planning will become more accessible and utilized. Keywords: Succession planning, farming, agricultural, rural development, workforce development, rural Ontario ------------------------------------------------------------------- Titre: Lecons de planification de la releve dans le Canada rural: un examen des plans de la releve et des ressources disponibles d'une ferme dans le comte de Haldimand, en Ontario. Resume Pour les promoteurs economiques locaux, la planification de la releve devient une question de premier plan du fait du vieillissement de la population et de la main d'œuvre. Cela est particulierement vrai dans les communautes rurales comme le comte de Haldimand ou la base economique est l'agriculture, alors que l'âge moyen des fermiers continue d'augmenter tandis que peu de jeunes rentrent dans la profession. Promouvoir le developpement de la main d'œuvre a travers la planification de la releve augmentera la probabilite des fermiers aptes et competents de combler les postes de retraites qui amelioreront la stabilite economique et reduiront le risque d'echec d'entreprise agricole. Cette etude utilise des methodes qualitatives pour evaluer si les fermiers du comte de Haldimand sont conscients ou non du processus de planification de la releve, determiner si des ressources adequates sont disponibles pour aider les fermiers dans le processus de planification de la releve et identifier tout defi que peut rencontrer un fermier durant le processus de planification de la releve qui n'est pas traite a travers les ressources disponibles. Une liste de recommandations sur la maniere d'attenuer ces obstacles est aussi fournie, dans l'espoir que la planification de releve devienne plus accessible et utilisee.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".