Farm Characteristics and Resources: The C5.0 Classification Tree as a Means Towards Understanding Finnish Family Farmers’ Perceptions of Success

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Studies in Agricultural Economics2025https://doi.org/10.7896/j.3022article
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What the paper says

Tree as a Means Towards Understanding Finnish Family Farmers' Perceptions of SuccessThis study investigates the classification of the family farmers' perceptions of success, based on characteristics and resources.The empirical analysis was undertaken on primary data collected via a questionnaire completed by family farmers in Finland.The most important variables in the classification are identified using the C5.0 decision tree algorithm.The algorithm performs with an approximately 16% error rate.In the classification of family farmers' perceptions of success, farm characteristics are of minor importance, whereas the most important variables relate to resources and skills.The most important variables classifying perceptions of success are skills for exploiting opportunities, funding opportunities, and technology, machinery and equipment.The importance of the factors of resources (capital, capability, organisational, skills) are interpreted, together with factors of success (financial, self-realisation, growth and family).This study provides a further indication of the potential of the methodology to highlight the role played by farm characteristics and resources in family farm success.

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https://doi.org/https://doi.org/10.7896/j.3022

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@article{unknown2025,
  title        = {{Farm Characteristics and Resources: The C5.0 Classification Tree as a Means Towards Understanding Finnish Family Farmers’ Perceptions of Success}},
  author       = {Unknown author},
  journal      = {Studies in Agricultural Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.7896/j.3022},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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