Keeping the Actors in the Organic System Learning: The Role of Organic Farmers’ Experiments
Notice bibliographique
Résumé
<p>The creative process that leads to farmers’ innovations is rarely studied or described precisely in agricultural sciences. For academic scientists, obvious limitations of farmers’ experiments are e.g. precision, reliability, robustness, accuracy, validity or the correct analysis of cause and effect. Nevertheless, we propose that ‘farmers’ experiments’ underpin innovations that keep organic farming locally tuned for sustainability and adaptable to changing economic, social and ecological conditions. We first researched the structure and role of farmers’ experiments by conducting semi-structured interviews of 47 organic farmers in Austria and 72 organic/agroecology farmers in Cuba in 2007 and 2008. Seventysix more structured interviews explored the topics and methods used by Austrian farmers that were ‘trying something’. Farmers engaged in activities that can be labelled as farmers experiments because these activities include considerable planning, manipulating variables, monitoring effects and communicating results. In Austria and Cuba 487 and 370 individual topics, respectively, were mentioned for experimenting by the respondents. These included topics like the introduction of new species or varieties, testing various ways of commercialization or the testing of alternative remedies. Two thirds (Austria) and one third (Cuba) of the farmers who experimented had an explicit mental or written plan before starting. In both countries, the majority of the farmers stated that they set up their experiments first on a small scale and expanded them if the outcome of the experiments was satisfactory. Repetitions were done by running experiments in subsequent years and the majority of the farmers monitored the experiments regularly. In both countries, many experiments were not discrete actions but nested in time and space. For further research on learning and innovation in organic farming we propose an explicit appreciation of farmers’ experiments, encouraging further in-depth research on the details of the farmers’ experimental process and encouraging the inclusion of farmers’ experiments in strategies for innovation in organic and non organic farming. Strategic research and innovation agendas for organic farming would benefit from including organic farmers as co-researchers in all steps of the research process in order to encourage co-learning between academic scientists and organic farmers.</p>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».