Identifying Barriers and Drivers of Early Soybean Adoption in Saskatchewan
Notice bibliographique
Résumé
The introduction of earlier maturing soybean varieties into Western Canada has created an opportunity for Saskatchewan farmers to add a new crop into their rotations. However, farmers may be hesitant to adopt soybeans if they have less information or knowledge on growing soybeans than they do with other crops they are currently growing. Extension services can provide learning opportunities for farmers and reduce the uncertainty around growing soybeans. Collaborative extension services have been organized by the Saskatchewan Pulse Growers to facilitate adoption of soybeans in Saskatchewan. This thesis aims to assist extension service providers in the designing of future extension services by identifying factors that lead farmers to adopt, not-adopt, or dis-adopt soybeans, as well as identifying factors shared between these three adopter categories. With less than one percent of Saskatchewan cropland in soybeans, this research is studying the very early stages of adoption. Interviews were conducted with 39 farmers throughout southern Saskatchewan in the summer of 2016. Of these farmers, 16 were currently growing soybeans, 10 had grown them in the past, and 13 had never grown soybeans. Through these interviews, economic factors, agronomic factors, and farm characteristics that influence the decision to adopt soybeans were identified. Social capital and absorptive capacity were studied to look at the function they serve in assisting farmers past barriers to adoption. The role and availability of extension services was also examined. A probit model was developed to study the factors that influenced the decision to adopt soybeans. Results from the probit model show that absorptive capacity has a significant positive effect on the probability of adopting soybeans. Required gross return per acre is found to have a negative impact on the adoption decision. An OLS model was run with years growing soybeans as the dependent variable to analyze the factors that led to farmers growing soybeans for a longer period. Results from the model show expected profitability of soybeans and participation in on-farm soybean trials have a significant positive effect on the number of years growing soybeans. Age had a quadratic impact on years growing with the longest years growing at the age of 41. Social capital and absorptive capacity had a discernible impact. Farmers reported they are satisfied with the amount of support and information available to them about growing soybeans, signalling extension services are functioning very well. Involvement in on-farm soybean trials had a significant positive effect on the number of years growing soybeans implying the importance for extension service providers to continue to create these opportunities for farmers. When asked to identify barriers to adoption, the need for higher soybean yields and earlier maturity dominated the response. Farmers in the sample also favoured further investments in breeding over agronomic research.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».