Identifying Barriers and Drivers of Early Soybean Adoption in Saskatchewan
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".