Adoption of Integrated Crop-Livestock in Canada: Does Policy Have a Role?
Bibliographic record
Abstract
Increasing environmental concerns, production volatilities, and risks associated with farming have led to growing calls for more sustainable farming methods. One suggested approach is the adoption of integrated crop-livestock production systems (ICLS), which can be a more economically and environmentally sustainable method of production. Yet, ICLS adoption, either as a single operation or partnership between two or more farmers, in developed countries, including Canada, remains low despite evidence from field-trial data demonstrating the benefits. This study provides evidence for the causes underlying the low interest and adoption of ICLS practices among farmers and beef cattle ranchers in the most agriculturally intensive regions of Canada. First, we use a nested constant elasticity of substitution (CES) production function to model the influence of farmers’ profit-maximising behaviour on their choice of ICLS. Second, we deployed a survey with a discrete choice experiment and used multinomial and mixed logit models to assess willingness to adopt and the likelihood that specific policy interventions would lead to more socially desirable outcomes. The results showed that a lower percentage (35%) of respondents are familiar with ICLS practices, and the low level of ICLS adoption is caused by the lack of infrastructure, high labour requirements under ICLS, and insufficient knowledge of ICLS, among others. Additionally, respondents’ choice of ICLS partnership is influenced by partners’ familiarity, the potential cost change of adopting ICLS, the expected improvement in soil or beef cattle productivity, the value of monetary exchange between farmers, farmers’ knowledge of ICLS, and the availability of cost-sharing pro-grams or incentives between the government and farmers. Given the above, it is recommended that farmer-led groups be created to facilitate knowledge sharing between farmers while providing a platform for farmers to become familiar with one another. Also, the provision of incentives to farmers and ranchers through a cost-sharing program could defray the initial cost of ICLS and increase adoption.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".