Heterogeneous Motivations and Challenges of Regenerative Agriculture in Saskatchewan: Insights from Case Studies and Bioeconomic Modelling
Bibliographic record
Abstract
Regenerative agriculture is an alternative farming system proposed to improve soil health, improve economic sustainability, and mitigate climate change. There are a set of principles commonly agreed upon that help guide producers: limit disturbance, armour the soil, increase plant diversity, keep living roots in the soil, integrate livestock and crop operations, and understand the farm context. Regenerative agriculture is a flexible farming system that can be adapted to producers’ capabilities and conditions. The purpose of our project is twofold. The first is to gather information about the use of regenerative agricultural practices in Saskatchewan. We interview producers throughout Saskatchewan who have experience with regenerative agriculture. We ask a series of questions about their specific practices, barriers faced, and advice for future producers. The information will be shared with the Ministry of Agriculture to support producers interested in regenerative agricultural practices. The second objective is to create a renewable resource model to understand and model the economic motivations of the case studies. Regenerative agricultural practices focus on soil health management and are a large reason for why many adopt these practices. We construct a model where the representative farmer maximizes the net present value of annual net returns by choosing the amount they are willing to invest into building soil carbon stock, subject to the state of a farmer’s lands, defined by soil carbon content. The purpose of the model is to better understand the fundamental trade-off between short-term economic profits and long-term improvement in soil health. We develop a framework that can be applied in the decision-making process for a producer to adopt regenerative agriculture. Our model demonstrates that only farmers in select circumstances will opt-in to regenerative farming without a significant subsidy or cost-sharing program.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".