Exploring the adoption of beneficial management practices on leased first nations agricultural lands: a modelling approach for integrated nitrogen management
Bibliographic record
Abstract
In Canada, beneficial management practices (BMP) are being used to reduce agricultural greenhouse gas emissions, manage environmental risks, and contribute to national climate goals. A key component of BMP is effective nitrogen (N) fertilizer management, which is essential for improving both soil health and economic profitability and reducing environmental risk. This research employed a modelling approach to evaluate the potential adoption of BMPs related to nitrogen fertilizer management in canola production on agricultural lands on the Mistawasis Nêhiyawak First Nation (MNFN) reserve in central Saskatchewan. The MNFN lands have a unique historical and cultural perspective, where systemic barriers to modern agricultural adoption have limited participation of local farmers and shifted agricultural decision making to non-Indigenous farmers who rent Indigenous governed lands—a common arrangement across most First Nations in the region. The modelling exercise serves as a starting point for engaging with tenant farmers on future nitrogen management strategies that more closely reflect community values and desired outcomes for their lands, including the balance of economic viability with environmental stewardship. Two distinct fertilizer application scenarios were simulated: inorganic nitrogen fertilizer and the integrated use of organic and inorganic fertilizers as BMP for canola yield. Results indicate that the combined approach within the context of the integrated nitrogen management regime could increase crop yields. The economic evaluation highlighted the financial viability of nitrogen management BMPs, leading to higher net present values (NPV). Sensitivity analysis revealed the impact of market fluctuations on economic indicators, particularly prices and costs, indicating that BMPs offered greater resilience against price volatility and rising input costs. This study contributes to ongoing efforts to improve nitrogen fertilizer practices in the region and to facilitate adoption of BMPs, particularly on First Nation reserves in Canada, with spillover benefits for the Canadian agricultural sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".