Modelling Beneficial Management Practices in Agriculture in Western Canada to Observe Impacts on Greenhouse Gas Emissions and Environmental Sustainability
Bibliographic record
Abstract
Reducing greenhouse gas (GHG) emissions and other detrimental environmental effects of agriculture is a goal paramount to societal stability and prosperity. Understanding the advantages and constraints of beneficial management practices (BMPs) to the fullest extent in varying conditions is imperative for effectively selecting the right interventions tailored to specific farming scenarios. Modelling agricultural management practices and scenarios enables comprehensive testing of simulation experiments to be conducted efficiently, conveniently and at low cost while yielding accurate, representative results. The objectives of this research include: 1) Identify and review existing BMPs for mitigation of GHG emissions within farming systems relevant to the Canadian Prairies, 2) to implement the Holos model software to run simulations of selected farming scenarios and management practices, and 3) to inform future research recommendations in agricultural sustainability and identify existing knowledge gaps. The scenarios modelled focused on the Canadian Prairies, and hence the modelled replicates were evenly distributed across locations within Alberta, Saskatchewan, and Manitoba. A set of beneficial management practices was modelled using the Holos model software. The greatest reduction in farm GHG emissions occurred when nitrogen and phosphorus fertilizer inputs were reduced. The average reduction in emissions from a regime of high inputs to conservative inputs was 26% Kg CO2e. Across a variety of soil types and fertilizer regimes, the average reduction by switching to no-till or reduced tillage from intensive tillage was 24.9% Kg CO2e and 17.6% Kg CO2e respectively. This great reduction was attributed to increased soil carbon sequestration and reduced fossil fuel emissions from farm equipment operations. Livestock dietary changes also resulted in emissions reductions. A high protein diet for beef cattle caused a reduction of 33% Kg CO2e when compared with a low protein diet. High protein diets can increase efficiency of feed utilization (EFU). Fat supplementation and use of ionophores were also found to reduce emissions. Earlier studies have shown that both fat and ionophore supplements directly reduce methane emissions from digestion for beef cattle. The GHG emissions estimates from the Holos model suggest that implementation of beneficial management practices can play a large and important role in reducing emissions in agriculture. These results contribute to a comprehensive, valuable synthesis of the current knowledge base in BMPs for agricultural sustainability and provide deployable insights to guide BMPs implementation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".