The Costs of Soil Erosion to Crop Production in Canada between 1971 and 2015
Bibliographic record
Abstract
Canada is known for its massive and fertile landscape, and one of the biggest industries in Canada is crop production, which is responsible for contributing to the national economy as well as the global food supply. Soil erosion is considered the top challenge facing Canadian farmers in the 21st century. This study aims to evaluate soil erosion’s impact on Canadian crop production, assessed based on the integration of soil erosion analysis and multitemporal crop market values from 1971 to 2015. Soil Erosion Risk Indicator models were used to assess soil erosion’s impact on crop productivity using the relationship of soil organic carbon with crop yield gain/loss. The total soil erosion cost of yield losses in the 44 years leading up to 2015 is estimated to be CAD 33.51 billion. 2013 was found to show the highest loss, with CAD 1.93 billion. Oilseeds, small grains, and potatoes were the major crop commodities that were impacted by yield loss as a direct result of soil erosion, the costs being 41%, 37%, and 15%, respectively. Ontario and Saskatchewan were the most impacted provinces, with costs of 45.25% and 22.50%, respectively. Four eras were detected in this research, each having unique soil erosion costs, which reflect different agriculture policy and soil conservation efforts: Era 1 (1971–1988), Era 2 (1989–1995), Era 3 (1996–2007), and Era 4 (2008–2015). This research is the beginning of exploring the cost of the environmental impacts on agriculture sustainability in Canada and supporting decision makers in adopting effective soil conservation strategies to mitigate these impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".