Investigation of Cover Crop Adoption as a Soil Conservation Practice Across Southern Ontario
Bibliographic record
Abstract
Diversifying cropping systems with cover crops is an essential practice to maintain production and conserve the environmental impacts of agriculture under-stressed climatic condition. This study utilized OMAFRA’s Field Crop Data (2017), and Agriculture and Agri-Food Canada (AAFC) Annual Crop Inventory (ACI) datasets with Esri ArcMAP; to investigate crop diversity trends, and cover crop occurrence throughout corn and soybean systems. This study determined that field crop diversity is declining, and cover crops do not have a high adoption rate in corn and soybean systems. The Universal Soil Loss Equation (USLE) was then employed to assess the correlation between soil loss-sensitive fields with cover crop adoption. Investigating the south Simcoe Watershed as a case study, this research revealed that only a small portion (18.2%) of agricultural operations incorporate cover crops. These findings suggest that cover crops are an underutilized conservation technique that needs to target fields that have yet to adopt cover crops and operations that are located on erosion-prone soils in Southern Ontario.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".