Impacts of Land-use Change and Management on Soil Quality on an organic farm in Seeley’s Bay, Ontario
Bibliographic record
Abstract
Changes in land management can have a significant impact on soil quality, which can change the capacity of land to provide essential services to society. We explored the impact of land-use change and land management on an organic farm near Seeley’s Bay, Ontario on a Gananoque Clay soil. Three replicate soil samples were collected to a depth of 40cm (10 cm increments) from a native forest (never cultivated), a field producing hay for over 10 years, and a tilled field used to grow a variety of vegetables. The soils were analyzed by the entire class (GPHY 317) for bulk density, particle-size distribution, organic matter content, pH, soil colour, microbial biomass, and microbial activity. After reviewing the results, we found thegreatest decline in soil quality (using the forest soil as a “reference” for the area) was the tilled field for vegetable production. The tilled site had a higher average bulk density of 1.34g/cc compared to forests bulk density of 0.88g/cc. The forest had a higher average organic matter content of 5% versus an average of 4% found at the tilled site. Additionally, the tilled site had a higher pH (5.8 vs. 4.9) likely due to intermittent liming, and lower water holding capacity. The compilation of the results illustrates the true affects land-management has on soil quality. By understanding the impacts of different land-use methods society can modify current practices to help increase soil quality and prevent the loss of the critical services that healthy soil provides to society.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".