Conversion from forest to agriculture leads to soil health decline, which is not mitigated by mulching
Bibliographic record
Abstract
Conversion of forest to agriculture is happening across Canada and may increase northward with changes in climate. Conventional conversion practices lead to significant declines in soil organic matter stocks and degradation of soil health. Farmers in the Rainy River area of Northern Ontario are mulching residual wood into the soil during conversion to bring land into production quickly, while also retaining organic matter that would otherwise be removed. This study investigates if this practice benefits the soil. Thirteen soil response variables were evaluated, and we calculated an overall soil health score for soils collected from nine reference forests, nine fields that were mulched during conversion in the last 10 years, nine fields that have been conventionally converted in the last 10 years, and nine fields that have been in production for over 50 years (cleared conventionally). Five of the soil response variables and the soil health score differed significantly with conversion treatment. Soil health declined from 86 in the forest to 78 in the agricultural fields but there was no effect of time since conversion or mulching. Changes in response variables occurred within 10 years of conversion and there was no effect of mulching on any of the response variables. More time may be required to realize any benefits of incorporating wood mulch to soil.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".