Early improvement of soil health under cropping systems including perennial forage crops and dairy cattle slurry in a cool humid climate
Bibliographic record
Abstract
In the past decades, dairy farms in eastern Canada have dedicated more land to annual crops and less to perennial forages. Concurrently, there has been a shift in cattle manure management from solid to liquid forms. Our objective was to determine the early response of soil health indicators (soil aggregate stability to water, carbon [C] and nitrogen [N] in microbial biomass, particulate organic matter, and nonfractionated soil) to two crop types (annual crops vs. perennial forages) and N sources (mineral fertilizer, dairy cattle slurry, and biological N fixation) in the 0–10 cm surface soil at three locations (Ontario, Quebec, and Nova Scotia). Despite some variability in their response, most of the indicators pointed to an improvement in soil health within three years of implementation of perennial forages and/or dairy cattle slurry applications. Particulate organic matter and microbial biomass were generally more responsive to the experimental treatments than soil organic matter. Changes in soil C and N were found at the three locations within the first three years of the experiment. For instance, C concentrations were 7%–31% higher under perennial forages and/or dairy cattle slurry in comparison to annual cropping and/or mineral fertilization. Improvement in soil health was generally greater under perennial forages fertilized with dairy cattle slurry, than under annual crops receiving mineral fertilizer. The response of soil C concentration tended to be more pronounced at sites with lower initial soil C concentrations, and faster in the soil with the highest clay content.
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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 | 0.000 |
| 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) | 0.001 | 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".