Influence of cropping system and soil type on soil health
Bibliographic record
Abstract
There has been a continued adaption and application of soil health tests across all regions of the globe; however, there are challenges related to the interpretation of the results of soil health tests developed in one region but applied elsewhere. To determine the factors that are the most important for interpreting soil health tests in Nova Scotia, a soil health database was constructed using soil samples from diverse cropping systems and soil orders in the region. The Comprehensive Assessment of Soil Health, developed at Cornell University in New York, was adapted and combined with other soil health measures. Principal component analysis (PCA), analysis of variance, and a correlation analysis were applied to the dataset to determine if management (i.e., cropping system) or pedogenesis (i.e., soil order) was more important as a driver of soil health test results. It was determined that cropping systems explained more variance in the dataset than soil order. Although total carbon explained the most variation in the PCA, it was highly correlated to other measures of carbon such as permanganate oxidizable carbon and respiration that may be more responsive to management changes. In addition, autoclaved-citrate extractable protein, a test for nitrogen mineralization, was more related to measures of soil carbon than other measures of soil nitrogen availability. The findings of this study provide a foundation for interpreting soil health testing results for this region and will help indicate which cropping systems and soil management practices have the greatest potential for improving soil health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".