Assessment of soil health and identification of key soil health indicators for five long-term crop rotations with varying fertility management
Bibliographic record
Abstract
Long-term agricultural management practices affect soil health, but identifying measurable soil properties that reflect soil health (soil health indicators – SHIs) is a challenge. Five long-term (>40 years) crop rotations with varying cropping frequency, crop diversity, fertility and lime treatments were sampled as part of the Soil Heath Institute’s North American Project to Evaluate Soil Health Measurements (NAPESHM) at the University of Albert Breton Plots research site in 2019, and additional samples were collected in 2020 for additional indicator measurements. Many soil health frameworks such as the Cornell Assessment of Soil Health (CASH) generate soil health scores based on key SHIs identified from a large, multivariate database using principal component analysis (PCA). For our dataset, we adopted the PCA-based approach with the following questions in mind: 1) Were the key SHIs that explain a large portion of the total dataset variance also the key SHIs that were most sensitive to the rotation, fertilizer and lime management? 2) Were the key SHIs identified with PCA associated with soil health, soil fertility or inherent soil characteristics? We identified seven key SHIs with PCA that explained 86.8% of the database variance. Results of a permutational MANOVA suggested that crop rotation and fertilizer management significantly influenced the total variance of the identified key SHIs. Four of the seven identified key SHIs primarily reflected soil health and three SHIs primarily reflected soil fertility and/or inherent soil properties but were also positively associated with soil health at this site. Overall, the PCA-based approach used to develop the site-specific soil health score (SSpeSHS) proved to be a helpful screening tool for the identification of key SHIs that are sensitive to soil-health-promoting management practices from a large dataset.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".