Relationships between field management, soil health, and microbial community composition
Bibliographic record
Abstract
More meaningful and useful soil health tests are needed to enable better on-farm soil management. Our objective was to assess the relationship between field management, soil health, and soil microbial abundance and composition (phospholipid fatty acid analysis (PLFA)) in soil collected from two fields (farmer-designated ‘good’ versus ‘poor’) across 34 diverse (livestock, grain or vegetable cropping) farms in Maritime Canada. Soil health was measured using soil texture, surface hardness, available water capacity, water stable aggregates, organic matter, soil protein, soil respiration, active carbon, and standard nutrient analysis. All soils were medium to coarse textured (<8% clay). Mixed models analysis showed that both CSHA and PLFA were able to resolve statistical differences between cropping systems, however conventional soil chemical analysis was the only testing method to resolve statistical differences between farmer designated ‘good’ and ‘poor’ fields. Principle component analyses determined management history (rotation over previous three years), but not ‘good’ or ‘poor’ field designation, to be an important determinant of soil health. Water-stable aggregates and soil respiration were positively correlated with all PLFA microbial groups, and negatively correlated with sand, P, Cu and Al. Lower-intensity management (perennial forage, mixed annual-perennial cropping), manure application and low tillage were linked to higher soil respiration, water-stable aggregates, fungi, mycorrhizae, Gram negative bacteria, and lower soil available P. Correlations between CSHA and PLFA shows promise for integrating these two tests for improved soil health assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".