Relationships between field management, soil health, and microbial community composition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 it