Farm‐Scale Variation of Soil Quality Indices and Association with Edaphic Properties
Bibliographic record
Abstract
Farm‐scale variation of soil quality indices and association with edaphic properties. Soil organisms can be used as indicators of dynamic soil quality because their community structure and population density are sensitive to management changes. However, edaphic properties can also affect soil organisms and spatial variability can confound their utility for soil evaluation. We evaluate the relationship between two important agronomic functions, N‐mineralization potential and aggregate stability, and biological, chemical, and physical edaphic properties. Decomposers, nematodes, collembolans, total C, total N, pH, bulk density (D b ), and texture were evaluated at 81 sites across 25‐ha of an organic farm in western Washington. We built regression trees with biological, chemical, physical, and management parameters to explain the farm‐scale variation in microbial biomass ( r 2 = 0.74), nematode density ( r 2 = 0.61), collembolan density ( r 2 = 0.36), nematode structure index (SI, r 2 = 0.41), nematode enrichment index (EI, r 2 = 0.54), proportion of soil as aggregates > 0.25 mm ( r 2 = 0.60) and N‐mineralization potential ( r 2 = 0.58). Soils with microbial biomass > 597 μg C mic g −1 formed a homogeneous group with the greatest N‐mineralization potential, and soils with >13.5% clay formed a homogeneous group with the greatest proportion of soil aggregates > 0.25 mm. Increased soil aggregation was associated with larger nematode SI, though much of the variability in SI remained unexplained by the data. Tillage had a strong effect on both decomposer and nematode populations; soils not tilled for 5 yr had the largest microbial biomass and soils not tilled 2 wk before sampling had the largest nematode populations. Comparisons of soil quality indicators across farms should be sensitive to the association of indicators with soil texture and recent management practices.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".