Multiple soil health indicators are responsive to summer cover crops on an irrigated organic farm
Bibliographic record
Abstract
While cover crops (CC) are known to enhance soil health, outcomes are often subtle and confined to a shallow surface soil layer. We assessed 15 soil health indicators over three CC trials with a 15-species Blend polyculture, a Mustard biculture (white ( Sinapsis alba L.) and brown ( Brassica juncea (L.) Czern.) mustards), Buckwheat ( Fagopyrum esculentum Moench), and Faba bean ( Vicia faba L.) monocultures, and a Weedy fallow (no CC, weeds allowed to grow) on an organic farm in southern Alberta. Soil sampling times included (i) summer pretermination; (ii) fall post-termination; and (iii) spring post-termination of CC. Twelve of 15 soil health indicators showed significant effects of CC treatment for at least one sampling time. Soil organic C (SOC) ranked highest with 80% of sampling times showing significant CC effects. N-related indicators (total N (TN), nitrate-N)) were also quite sensitive, being significantly affected by CC treatment at 60% of sampling times. Three soil health indicators (acid phosphomonoesterase (AcP), wet aggregate stability, and free-living nematodes (FLN)) were consistent in their nonresponses to CC treatment at all sampling times. Comparing CC treatments with a Weedy fallow, showed that not all enhancements of soil health were explained by inclusion of a CC, with Weedy fallow as effective for some indicators. A polyculture Blend significantly enhanced soil health over a monoculture CC or Weedy fallow in 46% of instances of soil health indicator improvement. While CC led to enhancement of soil health, results were not always consistent, being contingent on specific indicators.
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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 | 0.000 | 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 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".