Causal relationships from legume crops to soil microbial properties relative to canola
Bibliographic record
Abstract
Abstract Legume crop rotational effects are usually studied in only the first subsequent non‐legume crop even though several consecutive crops usually follow a legume. We studied the effects of field pea ( Pisum sativum L.), faba bean ( Vicia faba L.), faba bean green manure (faba GM), lentil ( Lens culinaris Medik.), canola ( Brassica napus L.), and wheat ( Triticum aestivum L.), on soil microbial biomass carbon (MBC), β‐glucosidase enzyme activity, and bacterial physiological diversity in three subsequent crops at four locations. Generalized linear modeling (SAS PROC GLIMMIX) indicated that, when canola is grown 3 yr after an initial canola crop in the rotation, MBC is 4–40% (Bonferroni‐adjusted limits at 95% confidence) greater than when faba bean is the initial crop. Path modeling (SAS PROC CALIS) confirmed the negative effect on MBC by faba bean (relative to canola) as an initial crop. According to Bonferroni‐adjusted limits at 95% confidence, where faba GM was the initial crop, β‐glucosidase activity was 2–24% greater than where faba bean was the initial crop and 0.4–21.8% greater than where wheat was the initial crop. Similarly, where lentil was the initial crop, β‐glucosidase activity was 2–23% greater than where pea was the initial crop and 3–5% greater than where wheat was the initial crop. Path modeling revealed direct causal relationships from faba GM and lentil to β‐glucosidase activity. The legume crop effects on soil microbial properties were: faba GM > lentil > pea > faba bean, but we did not observe diminishing effects of the initial legumes to consecutive subsequent crops.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".