Soil microbial properties during decomposition of pulse crop and legume green manure residues in three consecutive subsequent crops
Bibliographic record
Abstract
Crop residue decomposition not only is mainly driven by, but also affects, soil microorganisms. However, soil microbial responses to legume crops are usually studied only in one subsequent crop. We compared the soil microbial effects of pea (Pisum sativa L.) and faba bean (Vicia faba L.) pulse crops (grown for seed) with faba green manure (GM) and chickling vetch (Lathyrus sativus L.) GM crops in three subsequent crops. Soil microbial biomass C (MBC), β-glucosidase enzyme activity, and bacterial physiological (C substrate utilization) diversity were measured in the summer (rhizosphere and bulk soil) and fall (bulk soil) in all subsequent crops: wheat (Triticum aestivum L.), canola (Brassica napus L.), and barley (Hordeum vulgare L.). Residues of faba bean (grown for GM, herein called faba GM, or for seed, herein called faba bean) usually resulted in the most soil MBC and β-glucosidase activity relative to the other residues. Faba and vetch GM residues increased bulk soil MBC or β-glucosidase enzyme activity more than pulse crop residues in the first and (or) third subsequent crops. Soil MBC and β-glucosidase activities were often positively correlated with initial crop residue N concentrations and negatively correlated with initial C:N ratios or C concentrations. Bacterial physiological diversity was the least responsive to crop residues and was affected differently by sampling time. β-Glucosidase activity was always greater in the fall after crop harvest than in summer. Therefore, β-glucosidase activity was a more sensitive and consistent biological indicator of crop residue effects, and perhaps soil health, than MBC or bacterial physiological diversity.
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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".