Effects of Sorgoleone on Soil Microbial Communities and Soybean Nodulation
Bibliographic record
Abstract
Bioinputs offer a promising alternative to synthetic herbicides, reducing environmental impacts, but their effects on soil microbial communities are not well understood. This study assessed the effects of aqueous sorghum extract on soil microbial communities and nodulation in soybean cultivated on sorghum and maize crop residues. The experiment was conducted in a completely randomized split-plot design with five replications, with sorghum or maize crop residues in the plots, and weed control with or without aqueous sorghum extract application in the subplots. Microbial biomass carbon (MBC), basal soil respiration (BSR), microbial quotient (qMIC), metabolic quotient (qCO2), mycorrhizal colonization, and number of viable nodules were measured. Aqueous sorghum extract application reduced MBC (77.92 mg C kg⁻¹ soil) and BSR (40.58 mg C-CO2 kg⁻¹ soil day⁻¹) under sorghum residue treatments, increased qCO2 (indicating higher microbial stress), and reduced qMIC, suggesting lower carbon use efficiency. Soybean mycorrhizal colonization was unaffected, but nodulation was significantly reduced under sorghum residue treatments (37 viable nodules per plant), suggesting an inhibitory effect on soybean-rhizobium symbiosis. These findings indicate that phenolic compounds and quinones in sorghum alter soil microbial activity and impair biological nitrogen fixation, particularly when combined with sorghum crop residues.
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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) | 0.001 | 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".