Microbial Activity of a Plinthosol With Application of Thiamethoxam Insecticide and Biochar
Bibliographic record
Abstract
Although thiamethoxam is an insecticide widely used in agriculture, its high mobility and persistence in the soil can result in contamination of groundwater and alteration in biogeochemical cycles. The objective of this study was to verify the effect of biochar, NPK fertilizer and thiamethoxam insecticide on soil microbial properties. The experiment was conducted in a randomized block design composed of the doses combination of mineral fertilizer NPK (0 and 300 kg ha-1 of the formulated 05-25-15), and biochar (0, 8, 16 and 32 t ha-1) in the absence and presence of thiamethoxam. Deformed soil samples were collected in all plots in the 0 to 0.10 m layer to determine the activity of the enzymes: acid and alkaline phosphatase, beta glucosidase and urease, beyond the microbial biomass carbon (MBC), basal respiration rate (C-CO2) and metabolic quotient (qCO2). To compare soil microbiology before and after the application of thiamethoxam, multivariate statistical techniques were used. The application of biochar resulted in increased enzymatic activity of urease, acid phosphatase, increase of qCO2 and basal respiration and reduction of MBC. In contrast, the application of the thiamethoxam insecticide suppressed the enzymatic activity of urease, acid phosphatase, resulting, however, in the elevation of alkaline phosphatase and basal respiration of the soil. Biochar application at doses greater than or equal to 16 t ha-1 resulted in elevation of qCO2 and reduction of MBC, regardless of the absence or presence of NPK chemical fertilization. Biochar effect on soil microbiological attributes is less significant than the effect of thiamethoxam application.
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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".