Effect of Cultivation and Soil Tillage Systems on the Microbial Biomass in Castor Bean Crop at the Irecê Plateau, Bahia
Bibliographic record
Abstract
Inadequate soil management alters the microbiological attributes of the soil, causing reduction in microbial biomass and activity. Microbial biomass is the living and active part of the soil and can serve as an indicator of changes in the quantity of due to changes in land use. This study aimed to evaluate the effect of intercropping and soil tillage systems on the microbial biomass in castor bean-based crop in the Irecê Plateau, Bahia, Brazil. The experiment was carried out on an eutrophic Haplic Cambisol with clay texture in the Mata Verde Farm of Alto do Quindinho, municipality of São Gabriel, Irecê Plateau, Bahia, Brazil. Six intercropping systems were evaluated including solely castor bean (control) and castor bean intercropped with each of the castor bean cake, common bean, pigeon pea, corn, and gliricidia. Plowing + harrowing and subsoiling were the two soil tillage techniques associated to the intercropping systems. C and N contents in microbial biomass (Cmic and Nmic), soil basal respiration and metabolic quotient were determined in soil samples collected from the 0-10 and 10-30 cm layers. The different soil management systems influenced microbial biomass and activity, and the most suitable conditions for soil microbiota occurred in the soil tillage system with subsoiling. In the semi - arid condition, at 0-10 cm depth, the castor bean + castor bean cake crop system promoted an increase of Cmic content, and the castor + gliricidia system increased Nmic content, both under soil tillage with subsoiling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".