Responses of soil microbial biomass carbon to tillage and fertilizer types in maize cultivation in Buea, Cameroon
Bibliographic record
Abstract
Although soil microbial biomass (MBC) comprises less than 5% of soil organic matter, it responds rapidly to changes in soil management practices and, therefore, is generally used as an early indicators of changes in soil carbon. The objective of this study was to evaluate the effects of tillage practices (conventional tillage and no-tillage) and fertilizer types (synthetic, organic, and no fertilizer) on soil MBC. The field experiment, located in Buea, was arranged in a split-plot design with three replications and had tillage systems as main plots and fertilizer types as sub-plots. Soil samples were collected at 0–15 cm depth at an interval of 4 (early season), 8 (mid-season) and 12 (late season) weeks during the 2020 and 2021 minor and major growing seasons respectively, for the determination of soil MBC by the chloroform fumigation and extraction method. The findings of the study showed that the main effect of tillage practice and fertilizer types was nonsignificant (p>0.05) in the 2020 and 2021 study season throughout the sampling period. Plots under zero tillage with control experiments (No.Till:CON) recorded the highest soil MBC in the 2020 season (201 mg/kg) while in the 2021 season, plots under zero tillage with organic fertilization (No.Till:ORG) recorded the highest (400.4 mg/kg) soil MBC. Soil MBC was higher in the 2021 season than in the 2020 season. These findings suggest that the use of compost in combination with either conventional tillage or no-tillage in farms in the study area could potentially enhance soil MBC. Key words: Tillage, fertilizer type, microbial biomass carbon, carbon sequestration.
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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 | 0.001 | 0.001 |
| 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".