Impact of Agricultural Activities on Soil Particle Size, pH, and Organic Matter in the Dimonika Biosphere Reserve, Mayombe (Republic of Congo)
Bibliographic record
Abstract
Land-use change disrupts several soil physico-chemical parameters. This study aimed to analyze the influence of agricultural activities, topography, and localities on soil texture, pH, and organic matter in the Dimonika Biosphere Reserve. To achieve this, 90 soil samples were collected using an auger based on land use types, topography, and localities. Analyses of soil texture, pH, total organic carbon (TOC), and total nitrogen (TN) were conducted at the IRSEN laboratory in Pointe-Noire. The results showed that clay texture predominates in the studied area, which is related to the nature of the soils. The Kruskal-Wallis ANOVA test highlighted a significant effect of agricultural activities on soil acidity, with an acidic pH ranging from 4.2 to 5 in cultivated areas, compared to a very acidic pH (3.5 to 4.2) in mature forests. However, neither topography nor localities affected pH. Total organic carbon significantly decreased in old plantations (1.3±0.3%) and fallows (1.5±0.5%) compared to mature forests (1.9±0.5%) and savannahs (2.3±0.6%), while total nitrogen showed no notable variations. Topography also had no influence on organic status (TOC, TN). However, at the local level, the Makaba area stood out with significantly higher TOC (2±0.5%) and TN (0.2±0.03%) compared to Les Saras (0.14±0.03%) and Kayes (0.16±0.2%). The C/N ratio, influenced only by agricultural activities, was below 15, indicating rapid organic matter mineralization and a low TOC content. Several potential solutions were proposed for sustainable soil management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".