Determining the impacts of deforestation and corn cultivation on soil quality in tropical acidic red soils using a soil quality index
Bibliographic record
Abstract
Forests around the globe have been converted to agricultural land to meet human demands. The investigation of soil quality index (SQI) as affected by land use change is essential to prevent and control soil degradation mainly in rapidly developing nations. Research on the effects of land-use change on soil quality, especially within deep soil layers, remains lacking despite the prevalence of forest conversion. Here, we selected six paired plots in an intact forest and an adjacent corn field and collected soil samples from 11 layers at depths of 0–140 cm. We then evaluated 16 soil variables for inclusion in a minimum data set and built a SQI from this dataset. Our results indicate that soil organic carbon, total nitrogen, potassium, and free iron are the most important indicators of soil quality in tropical acidic red soils. Deforestation and corn cultivation related to significant decreases in SQI. Of note, SQI decreased to a differing extent among different soil layers, implying that degradation was not constant among layers, despite the fact that tilling typically affects only the top 0–20 cm of soil. The effect of agricultural conversion on soil quality was more pronounced in topsoil soil layers than in the deep layer. The main driver of soil degradation in corn fields was found to be reduced total nitrogen, followed by reduced potassium. Therefore, mitigating or reducing the loss of these nutrients is recommended, possibly through fertilization. We also note that active iron plays an important role in maintaining soil organic carbon concentrations, and thus is critical for maintaining soil quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".