Early effects of slash-and-burn cultivation on soil physicochemical properties of small-scale farms in the Tapajós region, Brazilian Amazon
Bibliographic record
Abstract
SUMMARY Increasing human occupation of the Brazilian Amazon has led to the intensification of deforestation over the last 50 years. The present study is aimed at analysing the impacts of the first year of slash-and-burn cultivation on soil physicochemical properties. Sampling was done in 26 small-scale farms of the Tapajós River basin. In August 2004, soil samples were collected from primary forest plots planned for slash-and-burn cultivation. In September 2005, 1 year after the initial burning and the beginning of cultivation, the same sites were re-sampled. The results indicated that soil fertility after burning was relatively moderate, as the increase of base cations was not particularly marked. Moreover, although an increase of some nutrients (such as exchangeable phosphorus) was observed at soil surface, total carbon and nitrogen (N) pools did not change significantly. Nutrient leaching was also detected through the accumulation of both forms of available nitrogen (NO 3 and NH 4 ) as well as potassium in subsoil horizons. In addition, signs of erosion were seen, as a significant increase surface density occurred, coupled with up to 25% fine particle loss at the surface. The present study draws attention to the early impacts of slash-and-burn agriculture on soil properties within a year of cultivation. Furthermore, its regional dimension highlights undisturbed soils natural variability as well as differentiated responses to deforestation according to soil texture.
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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 | 0.001 |
| 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".