Higher runoff and soil detachment in rubber tree plantations compared to annual cultivation is mitigated by ground cover in steep mountainous Thailand
Bibliographic record
Abstract
Due to high rainfall erosivity and rapid land-use changes, South-East Asia is one of the hot spots of soil degradation worldwide. In recent decades, several studies showed that the expansion of rubber tree (RT) plantations into previously forested areas has caused a major increase in soil erosion. However, the effects of cropland conversion to RT plantations on surface runoff and soil detachment are still unclear. Here we show that mature RT plantations considerably increase runoff and soil detachment compared to annual crops or young RT plantations with intercrop, mostly due to the absence of understorey. Monitoring 1 m2 microplots over four years in mountainous Northern Thailand, we found that runoff and detachment increased with time since the onset of the rainy season and with rainfall height, but more so in mature RT plantations than in young RT plantations and maize. This led to much higher annual soil detachment in mature RT plantations (5.7 kg/m2 on average) than in maize and young RT plantations with intercrop (0.36 kg/m2). We identified two main factors explaining this difference: first, rubber tree leaf litter, although abundant, seemed ineffective in reducing runoff at the end of the rainy season. Secondly, the cover by low-growing plants in mature rubber tree plantation was usually sparse and provided little protection. In particular we showed that increasing the cover by low-growing plants from quasi-null cover to >31% cover decreased runoff coefficient by 32%. Our results demonstrate that afforestation by RT, at least under current management practices and on steep slopes, is overall detrimental to soil conservation but that its effects could be mitigated by the adoption of less intense weeding practices.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".