Growing Jatropha curcas L. Improves the Chemical Characteristics of Degraded Tropical Soils
Bibliographic record
Abstract
Intensive agriculture in tropical regions is the main cause of soil impoverishment, reducing its productivity. Studies based on soil restoration methods are being implemented, including the use of plants such as Jatropha curcas L., which could have the capacity to improve the agronomic properties of degraded soils in the tropics. The aim of this study is, therefore, to demonstrate that J. curcas L. can improve the characteristics of degraded tropical soil. Between October 2019 and November 2022, we evaluated the effect of spacing, planting material type and age, as well as their interactions, on carbon (C) and nitrogen (N) concentrations and pH at two depths (i.e., 0–10 and 10–20 cm) in the soil. The results reveal that after three years of J. curcas L. growth, C concentration and soil pH increased significantly (p < 0.001) at both depths, while N concentration increased significantly between 0 and 10 cm only. Plants grown from cuttings improved soil pH at 10–20 cm depth more (p = 0.012) than those grown from seeds. Three years after planting, soil N concentration under J. curcas reached a value comparable to that of undisturbed adjacent soil. Overall, our results indicate that J. curcas is a plant that can contribute effectively to restoring degraded tropical soils, therefore contributing to limiting the deforestation of natural forests.
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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) | 0.001 | 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".