Impact of Biochar Applications on Tropical Soils under Different Land-use Regimes
Bibliographic record
Abstract
The application of biochar to agricultural soils can either be beneficial or detrimental, as well as no clear effect to soils and crops. Therefore, the aim of our study was to investigate the effects of biochar addition on soil chemical and biological properties and nutrient leaching in three tropical soils with different types of land-use (forest, non-intensive and intensive farming). The soils were amended with and without 2% coconut shell (CS) and rice husk (RH) biochars by weight and incubated for up to 360 days. To assess the impact of biochar on soil leaching, 27 unplanted soil columns from the same types of land-use were also amended with and without 2% CS and RH biochars by weight. Five leaching experiments were conducted by passing through 100 ml of deionised water via each of the glass columns containing soil. The biochar addition significantly increased (P<0.05) the soil pH and total carbon, but had a marginal effect on CEC and had a limited effect on microbial activity. Biochar treatments reduced ammonium leaching in the forest soil, but had no clear effect on the other two soils. Our data showed that biochar application at a lower rate can ameliorate soil acidic conditions, enhance carbon sequestration and adsorb ammonium ion. However, the success depends on soil and biochar properties and land-use. The biochar samples studied have a limited capacity to reduce nitrate and phosphate leaching due to high biochar phosphate content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.000 | 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 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".