Biochar from Empty Fruit Bunches, Wood, and Rice Husks: Effects on Soil Physical Properties and Growth of Sweet Corn on Acidic Soil
Bibliographic record
Abstract
The Intentional amendment of soil with biochar is offering a new strategy for enhancing soil physical properties and soil fertility. Nonetheless, the characteristics of biochars vary with their different conditions and pyrolysis techniques. The objective of the present study was to improve the understanding of how adding biochar applications and the pyrolysis of native feedstock to acidic soil can be utilized to amend soil physical properties and soil fertility in Malaysia. Three kinds of primary biochar were used, empty fruit bunch (EFB) and wood biochar (WB) were produced from slow pyrolysis, and rice husk biochar (RHB) was prepared by gasification. The biochars were characterized by Brunauer-Emmett-Teller surface area analysis and scanning electron microscopy and applied at 15 and 30 t/ha to acidic soil. Results indicated that the total surface area of the RHB was approximately double of that of EFB and five times greater than that of WB. The application of RHB at 30 t/ha significantly increased the drained upper limit, permanent wilting point, hydraulic conductivity, and total porosity; however, this increase did not result in increased sweet corn growth, while EFB applied at rates 30 t/ ha resulted in a highly positive effect on sweet corn growth, suggesting that EFB has important potential benefits for agriculture, in conclusion, the selection of biochar as a soil amendment must be based on the intention of the amendment. If to enhance soil physical charactertics are an aim, then RHB is the most suitable option. If the objective of soil biochar amendment is to increase soil fertility, then EFB will be suitable choice.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".