Wheat straw biochar amendment significantly reduces nutrient leaching and increases green pepper yield in a less fertile soil
Bibliographic record
Abstract
Declining soil fertility and inefficient water and nutrient use pose a growing challenge to increasing agricultural production to meet growing global food demand. As a soil amendment, biochar can potentially serve in addressing these issues; however, its impacts on nutrient leaching from soils of different pre-existing fertility levels are poorly understood. A potted green pepper (Capsicum annuum L. var. Red Night) production system, arranged in a randomized complete block design, imposed two soil fertility management approaches (‘fertile’: standard soil + [N:P:K (kg ha −1) 140:165:160] vs. ‘less fertile soil’: 1:1 standard soil : sand, [N:P:K (kg ha −1) 140:190:240], factorially combined with three levels of wheat straw biochar amendment [0%, 1%, and 3% (w/w)]. Biochar treatment effects on nutrient leaching (NO3−-N and PO43−-P) and plant yield were assessed for each soil fertility management approach. Across soil fertility types, biochar amendments (vs. the lack thereof) significantly decreased (p≤0.05) leachate volume (68%–91%) and cumulative NO3−-N (78%–93%) and PO43−-P (80%–99%) losses, whereas NO3−-N, and PO43−-P concentrations in the leachate were only significantly reduced (p≤ 0.05) under the 3% biochar amendment. Pepper marketable yield in the less fertile soil was significantly (40%, p≤0.05) greater under the 3% biochar amendment than the non-amended treatment; however, no such difference existed in the fertile soil given its initially high soil nutrient levels. While farmers can amend soils with biochar to reduce nutrient leaching, its impact on plant productivity will depend on the rate of amendment.
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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 | 0.000 |
| 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".