Field Examinations on the Application of Novel Biochar-Based Microbial Fertilizer on Degraded Soils and Growth Response of Flue-Cured Tobacco (Nicotiana tabacum L.)
Bibliographic record
Abstract
Southwestern China is receiving excessive chemical fertilizers to meet the challenges of continuous cropping. These practices are deteriorating the soil environment and affecting tobacco (Nicotiana tabacum L.) yield and quality adversely. A novel microbially enriched biochar-based fertilizer was synthesized using effective microorganisms, tobacco stalk biochar and basal fertilizer. A field-scale study was conducted to evaluate the yield response of tobacco grown on degraded soil amended with our novel biochar-based microbial fertilizer (BF). Four treatments of BF (0%, 1.5%, 2.5% and 5%) were applied in the contaminated field to grow tobacco. The application of BF1.5, BF2.5 and BF5.0 increased the available water contents by 9.47%, 1.18% and 2.19% compared to that with BF0 respectively. Maximum growth of tobacco in terms of plant height and leaf area was recorded for BF1.5 compared to BF0. BF1.5, BF2.5 and BF5.0 increased SPAD by 13.18–40.53%, net photosynthetic rate by 5.44–60.42%, stomatal conductance by 8.33–44.44%, instantaneous water use efficiency by 55.41–93.24% and intrinsic water use efficiency by 0.09–24.11%, while they decreased the intercellular CO2 concentration and transpiration rate by 3.85–6.84% and 0.29–47.18% relative to BF0, respectively (p < 0.05). The maximum increase in tobacco yield was recorded with BF1.5 (23.81%) compared to that with BF0. The present study concludes that the application of BF1.5 improves and restores the degraded soil by improving the hydraulic conductivity and by increasing the tobacco yield.
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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) | 0.000 | 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 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".