Reviewing the role of biochar in paddy soils: An agricultural and environmental perspective
Bibliographic record
Abstract
The main challenge of the twenty-first century is to find a balance between environmental sustainability and crop productivity in a world with a rapidly growing population. Soil health is the backbone of a resilient environment and stable food production systems. In recent years, the use of biochar to bind nutrients, sorption of pollutants, and increase crop productivity has gained popularity. This article reviews key recent studies on the environmental impacts of biochar and the benefits of its unique physicochemical features in paddy soils. This review provides critical information on the role of biochar properties on environmental pollutants, carbon and nitrogen cycling, plant growth regulation, and microbial activities. Biochar improves the soil properties of paddy soils through increasing microbial activities and nutrient availability, accelerating carbon and nitrogen cycle, and reducing the availability of heavy metals and micropollutants. For example, a study showed that the application of a maximum of 40 t ha−1 of biochar from rice husks prior to cultivation (at high temperature and slow pyrolysis) increases nutrient utilization and rice grain yield by 40%. Biochar can be used to minimize the use of chemical fertilizers to ensure sustainable food production.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".