The application of biochar improves the nutrient supply efficiency of organic fertilizer, sustains soil quality and promotes sustainable crop production
Bibliographic record
Abstract
Abstract Rapeseed meal, a nutritious organic fertilizer (OF), contributes to improving soil environment and crop productivity. However, there are also problems, namely slow fertilizer efficiency and low nutrient utilization during the growing season. This 2‐year field trial was conducted to explore the effect of biochar addition on improving the nutrient availability of OF through a comparative study of various biochar application rates under rice‐rapeseed rotation conditions. The findings revealed that, compared to the individual application of chemical fertilizers (CF), OF alone decreased rice yield (2%/2%) and rapeseed yield (6%/10%) in 2019/2020. Compared with OF, combining biochar (15 t ha−1) with OF (OF + B15) significantly increased rice yield (17%/10%) and rapeseed yield (25%/20%) in the first/second year. Additionally, OF + B15 still increased rice yield (14%/7%) and rapeseed yield (12%/13%) for two consecutive years compared to CF. The co‐application of biochar and OF had positive impacts on soil physicochemical properties and enzymes. Compared to OF, OF + B15 elevated soil organic carbon (SOC) by 57%–81%, soil catalase 19%, invertase 14%–20%, urease 17%–19%, and phosphatase 13%–17% during rice season, and similarly increased SOC by 77%–90%, soil catalase 14%–16%, invertase 14%–20%, urease 18%–24%, and phosphatase 16%–17% in rapeseed season. Biochar addition improved soil conditions and enzymatic activities, and the available nutrient supply of OF. Also, the co‐application of biochar and rapeseed meal surpassed the effect of chemical fertilizer alone on the growth and yield of crops. Therefore, biochar coupling with organic fertilizer is an effective fertilization strategy based on resource recycling, which promotes both crop yield and sustainable agriculture.
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 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".