The application of biochar improves the nutrient supply efficiency of organic fertilizer, sustains soil quality and promotes sustainable crop production
Why this work is in the frame
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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.
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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 | 0.000 |
| 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 it