Different rates of biochar application change <sup>15</sup> N retention in soil and <sup>15</sup> N utilization by maize
Bibliographic record
Abstract
Abstract Biochar application to soil may impact soil nitrogen (N) dynamics, but the effects on N uptake and utilization by crop remain largely unknown, especially the effects of the rate of biochar application. To investigate the effects of biochar on soil 15 N retention rate and 15 N utilization efficiency ( 15 NUE) by maize, a six‐month 15 N isotope tracer technique combined with in situ pot experiment was conducted in Mollisol. The experiment included four treatments: no biochar applied (CK) and biochar applied at the rates of 12 t ha −1 (P12), 24 t ha −1 (P24) and 48 t ha −1 soil (P48). Compared with CK, biochar application reduced soil bulk density and 15 N loss rate, and significantly improved total N and 15 N retention amount in the 0–30 cm soil depth. The P24 treatment had the largest increase in 15 N retention rate throughout the 0–40 cm depth. After biochar application, the 15 N uptake and 15 NUE were significantly increased in the grain and leaf, which promoted grain yields. Contrary to this, the P48 treatment appeared to lower 15 N uptake and 15 NUE compared with P12 and P24. In conclusion, biochar application improves the potential of the soil to retain N and the improvement in 15 N uptake and utilization are more pronounced in maize leaves and grain. Moreover, biochar application promotes 15 N utilization in maize plant and improves maize yield. However, when biochar application rate is high (i.e. P48 treatment), the 15 N retention by the soil and 15 N utilization by the maize are reduced markedly compared with P12 and P24.
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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".