Different rates of biochar application change <sup>15</sup> N retention in soil and <sup>15</sup> N utilization by maize
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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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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