Maize straw is more effective than maize straw biochar at improving soil N availability and gross N transformation rate
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Bibliographic record
Abstract
Abstract Soil nitrogen (N) transformation is vital in determining farmland N availability. Although many studies have investigated the effect of biochar on N retention and loss via leaching and gaseous emissions, few have determined the dynamics of gross N transformation during crop growth in long‐term biochar‐amended soils and compared the effect of the biochar with that of its feedstock. In this study, we conducted a five‐time field measurement of soil gross N turnover rates via 15 N isotope pool dilution during maize growth in 2021. Three treatments were employed, including no amendment, biochar and straw applied annually at rates of 2.63 and 7.50 t ha −1 , respectively, since 2013. The results showed that biochar did not change the rate of gross N mineralisation when compared with no amendment, but straw increased it by 139% in August, resulting in significantly higher cumulative gross N mineralisation than biochar and no amendment (701 vs 489 and 499 mg kg −1 in 200 d). The inconsistent influence was attributed to the fact that inherent biochar‐N was recalcitrant and could not be mineralized like the straw. The gross nitrification rate was decreased by 72.9% and 77.4% by biochar and straw application, respectively, in June relative to no amendment, but then it increased from July to August in the straw treatment as a result of the elevated gross N mineralisation rate. The decreased nitrification in the biochar treatment was an outcome of the synergetic effect of a low ammonium pool (−59.4%) and a high gross ammonium immobilisation rate (+263%), which was likely due to excessive fertilizer N loss and abiotic adsorption to biochar. Meanwhile, biochar amendment inhibited bacterial 16S and fungal ITS genes, as well as ureC and bacterial and archaea‐ amoA gene copies. In conclusion, straw is more effective than biochar at improving soil N transformation and availability in the long term.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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