Predicting changes in soil organic carbon after a low dosage and one-time addition of biochar blended with manure and nitrogen fertilizer
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Bibliographic record
Abstract
Modeling plays an important role in predicting the long-term effects of biochar on soil organic carbon dynamics. The objective of our study was to apply the Century model to assess changes in temporal soil organic carbon in soil amended with manure and nitrogen fertilizer (MN), with manure and biochar (MB) or with manure, nitrogen fertilizer and biochar (MNB). We determined that, after 115 years, soil organic carbon stocks could not reach a steady state (equilibrium) or pre-cultivation levels, regardless of amendment type. Our results showed that a biennial input of manure and nitrogen fertilizer (MN) led to a 84% increase in soil organic carbon compared to a 79% (MNB) and 70% (MB) increase when amendments contained biochar. However, the quantity of organic matter input from crop residues and amendments was sufficient to increase the active fraction, with a turnover time of months to years, by 86%. In fact, carbon associated with the slow fraction, with a turnover time of 20 to 50 years, was the key driver for soil organic carbon accumulation in all amendment types. Although the passive fraction is the most stable form of carbon in the soil, with a turnover time of 400 to 100 years, once manure and biochar were added to the soil, this fraction increased up to 32%. Our results provided further insight into the ability of Century to accurately predict changes in soil organic carbon stocks when a combination of manure, nitrogen fertilizer or biochar were added to soil. Century predicted soil organic carbon stocks within -1% to +9% of measured values. However, further fine-tuning of the model is required since biochar undergoes chemical transformations (e.g., ageing) and changes soil physical parameters (e.g., bulk density) that can not be currently accounted for in the Century model. Addressing these limitations of Century will also help to increase the relationship between measured and predicted values.
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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 | 0.002 |
| 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