Predicting Soil Nitrogen Mineralization Potential using Pyrolysis-coupled FTIR
Bibliographic record
Abstract
Soil nitrogen (N) is a key component of plant nutrition but our ability to predict organic N mineralization potential remains incomplete. Several methods are commonly used to characterize and measure mineralizable N; however, they are generally lacking because of required lab resources and poor predictive power. Pyrolysis is an emerging technology used to characterize soil organic matter and the thermal stability of soils. However, the idea of using Pyrolysis technology to characterize soil N and measure soil N release is novel. We adopted a novel online pyrolysis coupled with FTIR (Fourier-transform infrared spectroscopy) technology to investigate soil N. The soil samples used were collected from a long-term field trial involving different crop rotations and fertilization to include a wide array of samples. Samples were pyrolyzed from 25 to 850 °C with a heating rate of 10 K min-1. The temperature at which 50% of the material underwent pyrolysis, referred to as T50, was determined to quantify the thermal stability. The focus was to look at mass loss characteristics, identify volatile matter released, T50, and the correlation of TG-FTIR data with a 12-week lab mineralization study. We found a negative correlation (R2= -0.67) between the T50 and mineralized N at week 12. In conclusion, this study elucidates the intricate interplay between temperature kinetics and nitrogen mineralization. The negative correlation between T50 and mineralizable N underscores the potential of the material to release N over time. This research offers a valuable foundation for optimizing Pyrolysis application in the context of soil nitrogen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".