Machine Learning Predicts Biochar Aging Effects on Nitrous Oxide Emissions from Agricultural Soils
Bibliographic record
Abstract
Biochar effects on agricultural soils change over time as biochar ages. To better understand the long-term impacts of biochar application on climate change mitigation, the effect of biochar aging on nitrous oxide (N 2 O) emissions has been widely investigated in field experiments. However, the underlying relationship of N 2 O emissions with biochar properties, fertilization practices, soil properties, and weather conditions is poorly understood. We collected data from 30 peer-reviewed publications with 279 observations and used machine learning (ML) to model and explore critical factors affecting daily N 2 O fluxes. We established and compared models constructed using neural networks (NN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). We found that the gradient boosting regression (GBR) model was the optimal algorithm for predicting daily N 2 O fluxes ( R 2 > 0.90). The importance of factors driving daily N 2 O fluxes is as follows: fertilization practices (44%) > weather conditions (30%) > soil properties (21%) > biochar properties (5%). In addition, the aging time of biochar, potassium application rate, soil clay fraction, and mean air temperature were critical factors affecting the daily N 2 O fluxes. When biochar is initially applied, it can reduce N 2 O emissions; however, it has no long-term effects in reducing N 2 O emissions. The accurate prediction and insights from the ML model benefit the assessment of the long-term effects of biochar aging on N 2 O emissions from agricultural soils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".