Developing a pedotransfer function for the prediction of nitrogen mineralization in the agricultural soils of Quebec
Bibliographic record
Abstract
Effective nitrogen (N) management is crucial for maximizing crop yields while minimizing environmental impacts. In the agricultural systems of Quebec, soil organic matter mineralization supplies a significant portion of crop N demand, but direct quantification is challenging and costly. This study utilized zero-N trial data to evaluate a two-pool zero-plus first-order kinetic model for predicting growing season N mineralization (GSNM) based on total N (TN) and potentially mineralizable N (PMN). Additionally, machine learning-based pedotransfer functions (PTFs) were developed to predict TN, PMN, and GSNM from easily measurable soil properties using a large soil health dataset ( n = 3117). The kinetic model showed strong agreement between predicted and observed soil N supply, especially with the inclusion of deeper soil layers and early-season mineralization estimates. Recursive feature elimination identified total carbon (TC) and clay as the best predictors for TN, yielding a Lin's concordance correlation coefficient (CCC) of 0.93 and a coefficient of determination ( R 2 ) of 0.86, while soil respiration (SR) and pH best predicted PMN (CCC = 0.89, R 2 = 0.80). For GSNM, SR, TC, and pH were the top predictors (CCC = 0.91, R 2 = 0.83). The developed PTFs provide a practical framework for estimating soil N-pools where direct data is unavailable, ultimately improving site-specific N management decisions. These tools support more efficient fertilizer use and minimize environmental losses. Future research should focus on integrating soil management practices into the development of PTFs and considering spatial and landscape variability through digital soil mapping.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".