Predicting soil nitrogen supply from soil properties
Bibliographic record
Abstract
Dessureault-Rompré, J., Zebarth, B. J., Burton, D. L. and Georgallas, A. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63–75. Prediction functions based on simple kinetic models can be used to estimate soil N mineralization as an aid to improved fertilizer N management, but require long-term incubations to obtain the necessary parameters. Therefore, the objective of this study was to examine the feasibility of predicting the mineralizable N parameters necessary to implement prediction functions and in addition to verify their efficiency in modeling soil N supply (SNS) over a growing season. To implement a prediction function based on a first-order (F) kinetic model, a regression equation was developed using a data base of 92 soils, which accounted for 65% of the variance in potentially mineralizable N (N 0) using soil total N (STN) and Pool I, a labile mineralizable N pool. However, the F prediction function did not provide satisfactory prediction (R 2=0.17–0.18) of SNS when compared with a field-based measure of SNS (PASNS) if values of N 0 were predicted from the regression equation. We also examined a two-pool zero- plus first-order (ZF) prediction function. A regression model was developed including soil organic C and Pool I and explained 66% of the variance in k S , the rate constant of the zero-order pool. In addition, a regression equation was developed which explained 86% of the variance in the size of the first-order pool, N L , from Pool I. The ZF prediction function provided satisfactory prediction of SNS (R 2=0.41–0.49) using both measured and predicted values of k S and N L . This study demonstrated a simple prediction function can be used to estimate SNS over a growing season where the mineralizable N parameters are predicted from simple soil properties using regression equations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".