Modeling of total and active organic carbon dynamics in agricultural soil using digital soil mapping: a case study from Central Nova Scotia
Bibliographic record
Abstract
Monitoring the changes in soil organic carbon (SOC) pools is critical for sustainable soil and agricultural management. This case study models total and active organic carbon dynamics (2015/2016 to 2019/2020) using digital soil mapping (DSM) techniques. Model predictors include topographic variables generated from light detection and ranging data; soil and vegetation indices derived from Landsat satellite images; and soil and crop inventory information from Agriculture and Agri-Food Canada to predict total organic carbon (TOC) and permanganate oxidizable carbon (POXc) at the 0–15 cm depth increment for a 37 km 2 study area in Truro, Nova Scotia. Quantile Regression Forest and stochastic Gradient Boosting Model were utilized for prediction. Although both models performed equally well for predicting TOC and POXc, the accuracy of TOC predictions (e.g., concordance correlation coefficient (CCC) = 0.67) was better than POXc predictions (e.g., CCC = 0.53). The Landsat variables and crop inventory were dominant predictors, while topographic variables across the relatively homogeneous terrain had relatively little influence. During the study period, changes in POXc were predicted across 98% of the study area, with a mean absolute loss of 5.77 (±11.48) mg/kg/year, and in TOC on 27% of the area, with a mean absolute loss of 0.15 (±0.09) g/kg/year. While the annual crop fields observed the highest loss of TOC and POXc, the decline in pasture–grassland–forage fields was relatively low. The study reinforced the effectiveness of DSM for modeling multiple SOC pools at the farm to landscape scales.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".