Applying provincially derived pedotransfer function and digital soil map predictions of nitrogen indices at the field scale
Bibliographic record
Abstract
There is a pressing need to provide crop producers with tools for estimating soil nitrogen (N) mineralization at the local level and to support variable N rate technologies. With provincial-scale datasets being used to train predictive models, there is an opportunity to observe how these predictions perform at the field scale, especially in fields below provincial soil quality benchmarks. This study examined how provincially derived decision support tools (DSTs) for estimating growing season N mineralization performed at the field scale for informing N fertilizer recommendations. DSTs included a 30 m resolution digital soil map (DSM) and novel pedotransfer functions (PTFs) developed using machine learning and predictor variables from a provincial database. Six agricultural fields were sampled for comparison with DST predictions, as well as to create novel infield maps (5 m resolution) of N indices. DSTs were successful at placing field N mineralization potential in context to provincial averages, achieving PTF predictions with a concordance (CCC) as high as 0.91 in comparison to direct soil measures, and 71%–100% of these direct measures within the 90% confidence interval of DSM predictions. Uncertainty decreased substantially using infield data, and infield maps of N indices achieved a CCC as high as 0.83. Field-scale predictors for N mineralization were different from DSM results, with relief variables having high importance. This study demonstrated how provincially derived PTF and DSMs of N indices could be used to inform N fertilizer recommendations. Development of field-scale N maps also introduces a powerful tool for guiding variable rate N applications.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".