Quantifying intra-field soil variability using categorical data: A case study of predicting soil organic matter using soil survey maps
Bibliographic record
Abstract
• Two new models predict a continuous soil attribute using zone-level categorical inputs. • Both models outperformed the baseline field-averaging method. • The framework supports scalable prediction with minimal input data. • The framework has the potential to generalize to other soil attributes. Accurate estimation of the spatial distribution of soil properties is essential for advancing precision agriculture. This study evaluates two modeling strategies for predicting a continuous soil attribute, log-transformed soil organic matter (SOM%), using zone-level categorical predictors such as soil type. The first approach employs a zonal regression model incorporating soil-type coefficients, while the second leverages normalized pairwise comparisons among intra-field zones. Both models are assessed against a baseline strategy reflecting composite sampling practice, in which a single field-wise average is assumed. Incorporating categorical structure increased the prediction accuracy; the zonal regression model achieved an RMSE of 0.097 (R² = 0.89), and the normalized pairwise model reached an RMSE of 0.108 (R² = 0.85), both improving upon the baseline field averaging method based on within-field samples (RMSE = 0.140, R² = 0.75). The proposed framework is generalizable to other soil attributes (e.g., pH, CEC, texture fractions) where zone-level delineation is available. This work offers a scalable, interpretable, and field-deployable methodology, contributing to spatial soil inference and site-specific agricultural decision-making under sparse sampling conditions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".