Performance of the Global-Local modelling approach for FT-NIR predictions of SOC and TN in diverse Saskatchewan agricultural soils
Bibliographic record
Abstract
• Global-Local models performed better than Global for predicting SOC and TN. • Site-Specific models outperformed all other models evaluated. • Global-Local model performed similar to the best Lab or Neighbour model. • Spiking performed as well as or better than the Global-Local model, depending on the site. • Inclusion of site-specific samples improved model performance. Precision agriculture requires a reliable, cost-effective method to measure soil organic carbon (SOC) and total nitrogen (TN), and Fourier Transform Near Infrared (FT-NIR) spectroscopy offers a promising solution. Here, we applied the Global-Local model to improve FT-NIR SOC and TN predictions in Saskatchewan agricultural soils. Soil samples (SOC: n = 1876; TN: n = 1442) were collected in 2020 and 2021 from six Saskatchewan agricultural regions. Spectral data were acquired, preprocessed using continuous wavelet transform (CWT), and modelled using Cubist regression. The Global-Local model was applied by combining a small subset of site-specific samples ( Lab ) with their k -nearest neighbours ( Neighbour ) from Saskatchewan spectral datasets. Its performance was compared with Leave-One-Site-Out ( LOSOV ), site-specific, Lab , Neighbour , and traditional spiking. Compared to LOSOV (SOC: R 2 = 0.55 – 0.76, CCC = 0.67 – 0.79, RPD = 1.20 – 1.44), site-specific models gave higher performance (SOC: R 2 = 0.71 – 0.88, CCC = 0.82 – 0.92; RPD = 1.59 – 2.70). The Global-Local model performed better than LOSOV and performed similarly to the best Lab or Neighbour models. Compared to the Global-Local , traditional spiking either improved or gave similar results due to higher variability in target variable and spectra datasets. The more accurate models using either spiking or Global-Local than LOSOV confirms the importance of incorporating site-specific samples into training datasets. Our results indicate that the application of the Global-Local model should be restricted to an individual field level, which was its original purpose. Future studies on optimization of the Global-Local model is needed to scale-up its application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".