Modelling soil water retention and water‐holding capacity with visible–near‐infrared spectra and machine learning
Bibliographic record
Abstract
Abstract We need measurements of soil water retention (SWR) and available water capacity (AWC) to assess and model soil functions, but methods are time‐consuming and expensive. Our aim here was to investigate the modelling of AWC and SWR with visible–near‐infrared spectra (vis–NIR) and the machine‐learning method cubist . We used soils from 54 locations across Australian agricultural regions, from three depths: 0–15 cm, 15–30 cm and 30–60 cm. The volumetric water content of the samples and their vis–NIR spectra were measured at seven matric potentials from −1 kPa to −1500 kPa. We modelled the following: (i) AWC directly with the average spectra of the samples measured at different water contents, (ii) water contents at field capacity and permanent wilting point and calculated AWC from those estimates, (iii) AWC with spectra of air‐dried soils, and (iv) parameters of the Kosugi and van Genuchten SWR models, then reconstructed the SWR curves to calculate AWC. We compared the estimates with those from a local pedotransfer function (PTF) and an established Australian PTF. The accuracy of the spectroscopic approaches varied but was generally better than the PTFs. The spectroscopic methods are also more practical because they do not require additional soil properties for the modelling. The root‐mean squared‐error (RMSE) of the spectroscopic methods ranged from 0.033 cm 3 cm −3 to 0.059 cm 3 cm −3 . The RMSEs of the PTFs were 0.050 cm 3 cm −3 for the local and 0.077 cm 3 cm −3 for the general PTF. Spectroscopy with machine learning provides a rapid and versatile method for estimating the AWC and SWR characteristics of diverse agricultural soils. Highlights Soil available water capacity can be estimated with vis‐NIR specta. Parameters of water retention models can be estimated with vis‐NIR spectra. vis‐NIR spectroscopy performed better than pedotransfer functions. The results apply to a diverse range of soils.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".