Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity
Bibliographic record
Abstract
Abstract The site‐specific cultivation as part of the precision‐agriculture concept is more and more introduced into practical farming. However, soil information is often not available in a spatial resolution intrinsically needed for precision farming or other site‐specific soil use and management purposes. One approach to obtain spatially high‐resolution soil data is the non‐invasive measurement of the apparent electrical conductivity (ECa). In this study, we recorded the ECa on three fields with an EM38 (Geonics, Canada). The ECa data were compared with (1) ground truth data obtained by conventional drilling, (2) traditional soil maps (large scale, ≤1:5,000), (3) the growth and yield of corn. The temporal variability of the ECa due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the ECa values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the ECa. Furthermore, in soils with differently textured layers, ECa was used to estimate the thickness of the uppermost loess layer. A comparison of ECa and large‐scale soil maps reveals some pros and cons of ECa measurements. The main advantages of ECa recordings are the high spatial resolution in combination with low efforts. Yet, the ECa signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the ECa pattern can be a precise indicator for the spatial distribution of different soils. A strong conformity of the spatial variability of plant growth (derived from orthophotos and yield maps) and ECa patterns within a field indicates that the ECa signal per se—without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, ECa surveys can be a powerful tool to facilitate and improve conventional soil mapping.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".