Predicting Soil Bulk Density in Boreal Podzolic Soil using Ground-Penetrating Radar and Electromagnetic Induction
Bibliographic record
Abstract
Soil compaction is one of the major challenges in sustainable agriculture, primarily due to the use of heavy farming machinery. Tillage and soil compaction influence soil properties, state variables, and processes, ultimately affecting soil health, crop growth, and yield. Traditional methods to estimate soil compaction level, like bulk density (BD) and penetration resistance, are laborious, destructive, time-consuming and provide point-scale measurements only. Near-surface geophysical techniques like Ground-Penetrating Radar (GPR) and Electromagnetic Induction (EMI) are being increasingly utilized to estimate soil properties and state variables in the agricultural landscape since GPR and EMI can address some of drawbacks of traditional methods. However, there is a lack of studies with GPR and EMI examining the BD change associated with tillage and soil compaction. We hypothesize that proxies from GPR and/or EMI can be used to predict BD as an indicator of soil compaction. The objectives were to: 1) evaluate the impact of BD change on dielectric constant (Kr) and direct ground wave amplitude (A) measured from GPR, and apparent electrical conductivity (ECa) measured by EMI; and 2) assess the predictive capability of GPR and EMI for BD determination. The experiment was conducted on a loamy sand textured soil at a boreal podzolic site in Newfoundland, Canada. Proxy data (i.e., Kr, A and ECa) were collected using a 500 MHz center frequency GPR system and an EMI sensor representing three compaction treatments (i.e., after tillage, after 4- and 10-time roller passes). Treatment effects and relationships between proxies and the average BD of 0-30 cm soil depth were tested using analysis of variance (ANOVA) and correlation analysis. A Random Forest (RF) regression approach was employed to identify the most significant variables for predicting BD. Subsequently, simple, and multiple linear regression models (LRM) were developed. The accuracy of these LRMs was assessed by comparing predicted and measured BD values. ANOVA results reveal that the measured BD and proxies are significantly different at all three compaction levels. The average BD strongly correlated with soil proxies; Kr(r=0.72), A (r=0.71), and ECa(r=0.89). Based on RF, ECa and Kr are the most important variables to predict BD for the studied data set. Therefore, ECaand Kr were used to develop simple and multiple LRMs. The simple LRM developed with ECa showed a higher coefficient of determination, R2=0.80, compared to Kr (R2=0.63), while the multiple LRM showed the highest R2 (R2=0.83). The model predicted BDs did not deviate from 1:1 line with a root mean square error of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".