Predicting Soil Bulk Density in Boreal Podzolic Soil using Ground-Penetrating Radar and Electromagnetic Induction
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».