Advancing digital soil mapping with multi-year crop cover data: Impacts on model accuracy and soil interpretation
Notice bibliographique
Résumé
Vegetation cover has a significant influence on soil properties and is commonly used as a covariate in digital soil mapping (DSM). Crop frequency (CrFr) covariates, representing the frequency with which a certain crop or class of crops are grown over multiple years, can be derived from multi-year vegetation data. Such data have the potential to provide promising insights into soil conditions and can enhance predictions of soil properties. Predictive modelling within a DSM framework can improve our understanding of the relationship between crop cover and different soil properties. This study had two main objectives: (1) to develop DSM models for six soil properties—bulk density (BD), organic carbon (OC), A horizon thickness (AT), total nitrogen (TN), pH, and cation exchange capacity (CEC)—both with and without CrFr covariates, and to compare their accuracy metrics; each soil property was modelled independently as a separate response variable; and (2) to investigate the relationships between covariates such as crop types, precipitation, and temperature and soil properties. The study was conducted in the Ottawa, Canada, region, an area with diverse crop cover. From 13 years of Annual Crop Inventory (ACI) raster data, five CrFr covariates were generated and added to other covariates commonly used in DSM, resulting in a total of 54 covariates for model training. Twelve models were developed for the six soil properties, both with and without CrFr covariates. Validation results showed that including CrFr covariates improved the accuracy of models for BD, OC, AT, and TN. However, the impact on models for pH and CEC was minimal, indicating that intrinsic soil factors likely influence these properties more than CrFr. Partial dependence plots indicated that the models captured expected patterns, such as the negative association of forest cover with BD and its positive relationship with OC and TN. In contrast, crops such as legumes and corn exhibit the opposite effects. Forests exhibited a negative relationship with AT, whereas croplands showed a positive association, indicating a likely difference between the Ap horizon and Ah. Uncertainty analysis revealed lower uncertainty in agricultural cropland areas and those with lower elevations. This study highlights the potential of DSM in assessing the impact of crop type on soils and suggesting what crops may be more beneficial for soil.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».