Lessons learned from a detailed exploration of APEX as a tool to represent corn residue management and cover crops
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Notice bibliographique
Résumé
Agricultural management practices to improve the regulation of water, sediments, or nutrients, make farming decisions and operations more complex. This extra complexity often stems from the use of multiple species and farm heterogeneity so that species can complement each other, and different fields (and the space between fields) can provide alternative benefits, like biomass or nutrient regulation. Mechanistic crop and farm models provide tools to explore the effect of these practices. The study goal was to assess the capability of a mechanistic crop model (the Agricultural Policy/Environmental eXtender Model, APEX) to represent the impacts of cover crops and corn residue on plant growth, water, erosion, and nutrient flow. Using Southern Ontario conditions, a simplistic corn–cover crop rotation strategy was implemented using APEX and hundreds of variables dynamically updated by the model were analyzed. The model's documentation and source code were analyzed to understand the connections among the variables. The model reproduced corn and cover crop growth patterns observed in Southern Ontario and the positive effects of cover crops and residue on water, sediments, and nutrient control. The model suggested that these practices generate important differences in nutrient dynamics and patterns of vertical accumulation of soil nutrients. Issues with the model are reported and ways to avoid them discussed. There were inconsistencies and unrealistic responses in the outputs when simulating two crops growing together or multiple fields, including small mass balance discrepancies, which —in complex numerical models like APEX— can generate hard-to-track differences and may be amplified when multiple fields are simulated over several years. Users should be aware of these limitations when assessing the role of diversified farming practices. The results highlight the importance of carefully reviewing the internal consistency of mechanistic models beyond validating a few key outputs, especially when the intended use of a model is to extrapolate the impacts to novel conditions or to infer processes not directly validated. These findings could open the conversation for more robust modelling and validating approaches when using crop models. • Mechanistic models of diversified crop management are essential to inform farming decisions and planning. • The logic, implementation, and results of multi-crop simulations in APEX was explored. • APEX shows that cover crops and corn residue affect water and soil nutrient dynamic differently. • APEX has limitations and software bugs related to multi-crop and multi-field simulations. • A more active involvement by researchers in model internal validation can improve the model.
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.
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 | 0,000 |
| 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 découle