Modeling economic, biophysical, and environmental dynamics of potato production system
Notice bibliographique
Résumé
The adoption and use of diversified cropping practices has become widely accepted by producers. However, the profitability of an enterprise is dependent on the biophysical and economic factors. The biophysical factors determine the enterprise relationship among the various crops, their interactions with the ecosystem, and how each of them fits into the overall management plan. The economic factors determine the relative advantage of each crop and service in the farm plan. Although there have been experimental studies in Canada examining the impacts of rotations on the agronomics of field crops, there is limited research in modeling economic and environmental dynamics of the potato production system. Potato production in Manitoba has rapidly increased in recent years and became the second largest potato producer in Canada producing about 22.2% of the total Canada's potato production as of 2004. Alberta has had similar expansion with the highest yield per acre in Canada. The expansion of potato industry in these two provinces and other parts of Canada has created some concerns about agro-environmental indicators such as water contamination, soil erosion and long-term profitability of potato production practices. The objective of this study was to address some of these concerns and develop a dynamic model that integrates environmental and economic components of potato production systems and estimates sustainability of some of the agricultural practices. A Stella Modeling framework was developed to provide crop production and environmental input to an economic model of potato rotations. The economic performance of different potato rotations ranging from two to four years in duration, and containing potatoes in combination with oilseed, cereal, forage, and legume crops was evaluated based on standard budgeting techniques. Net income was estimated as the income remaining above cash costs (i.e., seed, fertilizer, chemical, fuel and oil, repairs, crop insurance premium, miscellaneous, land taxes, and interest cost on variable inputs), ownership costs (depreciation, interest on investment, and insurance and housing) for machinery and grain storage, and labour. All annual inputs used in each phase of rotation for each management treatment, the type and frequency of field operation, year and replicate including pre-plant activities, tillage, fertilization, planting, insects and pests control, harvesting, storage, and transportation were included in the analysis. Modelling the economics of irrigated potato rotations required input from an agro-environmental model. This agro-environmental model was developed to simulate nutrient dynamics, soil moisture dynamics, soil characteristics and erosion, soil organic matter content, residue decomposition, and crop growth. The economic model takes into account economic factors and costs that are influenced by yield function, nutrients and crop water thus establishing the link between the agro-environmental module and the economic module. Farm operation costs in our model are divided into two categories: dependent costs and base costs. Dependent costs are costs that are controlled by the agro-environmental module, including yield dependent costs, irrigation dependent costs and fertilizer dependent costs. Base costs were developed through experimental evaluation with E-Views and remain constant throughout the rotation. On the agro-environmental side, the model outputs the changes in soil organic matter, soil loss due to erosion, and carbon dioxide emission due to decomposition and mineralization. Results of the model show that changes in these variables are dependent upon the length of, and the crops involved in, the rotation. The economic model results showed highest average net revenue was found in shorter two-year rotations due to the greater frequency of potato.
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 | 0,000 |
| 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 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 ».