Economic differentiation of rice and shrimp farming sustems and riskiness : a case of Bac Lieu, Mekong Delta, Vietnam
Notice bibliographique
Résumé
In production terms, Bac Lieu Province in the Mekong Delta of Vietnam is characterized by rice and saline-water shrimp farming. This paper presents two simulation models of economic differentiation of those farming systems. The first model simulates observed farmers' behavior in six different farming subzones of the province. After simulating 5 years for each farming system corresponding to each subzone, the results showed that economic differentiation has occurred in every subzone at the study site in terms of both household average accumulation of income and number of households in the rich and poor class. The household average accumulation of income of the rich household class in those subzones where physical conditions allowed shrimp farming has a high value, while that of the medium and poor households remains at a low value, and is even negative for two subzones. The household average accumulation of income of the rich household class in those subzones where physical conditions (freshwater zone) allowed only rice farming reaches a high value after 5 years of simulation, but this value is still less than that in shrimp-culture subzones. The poor households in these subzones of rice-based farming also face a negative income after some years. The second model aims at simulating changes in cropping system under various conditions. The individual decision-making process is based on a theoretical model, the Consumat. Scenarios based on alternative values of prices, yields, risk, and size of networks are compared. It is shown that prices and shrimp yields make the difference in terms of both wealth and economic differentiation. The questions raised over time are (1) Is there a differentiation in income distribution at the household level because of the biophysical conditions and market factor? (2) Is there a differentiation in household income within the subzone because of biophysical conditions and hetero-geneity in farm management knowledge? (3) How will the differentiation evolve if the farmers change their behavior? In this research, the first two questions are discussed by running a simulation model based on the observations of farmers' decisions for six different zones and the third question is discussed by running a simulation model using a theoretical model of the decision-making process, the Consumat approach (Jager 2000). A multi-agent systems (MAS) model supported by the CORMAS (common-pool resources and multi-agent systems) program helps us to answer those questions. It allows us to visualize the scenarios after linking several biophysical and socioeconomic factors. Consequently, given the complexity of this subject, the spatial characteristics, and, above all, the noneconomic and interactive behavior of farmers, we use the MAS model to simulate the scenarios. This paper presents first the background of the study and a brief review of applications of MAS for water management and economic differentiation. Then, a first model is conceptualized and simulated to explore the consequences of the actual behavior of stakeholders. A second model, more abstract, explores the consequences of the changes in behavior and the relative effects of various driving forces.
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 ».