Economic differentiation of rice and shrimp farming sustems and riskiness : a case of Bac Lieu, Mekong Delta, Vietnam
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".