# Parallel Agent-Based Framework for Analyzing Urban Agricultural Supply Chains
## 2 Related Work  
This section provides a comprehensive review of recent studies and methodologies related to simulating farmers' decision-making processes and agricultural systems. The works discussed here explore various approaches, including agent-based modeling, to understand and predict the behavior of farmers under different scenarios. These studies highlight the complexities of agricultural systems, incorporating factors such as environmental conditions, market dynamics, technological adoption, and policy impacts. The work in [9] presents a simulation of the decision-making dynamics of small farmers under various scenarios. The input data and models are primarily based on studies and the realities of Czech agriculture. However, the definition of small farmers used in this work shares similarities with ours, including seasonal dependence, a reliance on family labor, and a focus on fruit and vegetable production over livestock. In [9], farmers' decision-making is influenced by two main factors: the business model and the dynamics of the system in which they operate. On one hand, the business model serves as an analytical strategic tool that defines how a company creates and captures value [10]. According to the authors, this theoretical framework enables the explicit modeling of farmers' adaptation processes to changes and external factors. The methodology is based on [11] and requires well-specified models, both causally and mathematically. However, this study does not account for the full production flow of small farmers. Specifically, the market with intermediaries and the consumer-driven demand remain implicit as exogenous variables. Additionally, the methodology is predominantly quantitative, meaning certain aspects of agents' decision-making are not considered. For instance, environmental variables such as climate factors (e.g., frosts, droughs, and heatwaves) are not integrated into the decision-making model. The study in [12] presents a simulation-based decision support system for small farmers, focusing on a case study in Sri Lanka. In this initiative, the government provides farmers with simulation tools. However, a key implementation challenge arises: the target farmers are largely illiterate. To address this, the proposed system enables users to visually manipulate simulation scenarios, minimizing the need for written interfaces. The methodology is based on mathematical programming [13], a widely used approach in agricultural modeling and analysis, although real-world applications remain limited [14]. The study introduces two mathematical models. The first model employs a linear formulation, but its main limitation is the inability to represent nonlinear effects, such as the impact of planting a specific crop on adjacent land or across different time periods. To overcome these constraints, the second formulation is designed as a constrained quadratic problem. While this model captures certain nonlinear interactions, it requires relaxing some constraints from the linear formulation, such as individual budget constraints for each farmer. The study does not provide conclusions regarding the accuracy of the simulations. The study in [15] employed an agent-based model (ABM) to simulate future crop patterns in Luxembourg, incorporating environmental awareness into farmers' decision-making. The results, used for a life cycle assessment (LCA), reveal that focusing solely on reducing greenhouse gas emissions leads to suboptimal trade-offs. LCA is a methodology used to evaluate the environmental impact of a product, material, or crop throughout its life cycle [16]. The study also compares the bottom-up approach of ABM with a top-down method, highlighting the respective advantages and limitations of each. This work is further expanded in [17]. While the authors acknowledge the importance of using geographically explicit farm data, the simulations were conducted with synthetic polygons representing farms.