An Agent-Based Model Integrating the Theory of Planned Behavior for Simulating the Effect of Public Policies on Agricultural Diversity

• Pedro H. I. Soares UFS
• Marcos A.S. da Silva Embrapa
• Márcia H.G. Dompieri Embrapa
• Fábio R. de Moura UFS
• Neíza C. S. Batista Embrapa
• Sonise dos S. Medeiros Embrapa

DOI: https://doi.org/10.5753/eniac.2025.14420

Abstract

The study proposes an agent-based model that incorporates the Theory of Planned Behavior (TPB) to simulate the impact of public policies on agricultural diversification in the municipality of Santana de So Francisco (SE, Brazil). Implemented in NetLogo, the model simulated scenarios with climate variability, market prices, and financial incentives. The results indicated that economic incentives alone did not increase diversification, while extreme climate shocks (high rainfall and humidity variability) had a stronger influence on farmer decisions. The interviews revealed barriers such as limited financial and human resources, which reduced the effectiveness of the policy. The study concludes that effective strategies must combine incentives, technical support, and climate adaptation, along with involving farmers in policy design.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2):179–211.

Bartkowski, B., Schüßler, C., and Müller, B. (2022). Typologies of European farmers: approaches, methods and research gaps. Reg Environ Change, 22(43):–.

Bastidas-Orrego, L. M. et al. (2023). A systematic review of the evaluation of agricultural policies: Using prisma. Heliyon, 9(10):e20292.

Dressler, G. et al. (2019). Implications of behavioral change for the resilience of pastoral systems—lessons from an agent-based model. Ecological Complexity, 40(Part B):100710.

Emami, S., Dehghanisanij, H., and Hajimirzajan, A. (2024). Agent-based simulation model to evaluate government policies for farmers’ adoption and synergy in improving irrigation systems: A case study of Lake Urmia basin. Agric. Water Manag., 294:108730.

Fuentes, M. A., Tessone, C. J., and Furtado, B. A. (2022). Public policy modeling and applications 2021. Complexity, (9764151):1–3.

Haensel, M., Schmitt, T. M., and Bogenreuther, J. (2023). World of cows - exploring land-use policies for a dairy-farm world.

Malawska, A. and Topping, C. J. (2016). Evaluating the role of behavioral factors and practical constraints in the performance of an agent-based model of farmer decision making. Agricultural Systems, 143:136–146.

Mellaku, M. T. and Sebsibe, A. S. (2022). Potential of mathematical model-based decision making to promote sustainable performance of agriculture in developing countries: A review article. Heliyon, 8(2):e08968.

Muelder, H. and Filatova, T. (2018). One theory - many formalizations: Testing different code implementations of the theory of planned behaviour in energy agent-based models. JASSS, 21(4).

Orach, K. and Schlüter, M. (2020). Polisea: model of policy – social ecological system adaptation.

Paschoalino, P. A. T. and Parré, J. L. (2023). Diversificação e produção agrícola no Brasil uma análise por modelos espaciais. Revista de Política Agrícola, 32(1):121–140.

Paulus, A. et al. (2022). Landscape context and farm characteristics are key to farmers’ adoption of agri-environmental schemes. Land Use Policy, 121:106320.

Piedra-Bonilla, E., Braga, C., and Braga, M. (2020). Diversificação agropecuária no Brasil: conceitos e aplicações em nível municipal. Rev. de Agron. e Agro., 18(2):1–28.

Polhill, J. G. and Rouchier, J. (2023). Policy modelling requires a multi-scale, multicriteria and diverse-framing approach. RofASSS.

Rausser, G. and Just, R. (2022). Principles of Policy Modeling in Food and Agriculture, chapter 1. Springer, Cham.

Sanga, U., Berrío-Martínez, J., and Schlüter, M. (2023). Modelling agricultural innovations as a social-ecological phenomenon. SESMO, 5:18562.

Schlüter, M. et al. (2017). A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecological Economics, 131:21–35.

Senger, I., Borges, J. A. R., and Machado, J. A. D. (2017). Using the theory of planned behavior to understand the intention of small farmers in diversifying their agricultural production. Journal of Rural Studies, 49:32–40.

Shaaban, M. (2023). Viability of the social–ecological agroecosystem (visa). SoftwareX, 22:101360.

Silva, M. A. S. d. et al. (2022). Tracking the connection between Brazilian agricultural diversity and native vegetation change by a Machine Learning approach. IEEE Latin America Transactions, 20(11):2371–2380.

Will, M. et al. (2021). How to make socio-environmental modelling more useful to support policy and management? People and Nature, 3(3):560–572.

Wittstock, F. et al. (2022). Understanding farmers’ decision-making on agrienvironmental schemes: A case study from Saxony, Germany. Land Use Policy, 122:106371.

Zhang, R. et al. (2018). Projecting cropping patterns around poyang lake and prioritizing areas for policy intervention to promote rice. Land Use Policy, 74:248–260.

Ziv, G. et al. (2020). Bestmap: behavioural, ecological and socio-economic tools for modelling agricultural policy. Research Ideas and Outcomes, 6(e52052).