An Agent-Based Model Integrating the Theory of Planned Behavior for Simulating the Effect of Public Policies on Agricultural Diversity
Resumo
O estudo propõe um modelo baseado em agentes integrando a Teoria do Comportamento Planejado (TCP) para simular o impacto de políticas públicas na diversificação agrícola no município de Santana de São Francisco (SE). Implementado no NetLogo, o modelo simulou cenários com variações climáticas, preços de mercado e incentivos financeiros. Os resultados indicaram que incentivos econômicos isolados não aumentaram significativamente a diversificação, enquanto choques climáticos extremos (alta variabilidade de precipitação e umidade) tiveram maior influência nas decisões dos produtores. Entrevistas revelaram barreiras como falta de recursos financeiros e humanos, reduzindo a eficácia das políticas. Conclui-se que estratégias eficazes devem combinar incentivos, suporte técnico e adaptação climática, além de envolver os produtores no desenho das políticas.Referências
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).
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).
Publicado
29/09/2025
Como Citar
SOARES, Pedro H. I.; SILVA, Marcos A.S. da; DOMPIERI, Márcia H.G.; MOURA, Fábio R. de; BATISTA, Neíza C. S.; MEDEIROS, Sonise dos S..
An Agent-Based Model Integrating the Theory of Planned Behavior for Simulating the Effect of Public Policies on Agricultural Diversity. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 2008-2019.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14420.
