MODELLING ADAPTATION TO CLIMATE CHANGE IN AGRICULTURAL SYSTEMS: AGENT-BASED APPROACH

Yıl 2025, Cilt: 18 Sayı: 2, 566 - 585, 30.04.2025

Gizem Eren

https://doi.org/10.25287/ohuiibf.1471855

Öz

In recent years, there has been increased interest in using agent-based modeling to simulate climate change's effects on agricultural output. Agent-based modeling allows for a more detailed and nuanced understanding of how individual agents, such as farmers, make decisions in response to changing environmental conditions. By simulating interactions between these agents and their environment, we can better anticipate how different adaptation strategies may impact overall agricultural productivity. This approach also enables the exploration of various scenarios and their potential outcomes, providing valuable insights for policymakers and stakeholders. Agent-based models offer the advantage of simulating the decision-making process of individual entities and their interactions, integrating social dynamics and non-financial factors into decision-making, and establishing dynamic connections between social and environmental processes. In this paper, we review the agent-based climate change adaptation models that have been developed around the questions of (a) who adapts, (b) who adapts to what, (c) how adaptation occurs, and (d) what constitutes good adaptation. From there, we aim to show how these models simplify the process of perceiving the world by approximating reality. While at the same time recognizing the constraints of the model itself and the uncertainties, we also discuss whether they can be overcome.

Anahtar Kelimeler

Climate Change , Agent-based Model , Decision-making

Kaynakça

• Alexander, P., Moran, D., Rounsevell, M. D. A., & Smith, P. (2013). Modelling the perennial energy crop market: The role of spatial diffusion. Journal of the Royal Society Interface, 10(88). Scopus. https://doi.org/10.1098/rsif.2013.0656
• Amadou, M. L., Villamor, G. B., & Kyei-Baffour, N. (2018). Simulating agricultural land-use adaptation decisions to climate change: An empirical agent-based modelling in northern Ghana. Agricultural Systems, 166, 196–209. Scopus. https://doi.org/10.1016/j.agsy.2017.10.015
• Ambrosius, F. H. W., Kramer, M. R., Spiegel, A., Bokkers, E. A. M., Bock, B. B., & Hofstede, G. J. (2022). Diffusion of organic farming among Dutch pig farmers: An agent-based model. Agricultural Systems, 197, 103336. https://doi.org/10.1016/j.agsy.2021.103336
• Apetrei, C. I., Strelkovskii, N., Khabarov, N., & Javalera Rincón, V. (2024). Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning. Ecological Modelling, 489, 110609. https://doi.org/10.1016/j.ecolmodel.2023.110609
• Arnold, R. T., Troost, C., & Berger, T. (2015). Quantifying the economic importance of irrigation water reuse in a Chilean watershed using an integrated agent-based model. Water Resources Research, 51(1), 648–668. https://doi.org/10.1002/2014WR015382
• Asrat, P., & Simane, B. (2018). Farmers’ perception of climate change and adaptation strategies in the Dabus watershed, North-West Ethiopia. Ecological Processes, 7(1), 7. https://doi.org/10.1186/s13717-018- 0118-8
• Azadi, Y., Yazdanpanah, M., & Mahmoudi, H. (2019). Understanding smallholder farmers’ adaptation behaviors through climate change beliefs, risk perception, trust, and psychological distance: Evidence from wheat growers in Iran. Journal of Environmental Management, 250. Scopus. https://doi.org/10.1016/j.jenvman.2019.109456
• Babaeian, F., Delavar, M., Morid, S., & Jamshidi, S. (2023). Designing climate change dynamic adaptive policy pathways for agricultural water management using a socio-hydrological modeling approach. Journal of Hydrology, 627. Scopus. https://doi.org/10.1016/j.jhydrol.2023.130398
• Baeza, A., Bojorquez-Tapia, L. A., Janssen, M. A., & Eakin, H. (2019). Operationalizing the feedback between institutional decision-making, socio-political infrastructure, and environmental risk in urban vulnerability analysis. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85064965233&doi=10.1016%2fj.jenvman.2019.03.138&partnerID=40&md5=e4185c51ebed927fb42e1 8949f38eac7
• Baeza, A., & Janssen, M. A. (2018). Modeling the decline of labor-sharing in the semi-desert region of Chile. Regional Environmental Change, 18(4), 1161–1172. https://doi.org/10.1007/s10113-017-1243-0
• Bazzana, D., Foltz, J., & Zhang, Y. (2022). Impact of climate smart agriculture on food security: An agent-based analysis. Food Policy, 111. Scopus. https://doi.org/10.1016/j.foodpol.2022.102304
• Berger, T., Troost, C., Wossen, T., Latynskiy, E., Tesfaye, K., & Gbegbelegbe, S. (2017). Can smallholder farmers adapt to climate variability, and how effective are policy interventions? Agent‐based simulation results for Ethiopia. Agricultural Economics, 48(6), 693–706. https://doi.org/10.1111/agec.12367
• Bharwani, S., Bithell, M., Downing, T. E., New, M., Washington, R., & Ziervogel, G. (2005). Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa. 360(1463), 2183–2194. Scopus. https://doi.org/10.1098/rstb.2005.1742
• Block, A., Keuler, K., & Schaller, E. (2004). Impacts of anthropogenic heat on regional climate patterns. Geophysical Research Letters, 31(12). https://doi.org/10.1029/2004GL019852
• Brown, C., Bakam, I., Smith, P., & Matthews, R. (2016). An agent‐based modelling approach to evaluate factors influencing bioenergy crop adoption in north‐east Scotland. GCB Bioenergy, 8(1), 226–244. https://doi.org/10.1111/gcbb.12261
• Carozzi, M., Martin, R., Klumpp, K., & Massad, R. S. (2022). Effects of climate change in European croplands and grasslands: Productivity, greenhouse gas balance and soil carbon storage. Biogeosciences, 19(12), 3021–3050. https://doi.org/10.5194/bg-19-3021-2022
• Castro, J., Drews, S., Exadaktylos, F., Foramitti, J., Klein, F., Konc, T., Savin, I., & Van Den Bergh, J. (2020). A review of agent‐based modeling of climate‐energy policy. WIREs Climate Change, 11(4), e647. https://doi.org/10.1002/wcc.647