Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India

Kalthof, M.W.M.L., de Bruijn, J. ORCID: https://orcid.org/0000-0003-3961-6382, de Moel, H., Kreibich, H., & Aerts, J.C.J.H. (2025). Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India. Natural Hazards and Earth System Sciences 25 (3) 1013-1035. 10.5194/nhess-25-1013-2025.

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Abstract

Consecutive droughts, becoming more likely, produce impacts beyond the sum of individual events by altering catchment hydrology and influencing farmers' adaptive responses. We use the Geographical, Environmental, and Behavioural (GEB) model, a coupled agent-based hydrological model, and expand it with the subjective expected utility theory (SEUT) to simulate farmer behavior and subsequent hydrological interactions. We apply GEB to analyze the adaptive responses of similar to 1.4 million heterogeneous farmers in India's Bhima basin over consecutive droughts and compare scenarios with and without adaptation. In adaptive scenarios, farmers can either do nothing, switch crops, or dig wells, based on each action's expected utility. Our analysis examines how these adaptations affect profits, yields, and groundwater levels, considering, e.g., farm size, risk aversion, and drought perception. Results indicate that farmers' adaptive responses can decrease drought vulnerability and impact after one drought (6 times the yield loss reduction) but increase them over consecutive periods due to switching to water-intensive crops and homogeneous cultivation (+15 % decline in income). Moreover, adaptive patterns, vulnerability, and impacts vary spatiotemporally and between individuals. Lastly, ecological and social shocks can coincide to plummet farmer incomes. We recommend alternative or additional adaptations to wells to mitigate drought impact and emphasize the importance of coupled socio-hydrological agent-based models (ABMs) for risk analysis or policy testing.

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