Farmers' intention and decision to adapt to climate change: A case study in the Yom and Nan basins, Phichit province of Thailand Noppol Arunrat a, b , Can Wang a, c, * , Nathsuda Pumijumnong b , Sukanya Sereenonchai b, d , Wenjia Cai c a State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing,100084, China b Faculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom, 73170, Thailand c Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China d Institute of Communication Studies (ICS), Communication University of China, Dingfuzhuang East Street, Chaoyang District, Beijing, 100024, China article info Article history: Received 9 August 2016 Received in revised form 20 October 2016 Accepted 12 December 2016 Available online 21 December 2016 Keywords: Climate change Adaptation Intention Communication Logistic regression model Theory of planned behavior abstract Adaptation at farm level is an effective measure to cope with global climate change. The study aims to clarify farmers' intentions and decisions regarding global climate change adaptation. Logistic regression models were used to examine the influences of socioeconomic factors and climate adaptation commu- nication processes on farmers' decision to apply adaptation strategies against drought and flood. Spe- cifically, for a thorough understanding of non-adapting farmers, the theory of planned behavior was incorporated, to assess these farmers' intention to adaptation. Results showed that farmers' perceptions were consistent with the weather data over a short period, reporting a rise in temperature and a greater decrease in precipitation. Agricultural experience, farm income, training, social capital, and effective climate adaptation communication were statistically significant in increasing the probability of farmers' adaptation. For farmers who do not perceive climate change but adapted nonetheless, social capital played a major factor, driving their belief in, and behavior to adaptation, of which the most important aspects were neighbors and peer groups. Farmers' intention to adapt was mostly affected by perceived behavioral control factors, followed by attitude and subjective norms. Therefore, successful policies to enhance farmers' perceptions and adaptive capacity can encourage both actual and intended adaptation farmers. Adaptation strategies require the participation of multiple players from all related sectors engaging with local communities and farmers. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction Climate change damages farming productivity and the success of agricultural initiatives (Mikhail et al., 2010). In particular, pre- cipitation, and temperature changes present the main risk, increasing extreme climatic events, such as floods and droughts worldwide (Petley, 2012). Southeast Asian countries, such as Thailand, are already experiencing climate change and the increased frequency of climate-related hazards, like droughts and floods, which have resulted in substantial impacts in many areas (Ono et al., 2010). In 2010, Thailand faced its worst drought in the past 20 years, leading to the lowest water level of the Mekong River in 50 years (Marks, 2011). In 2011, the greatest flood recorded in Thailand struck the Chao Phraya basin and caused tremendous damage in northern and central Thailand (Komori et al., 2012). Empirical evidence proves that climate change adaptation enables a reduction in its impacts, the protection of poorer farmers' liveli- hoods, and the enhancement of possible potential advantages (Gandure et al., 2013). Consequently, appropriate adaptation stra- tegies and support policies are crucial to anticipate the nature of expected changes, and to understand how climate change and its associated hazards are perceived, experienced, and responded by local farmers. Farmers' adaptation to climate change, behavior and decision making can be affected by socioeconomic factors, which have been investigated in various countries (Beermann, 2011; Mariano et al., 2012; Figueiredo and Perkins, 2013; Tessema et al., 2013; Wamsler et al., 2013; Duan and Hu, 2014; Obayelu et al., 2014; * Corresponding author. State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China. E-mail address: canwang@tsinghua.edu.cn (C. Wang). Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro http://dx.doi.org/10.1016/j.jclepro.2016.12.058 0959-6526/© 2016 Elsevier Ltd. All rights reserved. Journal of Cleaner Production 143 (2017) 672e685