Artificial intelligence (AI) has brought a revolution in nearly every sector, whether on a small or large scale, and agriculture is no exception. So, what motivates farmers to adopt AI technology, especially in the face of climate change?
According to a survey of 471 Thai farmers, they may be more willing to use AI when they believe the tools are easy to use, useful and supported by people around them, rather than simply because they perceive climate change as a severe threat.
The study, The Perception of Climate Change Threats on Intention to Use AI for Sustainable Agriculture Among Thai Farmers, published in Sustainability, used Partial Least Squares Structural Equation Modeling to examine what drives farmers’ intention to adopt AI for climate adaptation and sustainable agriculture.
Thailand’s agricultural sector is facing mounting pressure from floods, droughts, heat, erratic rainfall and rising production risks. According to the paper, climate-related damage poses a serious threat to farm output, rural incomes and national food security and AI is a potential tool for precision agriculture, better crop planning, climate forecasting, fertilizer management, disease detection and cost reduction.
The study tested whether farmers’ awareness of climate threats would push them toward AI adoption. The authors used an integrated framework combining Protection Motivation Theory, Theory of Planned Behavior and the Technology Acceptance Model. The model examined perceived severity, perceived vulnerability, AI self-efficacy, response efficacy, perceived ease of use, perceived usefulness, attitude, subjective norms and intention to use AI.
The results challenge the assumption that climate fear automatically leads farmers to adopt new tools. Perceived severity of climate change did not significantly affect intention to use AI. Perceived vulnerability also showed no significant effect. Response efficacy, or the belief that AI could effectively reduce climate risks, was also not a significant direct driver of adoption.
Farmers may recognize climate change as a serious problem, but awareness alone may not be enough to change technology behavior. The research suggests that climate threats may work indirectly by pushing farmers to seek information, discuss solutions and evaluate technologies, rather than directly leading them to use AI.
Most respondents were older, had lower levels of formal education and had long agricultural experience. Many farmers also had low incomes. The study suggests that farmers may be familiar with climate variability and may continue to rely on experience, weather patterns, soil conditions and traditional practices, making climate risk less powerful as a direct trigger for AI adoption.
Perceived ease of use had a strong and significant effect on perceived usefulness, which means farmers were more likely to see AI as valuable when they believed it was simple to operate. Perceived usefulness, in turn, strongly influenced attitude toward AI. Attitude was one of the most important direct predictors of intention to use AI. Farmers who viewed AI positively were more likely to admit that they intended to use it for agricultural productivity and climate-related decision-making.
The study found that subjective social norms significantly influenced intention to use AI. Practically, farmers were more likely to adopt AI when family members, friends, community figures, local leaders, media or influential people encouraged its use or demonstrated its value.
AI self-efficacy was another significant driver. Farmers who believed they could learn to use AI independently were more likely to intend to adopt it. This finding is especially important for rural communities where formal digital training may be limited and where confidence can determine whether a farmer tries a new tool or avoids it.
AI adoption among Thai farmers is not only a technology issue, but also a social and psychological issue. They need to see AI as useful, easy, trusted and relevant to their own work. They also need social proof from people and institutions they trust.
Thailand has high levels of mobile phone and internet use, and many farmers own smartphones, but agricultural technology use remains limited. The paper notes that only a small share of Thai farmers use agricultural applications, and drone use remains low, which suggests that access to devices alone does not guarantee digital transformation in farming.
Governments and agricultural agencies cannot rely on climate warnings alone to accelerate AI adoption. Awareness campaigns about climate risks may be necessary, but they are unlikely to be sufficient unless paired with practical training, local demonstrations and easy-to-use AI services.
Policy should focus on showing concrete benefits. Farmers need visible examples of how AI can reduce fertilizer costs, improve planting decisions, identify crop disease, manage water use and increase yields under changing climate conditions. Demonstration farms, model villages and locally relevant case studies could help turn AI from an abstract tool into a practical farming service.
The findings also point to the need for AI literacy and AI governance. Farmers should be trained not only to use AI, but also to understand its limits, risks and responsible use. Without careful planning, AI adoption could deepen inequality by helping better-resourced farmers first while leaving poorer and older farmers behind.
Government support may help reduce cost barriers because advanced AI tools and high-quality analytical systems may not always be free. The study warns that poor planning could create an AI divide, where farmers with better education, income and digital skills benefit first, while vulnerable farmers fall further behind.
The researchers suggest that Thailand can build on farmers’ smartphone access and popular communication platforms to deliver AI training and guidance. Local leaders, community networks and social media can play a role in building trust and creating social momentum around agricultural AI. Put simply, AI adoption in agriculture will depend less on telling farmers that climate change is dangerous and more on proving that AI is simple, useful and supported by trusted networks.
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