Marwa KHAIRALLAH1*
1 Faculty of Econimic Sciences and Management, University of Sfax, Tunisia
email: marwa.khairallah@isggb.u-gabes.tn
Keywords: climate change adaptation, climate-smart agriculture, climate-smart aquaculture, adaptive capacity, resilience, sustainability
Abstract
Climate change poses unprecedented challenges to agricultural and aquaculture systems worldwide, threatening food security, water availability, biodiversity, and the sustainability of rural livelihoods. Increasing temperatures, extreme weather events, droughts, floods, and ecosystem degradation are placing significant pressure on producers to adopt more resilient and adaptive production practices. In response, Artificial Intelligence (AI), the Internet of Things (IoT), and smart sensing technologies have emerged as promising tools for supporting Climate-Smart Agriculture and Aquaculture Technologies (CSATs). Recent studies indicate that AI-driven systems optimize water, fertilizer, feed, and energy use, often reducing resource consumption while maintaining or improving productivity. In aquaculture, AI-based monitoring and feeding systems have been shown to reduce mortality rates and enhance production efficiency, thereby contributing to climate-resilient food systems (Kanwal et al., 2024; Dhamdhere et al., 2025; Baena-Navarro et al., 2025). Likewise, AI-powered climate advisory services, predictive analytics, and pest and disease detection systems improve producers’ ability to anticipate and respond to climate-related risks (Verma et al., 2024; Chao, 2024; Gryshova et al., 2024). Despite growing evidence regarding the technical benefits of AI-enabled climate-smart technologies, limited research has examined the behavioral and organizational mechanisms through which AI adoption translates into greater climate resilience among agricultural and aquaculture producers. Existing studies have largely focused on technological performance while overlooking the role of adaptive capacity as a critical intermediary between technology adoption and resilience outcomes. Furthermore, few studies have simultaneously investigated agriculture and aquaculture within a unified analytical framework. To address this gap, the present study investigates the following research question: How does the adoption of AI-enabled climate-smart technologies influence producers’ adaptive capacity and resilience to climate change in agriculture and aquaculture? The study pursues three objectives: (1) to identify the determinants of AI-enabled climate-smart technology adoption; (2) to examine the influence of AI adoption on adaptive capacity; and (3) to assess the impact of adaptive capacity on resilience and sustainability performance under climate-related stress. The proposed conceptual framework integrates three complementary theoretical perspectives. First, the Technology Acceptance Model (TAM) explains how perceived usefulness and perceived ease of use shape producers’ willingness to adopt AI technologies (Rodzoan, 2024; Yu et al., 2025; Hrynevych et al., 2022). Second, Adaptive Capacity Theory emphasizes the ability of individuals and organizations to anticipate, respond to, and recover from environmental disturbances. Third, Resilience Theory explains how socio-ecological systems maintain functionality and sustainability despite climate-related disruptions. The framework further acknowledges the importance of facilitating conditions, access to information, extension services, and financial resources in supporting technology adoption and climate adaptation (Atube et al., 2021; Yu et al., 2025). Methodologically, the study adopts a quantitative approach based on a survey administered to approximately 400 agricultural and aquaculture producers operating in climate-vulnerable regions. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both measurement and structural models. Reliability and validity are evaluated through Cronbach’s Alpha, Composite Reliability, Average Variance Extracted (AVE), Fornell-Larcker criterion, and HTMT ratios. Bootstrapping procedures are employed to test the significance of direct, indirect, and mediating effects. The findings suggest that perceived usefulness, ease of use, facilitating conditions, and access to information significantly influence AI adoption. AI-enabled climate-smart technologies enhance adaptive capacity by improving climate forecasting, resource optimization, early risk detection, and data-driven decision-making. Adaptive capacity mediates the relationship between AI adoption and resilience, while resilience positively affects sustainability outcomes, including resource efficiency, environmental performance, productivity, and food security. The results also demonstrate that AI-enhanced IoT ecosystems (“Agri-AIoT” and “Aqua-AIoT”) strengthen absorptive and adaptive capacities, enabling producers to better cope with climate variability and uncertainty (Mansoor et al., 2025; Daraojimba et al., 2024; Liu et al., 2025). This study contributes to the literature in three ways. First, it extends climate adaptation research by integrating technology acceptance, adaptive capacity, and resilience theories within a single framework. Second, it provides one of the few empirical investigations simultaneously examining agriculture and aquaculture in the context of AI-driven climate adaptation. Third, it offers practical guidance for policymakers, agricultural extension agencies, and technology developers seeking to accelerate the adoption of climate-smart technologies and strengthen climate resilience in food production systems. Ultimately, the findings support ongoing efforts to achieve sustainable development goals related to climate action, sustainable agriculture, resilient aquaculture, and global food security.
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Received:22 March 2026, Revised:30 April 2026, Accepted: 22 August 2026, Published: 28 August 2026
Citation
KHAIRALLAH, M. (2026). BEYOND CLIMATE AWARENESS: UNDERSTANDING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING ADAPTIVE CAPACITY AND RESILIENCE IN CLIMATE-SMART AGRICULTURE AND AQUACULTURE. Pescuitul și Acvacultura (Fishing and Aqvaculture), 3(5). https://doi.org/10.5281/zenodo.22141509
