Traditional technology adoption models in agriculture fail to adequately capture the complex interplay of socio-technical factors that drive farmer decision-making, resulting in limited predictive accuracy and insufficient understanding of diffusion dynamics. Existing approaches predominantly rely on static econometric frameworks or simplified diffusion models that overlook the dynamic social interactions, trust networks, and heterogeneous decision-making processes that characterize real-world agricultural technology adoption. This gap hinders effective policy design and technology deployment strategies. To address these limitations, this paper presents AdoptAgriSim, a Socio-Technical Agent-Based Simulation Model for Predicting Smart Agriculture Adoption Dynamics, which integrates economic, social, and technological dimensions into a unified framework. The model employs multi-agent reinforcement learning and socioeconomic network modelling to capture how individual farmers, peer networks, and market forces interact during the diffusion of technology. AdoptAgriSim incorporates a multi-objective decision mechanism that balances rational economic reasoning with social learning shaped by trust-based network structures. Using three real-world datasets from Iowa (USA), Europe, and India, the model achieves 94.2% prediction accuracy for five-year adoption intervals, outperforming existing diffusion and econometric models. It effectively reproduces emergent adoption behaviours such as technology clustering, peer-driven influence cascades, and region-specific diffusion trajectories. Significant contributions include 1 : a socio-technical model integrating multi-dimensional decision factors 2 ; a reinforcement-based optimiser that accounts for both economic and non-economic objectives 3 ; dynamic network evolution mechanisms reflecting real-world social interactions; and 4 extensive validation across diverse agricultural contexts. Results show that social factors contribute 34% more to adoption variance than previously estimated, underscoring the centrality of peer influence and trust networks in accelerating the diffusion of technology. The proposed framework offers valuable insights for policymakers and technology designers, highlighting that strengthening social connectivity and targeted network interventions can substantially accelerate sustainable agricultural transformation.

The study aimed to investigate the soil-plant relationship by looking at knowledge levels of social media users. The study examined the relationship between the users’ characteristics and their knowledge on soil-plant relationship. Online survey was designed and distributed to gather the data. The number of response was received from 383 respondents where 375 participants provided completed information and the remaining eight responses were eliminated due to quality standards. The results show most of the participants (68.8%) are moderately depend on social media to acquire knowledge on soil-plant relationship and indicated “Twitter” as the highly utilized platform followed by the “Youtube”. Meanwhile, 48.8% of the participants indicated that social media have a high impact on their knowledge-based information on soil-plant relationship and agricultural contents. Inferential analysis shows there was a significant positive relationship (p < 0.01) between independent variables; Twitter, Youtube, Instagram, and Snapchat and the degree of trust on these platforms, and the level’s knowledge on soil-plant relationship (dependent variable). Only 13% of the variance accounted for the impact of social media on participants’ agricultural knowledge-base can be predicted from the combination of participants’ reliance on a set of social media platforms (Twitter, Instagram, Youtube, and Snapchat). The study revealed the trust and confidence of the users using social media on agricultural information/soil plant relationship had a medium effect in social and educational research.

The current study examined the information preferences of the subscribers of various agricultural content accounts on social media as well as the perceived impact of social media on crop production skills in Saudi Arabia. Data were collected from the subscribers of the agricultural content accounts of Saudi Arabia on social media using an online survey with the help of a structured questionnaire and a convenient sampling methodology. Both descriptive statistics and Pearson correlation analysis were run to analyse the data. The majority (81%) of the respondents preferred agricultural information in the form of videos, followed by photos (75%). Moreover, a moderate to high impact was reported by the respondents on their various agricultural skills. Trust in social media had a significant negative correlation with respondents’ perceived impact on their crop production skills. The respondents’ preferences for a particular type of information had a significant positive correlation with the perceived impact of social media on their crop production skills. We conclude that subscribers’ trust in social media and their preference for agricultural information in the form of videos are the critical factors that may shape their behaviour positively.

The purpose of this study was to assess rural populations’ understanding of environmental protection. This study highlights the cognitive aspects of rural people regarding their surrounding environment. A paper-based, structured questionnaire was designed to collect data. A random sample of 317 participants was selected to represent the entire population. The questionnaire was distributed by posting it to their national addresses. A total of 270 valid responses were collected. The findings revealed that respondents had a moderate level of knowledge about protecting the rural environment from water pollution (67 %). Their knowledge of air pollution was low (94.4 %); however, their level of understanding about food pollution was high (79.6 %). The results indicate that 20 % of the variance in overall knowledge regarding the environment can be predicted from the combination of predictor variables (education, income, tendency to change, aspiration for a better environment, and tendency to protect the environment). Predictor variables, including the willingness to protect and improve the environment and the tendency to change, can positively affect overall environmental knowledge. In fact, participants’ attitudes toward these variables are low. These factors are crucial to the dissemination of environmental information.