| چکیده انگلیسی مقاله |
Introduction: Over recent decades, digital agriculture has emerged as a key tool for enhancing productivity and sustainability in rural areas. This concept involves the use of technologies such as the Internet of Things (IoT), remote sensing, and machine learning to optimize resource consumption, increase productivity, and manage environmental sustainability. However, rural farmers face challenges in adopting these technologies due to infrastructure and information limitations. Digital agricultural education can play a significant role in empowering farmers and increasing their awareness of modern technologies. In this context, designing an innovative educational model that addresses the social, economic, and environmental dimensions of agriculture is essential. To be effective, such a model must incorporate accessible training methods, local language support, and real-time advisory services tailored to the specific needs of rural communities. It should also consider gender inclusivity, digital literacy, and community engagement to ensure widespread adoption. This research aimed at proposing an effective model for digital agricultural education to contribute to the sustainable productivity of agriculture in rural areas and improve the living standard of farmers. Furthermore, the study highlighted the importance of public-private partnerships and government support in scaling digital initiatives and bridging the digital gap in underserved farming regions.
Materials and Methods: This study utilized a mixed research method with an exploratory sequential approach to design an innovative digital agricultural education model focusing on social, economic, and environmental impacts. In the qualitative phase, the required data were collected through semi-structured interviews with 16 managers from the Ministry of Agriculture-Jahad (MAJ) as well as agricultural education specialists and university professors. Data analysis was carried out in five stages: generating initial codes, searching for selective codes, forming sub-themes, and defining main themes. In total, 50 selective codes, 26 sub-themes, and 9 main themes/categories were identified. The validity of the findings was confirmed by interviewees, and their reliability was tested through retesting and comparing coding between two researchers. In the quantitative phase, the required data were collected through a questionnaire designed based on the results of the qualitative phase from a sample population consisting of agricultural education specialists, staff, and university professors. A cluster sampling method was used, and the sample size was determined to be 1960 people. The reliability of the questionnaires was confirmed using Cronbach's alpha. The data were analyzed using structural equation modeling to examine the relationships among the identified variables. The final model provided insights into the key drivers of effective digital agricultural education and could serve as a foundation for policy-making and curriculum development. The findings contributed to a better understanding of the factors affecting digital agricultural education and provided an innovative educational model that could be adapted to various rural contexts.
Results and Discussion: The study results showed that the innovative digital agricultural education model had a significant impact on improving productivity and sustainability in rural agriculture. In the qualitative phase, thematic analysis led to the identification of 9 main themes, including social sustainability, economic sustainability, environmental sustainability, digital innovations, education and empowerment, policy-making, technological infrastructure, synergies and partnerships, and research and development. In the quantitative phase, path coefficients showed that all independent variables had significantly positive impacts on the digital agricultural education model (P<0.05). The highest impacts were related to the education and empowerment (β=0.813) and the policy-making and strategy (β=0.817). The R² value (0.585) and the overall model fit index (GOF=0.412) indicated a strong match between the model and the empirical data. In addition, the predictive relevance (Q²) confirmed the model’s predictive accuracy. The findings underscore the importance of educational, technological, and policy factors in the development of digital agriculture and emphasize the role of synergy between government, universities, and the private sector. These results can serve as a foundation for policy strategies and the design of digital educational programs in the field of sustainable agriculture.
Conclusion and Suggestions: The study findings indicated that the innovative digital agricultural education model, focusing on social, economic and environmental impacts, could be an effective solution for improving productivity and sustainability in the rural agriculture. The identification of the above-mentioned nine key themes/dimensions showed that combining these factors could facilitate the successful adoption and implementation of digital agricultural education. The findings also emphasized that technological infrastructure, supportive policies, and the development of farmers’ digital skills would be essential components of this model. Furthermore, the role of digital innovations such as IoT and Artificial Intelligence (AI) in optimizing agricultural processes and enhancing productivity was confirmed. The consistency of this study’s findings with previous research highlights the importance of policy strategies, technology development, and digital education in fostering sustainable agriculture. Therefore, it is recommended that policymakers and planners, considering these dimensions, develop comprehensive strategies for the expansion of digital agricultural education, which will contribute to increased agricultural productivity, the preservation of natural resources, and sustainable development |