Introduction: Lean technology in agriculture is recognized as a transformative paradigm in the present era that has the potential to revolutionize traditional agricultural methods by integrating the most advanced digital technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), robotics, and big data. Lean technology transfer in agriculture, focusing on eliminating resource waste and creating added value in all links of the supply chain, has been proposed as a key solution to confront global challenges such as water scarcity, climate change, and food insecurity. By applying principles such as precise agriculture, smart waste management, and optimization of planting to harvesting processes, this technology not only reduces production costs but also improves product quality and market transparency. The success of this model requires tripartite cooperation between the government, the private sector, and farmers to form a sustainable cycle of production, distribution, and consumption that is in line with the environmental and economic goals of the 21^st century. In Iran, despite significant achievements in some areas of agricultural technology, several obstacles, including the dispersion and small scale of production units, infrastructural limitations, lack of an integrated technology transfer system, and shortage of specialized human resources, have challenged the process of utilizing these technologies. This study aimed at analyzing the factors affecting the transfer of lean technology in agriculture. The importance of this study becomes more apparent when we know that according to a report by the Food and Agriculture Organization of the United Nations (FAO), utilizing new technologies can be effective in increasing the productivity of the agricultural sector.
Materials and Methods: This applied-developmental research was conducted using a descriptive-survey method. The statistical population of the research included 250 people (130 PhD students and professors of the Faculty of Agriculture of Razi University in Kermanshah province of Iran and 120 managers and specialists of companies located in growth centers and science and technology parks in the province). Using the Cochran formula and simple random stratified sampling method, 161 people were selected from the statistical population while 150 questionnaires were finally completed. The data collection tool was a researcher-made questionnaire with 31 closed-ended questions based on a five-option Likert scale (very low, low, medium, high, very high), whose face validity was examined by a panel of experts. The reliability of the questionnaire was also examined and confirmed by calculating Cronbach’s alpha of 0.89. Composite Reliability (CR) was also used to measure the reliability of the constructs. In addition, the fit of the designed model in relation to the factors affecting the transfer of lean technology in agriculture was also examined in two measurement and structural sections. The Structural Equation Modeling (SEM) measurement (model section) was studied with indicators such as factor loadings, Cronbach’s alpha, Composite Reliability (CR), convergent validity or Average Variance Extracted (AVE ) index as well as divergent validity (Fornell-Larker test). The structural model was also examined with indicators such as R² coefficient of determination, t-coefficients and path coefficients (Beta). The study data were analyzed using SEM and using the Smart PLS3 software package.
Results and Discussion: The analysis of the research data showed that six main factors with different impact coefficients would determine the success of transferring the lean technology to the agricultural sector of Iran. Key factors such as technological factors, legal factors, government support, infrastructural factors, human resource training and education, and management skills were found to be influential on the transfer of lean technology in agriculture. Interestingly, the path analysis showed that the interaction of these factors with each other (especially managerial factors and technological factors and human capital) had a double effect on technology transfer. The final research model was confirmed with favorable fit indices, indicating the high efficiency of the model in predicting the success of technology transfer. Also, the study results showed that different regions of the country required different strategies in technology adoption due to differences in the level of development.
Conclusion and Suggestions: The study findings indicated that the transfer of lean technology in Iranian agriculture would require a comprehensive and systematic approach. The model proposed in this study, as a comprehensive framework, can help policymakers, managers and farmers in facilitating the technology transfer process. For the effective implementation of this model, it is recommended: 1) Establish a special fund for the development of smart agriculture with the participation of the private and public sectors, 2) Establish a network of international communications and interactions for the transfer of lean technologies, 3) Develop national standards for smart agricultural products, and 4) Develop skill-based training programs in the field of lean technology for farmers and experts. Ultimately, the success of implementing this model requires the cooperation of all stakeholders in the form of an operational plan with specific quantitative and qualitative goals and specific time frames. Proper implementation of these strategies can lead to an average increase in productivity in the Iranian agricultural sector by up to 35 percent in the next five years.