نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی ماشین های کشاورزی، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
2 استاد ، گروه مهندسی ماشین های کشاورزی، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
3 گروه مهندسی ماشین های کشاورزی، دانشکدۀ مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Agriculture, as the most critical pillar of food security, requires the adoption of modern and forward-looking technologies in the field of mechanization. The present study was conducted with the aim of identifying and examining the effects of variables influencing the development of agricultural mechanization in Boroujerd Province. To this end, through field studies, documentary reviews, and the collection of expert opinions from specialists in agricultural mechanization, a total of 73 influencing variables were identified and validated. Among these, 64 variables with an importance coefficient higher than 75 percent were selected. After confirming the reliability of the collected questionnaires, cross-impact analysis was conducted across six groups, namely political, economic, sociocultural, technical, environmental, and legal. Then, 19 key variables from different groups were selected, and both direct and indirect influence calculations were performed on these. In the combined analysis of variables, the highest direct influence and dependence scores were obtained for variables education and extension and precision agriculture technology, with respective values of 904 and 1523. Similarly, the highest indirect influence and dependence scores were attributed to government financial support and environmental pressures on natural resources, with respective values of 963 and 1463. According to the results, sustainable development of agricultural mechanization will only be achieved when, alongside technical investments, sociocultural, political, and environmental variables are also given due consideration. Otherwise, the mechanization process will not only lack the necessary efficiency, but may also generate adverse consequences for food security, natural resources, and the overall trajectory of the country's sustainable development.
کلیدواژهها [English]
Agriculture, as the most fundamental human activity, plays a vital role in meeting basic human needs. With the rapid growth of the global population and the simultaneous limitation of production resources, the necessity of shifting perspectives toward agricultural production methods has become increasingly evident. In the broader discourse on mechanization, the involvement of experts from disciplines beyond agriculture such as environmental sciences and sociology, as well as learning from the experiences of leading countries, has gained particular importance. An examination of the study country's mechanization trajectory reveals a pattern of hasty decision-making, often lacking scientific justification. This is further compounded by incomplete implementation and inadequate monitoring. Nevertheless, forecasting the future path of agricultural mechanization and identifying the key factors that influence the strengthening of its fundamental elements requires a comprehensive and forward-looking approach. Accordingly, this study seeks not only to identify the influencing factors and variables but also to analyze their mutual effects. On this basis, it becomes possible to provide a holistic and data driven outlook on the future of agricultural mechanization.
In this study, both documentary sources and field investigations were combined with a qualitative phenomenological approach to capture the tacit knowledge of experts. Influential factors were identified through carefully designed expert interviews, extraction from research literature, and a researcher-developed questionnaire. Expert evaluations employed Likert scale ratings, a 0–3 numerical scoring method, and a –3 to +3 scale, with the interpretation of values based on expert judgment. The identified variables were then analyzed using the MICMAC software. To ensure questionnaire reliability, Cronbach’s alpha was calculated. Approximately 70 variables influencing the state of agricultural mechanization were identified and assessed by experts. After feedback was collected, the reliability of the questionnaire was confirmed, and the relative importance of variables was determined. Data processing was carried out using Python software. The subsequent stage involved developing the cross-impact matrix. Using the PESTEL framework, the variables were classified into six dimensions: political, economic, sociocultural, technological, environmental, and legal. A subset of representative variables from each category was then selected, with Python applied to support the selection process. Finally, influence/dependence maps were drawn, variables were ranked, and their interrelationships analyzed.
In this study, the reliability of the questionnaire was analyzed using Python software. The calculated Cronbach’s alpha coefficient was 0.868, and the standardized alpha value was 0.858. Since these values fall within the acceptable range of 0.80–0.90, the questionnaire demonstrated satisfactory reliability and was considered dependable for the subsequent stages of the research. The results of the analysis of combined variables from different groups indicate that among the variables, education and government support exhibit the highest level of direct influence, and therefore, can be considered as the main driving forces in the system. In contrast, technology and land consolidation show the highest direct dependency, reflecting their strong sensitivity to changes in other variables. From the perspective of indirect influence, variables such as government financial support and inflation rate rank higher, highlighting their role in shaping the system’s indirect dynamics. Likewise, in terms of indirect dependency, variables such as environmental pressures, sustainable agriculture, and climate change record the highest values, indicating that the stability and future trajectory of the system are strongly affected by changes in other factors. Overall, the emerging patterns suggest that variables like education, government policies, and financial support serve as key driving forces, whereas variables such as technology, land consolidation, and sustainable agriculture fall more into the category of dependent and reactive factors. Similarly, variables like labor regulations, which appear in the lower ranks, have limited impact on the overall system dynamics and play more of a marginal role.
The findings of this study highlight that agricultural mechanization in the country evolves within a complex and multidimensional system. Each category of variables, with its own drivers and outcomes, plays a crucial role in shaping the future of mechanization, while their interactions create a dynamic yet fragile development trajectory. For example, in the political group, government support policies, agricultural management structures, and financial subsidies emerge as key drivers, steering resources and influencing mechanization pathways. In contrast, variables such as rural development policies and agricultural machinery trade exhibit strong dependency, quickly reflecting shifts in macro level strategies. Indirectly, the integration of mechanization into national policy frameworks underscores the linkage between strategic policymaking and program implementation. Similar influence/dependence patterns are observed across economic, social, technological, environmental, and legal dimensions. Overall, the results suggest that sustainable agricultural mechanization requires more than technical and economic investment; it depends on institutional reforms, active stakeholder participation, and environmental considerations. Neglecting these aspects may undermine efficiency and pose risks to food security, natural resources, and long-term sustainable development.
Conceptualization: Mehrdad Jalalvand, Asadolah Akram and Majid Khanali. Methodology: Mehrdad Jalalvand. Software: Mehrdad Jalalvand. Validation: Majid Khanali. Formal analysis: Asadolah Akram. Investigation: Mehrdad Jalalvand. Resources: Asadolah Akram and Majid Khanali. Data curation: Mehrdad Jalalvand. Writing-original draft preparation: Mehrdad Jalalvand. Writing-review and final editing: Asadolah Akram and Majid Khanali. Visualization: Mehrdad Jalalvand. Supervision: Asadolah Akram and Majid Khanali. Project administration: Asadolah Akram and Majid Khanali.
A.R. All authors have read and agreed to the published version of the manuscrip
The data that support the finding of this study will be available from the corresponding author on reasonable requests.
Thank you to all those who have collaborated in this research.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The authors declare no conflicts of interest.
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