Introduction
Agricultural mechanization, as one of the main pillars of transformation in production systems, plays a crucial role in increasing productivity, reducing production costs, accelerating agricultural operations, enhancing the quality of agricultural products, reducing operational time, and minimizing resource waste. International studies have shown that in countries where agricultural mechanization has been properly developed, significant growth in labor productivity and reduction in product waste have been achieved. Mechanization has also played a crucial role in adapting to climate change by enhancing access to critical planting and harvesting times. In Iran, numerous studies have been conducted in the field of agricultural mechanization in recent years, but many of them have focused on a specific region or product. Considering the importance of agricultural mechanization in increasing productivity, reducing costs, and accelerating agricultural operations, this research aimed to develop evaluation indicators for agricultural mechanization in the operations of major crops in Iran.
Method
In the first step, key indicators of agricultural mechanization were identified by reviewing sources and expert opinions. Then, using the fuzzy analytic hierarchy process (FAHP), the indicators were weighted. To rank the different provinces of the country in terms of agricultural mechanization development indicators, the TOPSIS multi-criteria decision-making method was employed. Subsequently, using GIS software, the results of the TOPSIS model ranking were utilized to display the geographical distribution of the level of agricultural mechanization indicators. Data was collected from official sources such as the Ministry of Agricultural Jihad, the Statistical Center of Iran, and specialized surveys.
Results
By identifying and weighting eight key mechanization indicators, including mechanization coefficient, machine density, labor productivity, and fuel consumption, it was found that these indicators can be significantly effective in evaluating the performance of mechanization in different provinces. Provinces such as Khuzestan, Fars, Golestan, and West Azerbaijan, as well as Kermanshah and Hamadan, ranked at the top of the rankings. In contrast, provinces like Sistan and Baluchestan, Alborz, and Hormozgan had the lowest ranks. These differences were primarily due to differences in technical infrastructure, government support, agricultural land density, and access to mechanization services. Therefore, the findings of this study indicate that agricultural mechanization in Iran has developed unevenly and unbalancedly, despite some relative progress in certain provinces. Sensitivity analysis also showed that the mechanization coefficient and machine density indices had the greatest impact on the final ranking of the provinces. Therefore, targeted investment in these two areas can effectively improve the level of mechanization in less developed regions. The results indicate a significant difference in the level of mechanization among different provinces, with some indicators, such as the mechanization coefficient and machine density (machines per unit area), having a substantial impact on determining the final rank. The findings of this study can help policymakers allocate resources more effectively and enhance the productivity of agricultural mechanization.
Conclusions
Using FAHP and TOPSIS methods, the provinces of the country were ranked in terms of the level of mechanization development. Practical suggestions for policymakers and the Ministry of Agricultural Jihad include: allocating resources and subsidies for mechanization on a regional basis and based on the results of the ranking, designing support packages for provinces with low levels of mechanization, including facilities for purchasing machines, training and support services, and developing a network of technical and repair services in underdeveloped provinces to reduce machine downtime. It is also recommended that farmers and production unions be encouraged to adopt cooperative farming models, utilizing mechanized equipment jointly, and to enhance farmers' technical knowledge and skills through advanced mechanization training courses. Conducting prospective studies using predictive models (such as regression or machine learning) to measure the development trend of mechanization in the next decade and combining MCDM methods with spatial data (GIS) to provide decision-making maps for regional planning can help assess the development of agricultural mechanization indicators in Iran. Additionally, this study's limitations include the lack of complete access to accurate data in some provinces and reliance on secondary sources. It is suggested that more precise field data be used in future studies. International comparisons should be made to model countries with high levels of mechanization and the impact of mechanization on the productivity of the entire production system (including water productivity, inputs, and crop yield) should be examined.
Acknowledgements
The authors would like to thank Shahid Chamran University of Ahvaz and Khuzestan Agriculture and Natural Resources Research and Education Center, for providing funding for this research.
Author Contributions
Nasim Monjezi: Data collection and analysis, and initial writing
Jafar Habibi Asl: Study design and text revision
Ehsan Houshyar: Providing expert opinions and reviews
Data Availability Statement
Not applicable
Ethical Considerations
This section states ethical approval details (e.g., Ethics Committee, ethical code) and confirms adherence to ethical standards, including avoidance of data fabrication, falsification, plagiarism, and misconduct.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding Statement
The authors received no specific funding for this research.