Introduction: The adoption of mechanization and the careful selection of appropriate technologies is a strategic approach to addressing resource constraints and food insecurity in the agricultural sector. These technologies must be evaluated and implemented by researchers, stakeholders, and decision-makers within the agricultural domain to ensure their relevance, sustainability, and efficiency. The rapid growth in global population, the increasing demand for food, and the limited availability of arable land and water resources have made it imperative to find more effective ways of enhancing agricultural productivity, the potential of which is found in the mechanization by reducing human labor, optimizing time, and enabling large-scale cultivation. However, the benefits of mechanization can only be fully realized when the machinery used is appropriate to the local conditions and when its use is optimized through proper planning and management. A critical step in achieving this goal is ‘identifying and ranking’ the factors affecting the productivity of agricultural machinery. Understanding these factors can provide a suitable foundation for optimizing the use of available resources, reducing operational costs, and increasing the efficiency and effectiveness of machinery use in agricultural processes. Despite its importance, there has been insufficient investigation into identifying these influencing factors and resolving the challenges associated with machinery inefficiencies in specific local contexts. So, this research aimed to address that gap by identifying and ranking the productivity indicators of agricultural machinery in the horticulture and agronomy sectors of Chaharmahal and Bakhtiari province of Iran. This region, known for its diverse agricultural practices and dependency on crop production for economic sustainability, provides a unique case study for understanding how machinery performance can be enhanced through a data-driven and participatory approach.
Material and Methods: In order to gather comprehensive and reliable data, a combination of qualitative and quantitative research methods was employed. The Delphi method, sequential exploratory method, and various statistical analysis techniques were utilized to ensure the accuracy and depth of findings. The Delphi method, in particular, is well-suited for eliciting expert opinion and reaching consensus on complex issues. Questionnaires were distributed among a diverse group of stakeholders, including university professors, technical experts, managers of governmental agricultural organizations, local farmers, and agricultural producers. These individuals were selected based on their experience and familiarity with agricultural machinery and local farming practices. The collected data were analyzed and validated using MaxQDA software, which helped identify and categorize nineteen main codes. These codes were grouped into two broad sets and further divided into six distinct subsets, based on their thematic and functional similarities. To assess the influence and interdependence of these indicators, and to determine their relative importance, the fuzzy Decision Network Process (DNP) technique was used. This technique allows for more nuanced decision-making by incorporating uncertainty and ambiguity, which are often present in expert-based assessments.
Results and Discussion: The study analysis revealed that the most critical factor influencing the productivity of agricultural machinery was ‘its design and its interaction with the cultivated land’. This finding underscores the importance of tailoring machinery design to suit the specific characteristics of local soil types, topography, crop types, and climatic conditions. Poorly designed machinery that does not align with these variables can lead to decreased efficiency, higher fuel consumption, increased wear and tear, and ultimately, a reduction in crop yield. Following machinery design, the second most significant factor identified was ‘management and planning optimization’. Effective planning and proper management of machinery usage (such as scheduling maintenance, coordinating operations with crop cycles, and optimizing routes and field operations) can greatly enhance machinery performance and longevity. Among the six subsets identified, the performance criterion was found to be the most impactful, carrying a relative weight of 38 percent. This criterion encompasses several key performance indicators such as output efficiency, operational time, maintenance frequency, and reliability. The needs and conditions adjustment criterion ranked second with a weight of 15 percent, highlighting the importance of aligning machinery use with local agricultural needs and environmental conditions. Machinery efficiency followed closely, with a 14 percent impact, reflecting the overall operational output of the machines relative to the resources used. In the second subset, the efficiency of agricultural machinery once again emerged as a key factor, with a 14 percent influence. This was followed by the machinery application index at 11.5 percent, which includes the suitability of machinery for various farming operations such as plowing, planting, harvesting, and irrigation. Lastly, the provision of financial resources held a 7.8 percent impact, emphasizing the role of financial support and access to funding in the procurement, maintenance, and upgrading of machinery.
Conclusion and Suggestions: The comprehensive identification and examination of the factors affecting agricultural machinery performance are crucial for optimizing efficiency, reducing costs, and improving productivity. This study sought to accomplish that by focusing on the horticultural and agronomic sectors in Chaharmahal and Bakhtiari province of Iran. By employing a multi-method approach that included the Delphi technique, statistical analysis, and fuzzy DNP modeling, the research was able to uncover and prioritize a wide array of indicators influencing machinery productivity. The identified factors were categorized into two main groups: dependent and independent. The dependent category included financial and performance-related factors, while the independent category comprised design and fabrication, technical specifications, and operational applications. The study findings indicated that the dependent category— especially, performance-related factors— had a more significant impact on overall machinery effectiveness and therefore, would deserve further research and investment. The implications of this study are far-reaching. Policymakers, agricultural machinery manufacturers, and local producers can use these insights to improve machinery design, enhance training programs, allocate resources more efficiently, and promote policies that support sustainable agricultural development. By focusing on these productivity indicators and their prioritization, stakeholders can work towards a more resilient, efficient, and food-secure agricultural system in the region.