Introduction: Agricultural mechanization is recognized as one of the principal pillars of improving productivity, ensuring food security, and increasing rural income. The efficient use of machinery reduces labor intensity, shortens the production cycle, and improves the timeliness of field operations. However, its effectiveness is strongly conditioned by the physical and structural characteristics of farmland. In many developing countries, including Iran, agricultural land is characterized by small size, excessive fragmentation, and inappropriate spatial distribution, which together create serious constraints on the adoption of modern machinery. Land fragmentation increases machinery movement time, raises fixed costs per hectare, and reduces the economic returns on mechanization investments. Despite this, most previous studies have relied on descriptive analysis or simple linear relationships between farm size and productivity, and comprehensive structural models that simultaneously test the mediating role of mechanized production remain scarce. Accordingly, this study uses Structural Equation Modeling to explain the relationship between land structure (fragmentation and dispersion), mechanized production, and the economic performance of strategic-crop farmers in Noorabad Mamasani County.











Materials and Methods: The study area is Noorabad Mamasani County, located in the west of Fars Province, covering approximately 5,600 square kilometers, with nearly 90,000 hectares of agricultural land at an average altitude of 920 meters above sea level. The statistical population consisted of 19,601 farmers of strategic crops (wheat, barley, and sugar beet). Using Cochran's formula, a sample of 377 respondents was selected through random sampling. Data were collected using a researcher-made questionnaire covering plot area, number of plots, type of strategic crop, total production cost per hectare, net income, and more than fifty supplementary items. The land-structure construct was treated as a latent variable measured by two observed indicators: land dispersion and land fragmentation (average plot size), with plots smaller than four hectares classified as fragmented. Mechanized production was assessed as a composite index of machinery use across the three main stages of cultivation (planting, growing, and harvesting) based on a five-point Likert scale. Economic performance was measured through the two quantitative indicators of net income and total production cost per hectare. Data analysis was conducted using a two-step approach with Structural Equation Modeling in AMOS, following the method of Hair et al. (2019). Model fit was evaluated using GFI, CFI, RMSEA, and CMIN/DF indices, and path coefficients were tested at the five percent significance level.











Results: The goodness-of-fit indices indicated an acceptable and desirable model fit. GFI and CFI were both equal to 0.914, exceeding the recommended threshold of 0.90; RMSEA was 0.091, within the acceptable range; and CMIN/DF was 4.095, below the standard value of 5. The structural model showed that the path from land structure to mechanized production was statistically significant and negative (β = -0.193; p = 0.033), meaning that the less favorable the land structure, the lower the level of mechanized production. Both components—land fragmentation and land dispersion—had significant relationships with the land-structure construct (coefficients of 0.847 and 1.273, respectively, with significance levels below 0.001).





In contrast, the direct path from mechanized production to economic performance was not statistically significant (β = -0.139; p = 0.243); therefore, the hypothesis of a significant direct effect of mechanization on economic performance was rejected. Although the theoretical framework proposed mechanized production as a mediator between land structure and economic performance, the available model output was insufficient for a definitive confirmation of the indirect effect, since the indirect coefficient and its bootstrap significance test were not reported. Moreover, the t-statistic for the "cost" variable within the economic performance factor was non-significant, indicating that farmers' economic behavior in the study area is not necessarily shaped by precise profit-and-cost calculations.





Discussion: The findings indicate that land structure plays a decisive role in shaping the level of mechanization. In small and dispersed plots, mechanized operations face limitations such as repeated machinery relocation, wasted time, and reduced field efficiency. The negative loadings for machinery-related items reinforce a structural contradiction: as fragmentation increases, mechanization turns from a productive driver into an overhead cost burden.





The non-significant relationship between mechanization and economic performance can be explained by the high and inefficient costs of mechanization services in fragmented lands, where the link between cost and economic performance becomes disconnected. Farmers here continue cultivating due to subsistence dependence on land—a form of traditional persistence—even when production costs exceed income. This emphasizes the need to revise support models toward incentives that directly target input costs. The high factor loadings of the land-structure items also confirm the depth of the fragmentation crisis facing the study area.











Conclusion: The present study examined the relationship between land structure, mechanized production, and the economic performance of strategic-crop farmers in Noorabad Mamasani County. The structural model confirmed a negative and significant relationship between land structure and mechanized production, while the direct effect of mechanization on economic performance was not confirmed. From an applied perspective, the findings emphasize the necessity of simultaneously addressing the structural characteristics of land and the scale of mechanization technology. In small and dispersed plots, mechanization policy should not rely solely on large and heavy machinery; rather, technology must be selected in proportion to plot size, type of operation, and the farmer's economic conditions. It is recommended that planners integrate land consolidation with mechanization programs, develop scale-appropriate technologies for small plots, strengthen accessible mechanization services, and conduct complementary economic studies that directly measure rental, fuel, maintenance, and depreciation costs. Factors such as inheritance systems, fuel costs, and access to credit were not independently tested in the present model and may constitute the subject of future research.