Abstract
Introduction
In recent years, the role of rural areas in development has evolved, especially in developing countries. Rural regions are no longer seen merely as agricultural production sites but as key drivers for economic diversification, poverty reduction, and sustainable livelihoods. Among various strategies, the cultivation of high-value crops-particularly medicinal plants-has emerged as a viable approach to enhance rural resilience and income generation. Medicinal plants contribute significantly to global health, economies, and biodiversity, with the global market value projected to exceed $450 billion by 2025. In Iran, despite the presence of over 2,400 medicinal plant species and a long history of traditional medicine, this sector remains underdeveloped, often limited to low-scale processing and raw product sales. Homaijan district in Fars province, with its favorable climate and diversity of plant species, offers strong potential for medicinal plant cultivation. However, farmers’ adoption of these crops remains uneven due to varying socio-economic, institutional, and behavioral factors. Previous research highlights the importance of training, market access, and supportive policies in fostering adoption, yet region-specific insights are still scarce. This study aims to analyze the key factors influencing farmers’ adoption of medicinal plant cultivation in Homaijan using a descriptive-applied approach. Grounded in the Theory of Planned Behavior and Diffusion of Innovations, the research employs the logit model to assess binary adoption behavior based on data from local agricultural stakeholders. The findings are expected to fill regional knowledge gaps and inform targeted interventions to promote sustainable cultivation, economic empowerment, and effective rural development strategies.
Materials and Methods
This study employed a descriptive-analytical and cross-sectional design, conducted in the Homaijan district of Sepidan County, Fars Province, Iran. The region, situated between Shiraz and Ardakan, is characterized by a temperate climate, diverse agro-ecological features, and a mix of rain-fed and irrigated agricultural systems. Irrigation sources include rivers, wells, springs, and traditional qanats. The dominant occupations are farming and livestock breeding, with main crops including grapes, walnuts, almonds, wheat, and legumes. The target population consisted of all agricultural producers in Homaijan, totaling 3,759 individuals based on the latest statistics. Using Cochran’s formula, a sample of 150 respondents was selected through simple random sampling. Ethical standards were observed throughout the study; informed consent was obtained from all participants, and data collection was approved by a relevant academic ethics committee. Data were collected using a structured, researcher-designed questionnaire with items rated on a five-point Likert scale. The content validity (face validity) of the instrument was assessed by a panel of experts in rural development, agricultural extension, and medicinal plant cultivation. To evaluate instrument reliability, a pilot test was conducted on a separate sample, yielding Cronbach’s alpha values above 0.70, indicating acceptable internal consistency. The study employed a mixed-methods analytical framework, combining quantitative descriptive and inferential statistical approaches. Descriptive statistics such as mean, standard deviation, and frequency were used to describe demographic and variable distributions. For inferential analysis, independent sample t-tests compared means between adopters and non-adopters, and binary logistic regression (logit model) was used to identify predictors of adoption. All statistical analyses were conducted using SPSS version 26 and STATA version 15, enabling accurate modeling of both quantitative patterns and binary decision outcomes.
Results and Discussion
The study analyzed responses from 150 farmers in Homaijan district using descriptive and inferential statistics. The mean age of respondents was 53 years, and the average landholding size was 3.5 hectares. Over 90% of respondents were covered by agricultural insurance, suggesting a risk-mitigating factor for innovation adoption. Independent samples t-tests revealed significant differences between adopter and non-adopter groups across most behavioral and perceptual variables. Adopters scored significantly higher on all items of behavioral intention toward cultivating medicinal plants (p < 0.01), including willingness to follow expert advice and attend training programs. This supports the Theory of Planned Behavior, emphasizing the mediating role of intention in behavior change. For perceived expected performance, adopters reported higher income expectations, cost savings, and easier crop management. However, they did not expect higher physical yields compared to wheat, highlighting a distinction between economic and volumetric productivity. Among internal psychological traits, adopters demonstrated greater risk tolerance, higher awareness of medicinal plant benefits, and were more influenced by environmental stressors like drought (p < 0.01). In contrast, access to labor or familiarity with production stages did not significantly differ between groups. Social influence emerged as a key factor: adopters reported stronger encouragement from family, peers, and agricultural experts. The highest differential was observed in acting on advice from experienced farmers (t = -3.58, p = 0.01), underscoring the role of peer learning networks. The logit model identified six statistically significant predictors of adoption: farming experience (β = 0.06), smaller land size (β = -1.27), internal psychological traits (β = 1.07), social influence (β = 0.89), extension activities (β = 1.77), and behavioral intention (β = 1.44). The model explained 50% of the variance (Pseudo-R² = 0.50). Marginal effects showed that a one-unit increase in extension engagement raised the probability of adoption by 44%, while each hectare increase in land size reduced it by 32%. These findings highlight the multifactorial nature of adoption behavior and the need for tailored, multi-dimensional interventions in rural innovation systems.
Conclusions
This study showed that the adoption of medicinal plant cultivation in Homaijan is shaped by a combination of individual, social, institutional, and experiential factors—beyond technical or economic considerations. Variables such as personal motivation, social influence, training activities, and farming experience significantly impacted farmers' willingness to adopt, emphasizing the importance of behavior, perception, and community dynamics. The findings align with the Theory of Planned Behavior and Diffusion of Innovations, highlighting the role of self-efficacy, social norms, and targeted extension. If validated elsewhere, these insights could inform broader strategies for promoting agricultural or environmental innovations. Practically, the study suggests that financial incentives alone are insufficient without fostering farmers’ intrinsic motivation and providing supportive learning environments. Supporting smallholders and promoting peer-led extension may further drive adoption. Nonetheless, unresolved issues remain—such as the roles of value chains, branding, and policy frameworks. Future research should address these gaps through localized, interdisciplinary approaches. Ultimately, medicinal plant cultivation can be a viable path to sustainable rural development—if supported by evidence-based, participatory, and socially rooted strategies.