Factors influencing the agricultural extension model sites in Iran

Abstract

This study aims to explore the factors and challenges influencing Agricultural Extension Model Sites (AEMSs) in improving irrigated wheat production in Iran. An exploratory survey was conducted to investigate changes related to pre-planting, planting, harvesting, and post-harvesting activities by examining farmers who are members of wheat AEMSs. The goal was to understand the effects and challenges faced by these sites in agriculture. The study revealed that challenges at model sites are grouped into six categories: technical-structural, planning, political-motivational, financial and credit, agronomic, and cooperation and coordination factors. These six factors accounted for 68.55% of the variance in AEMSs challenges. The most significant effects of AEMSs included improving the efficiency of agricultural activities, and increasing farmers’ trust in agricultural extension agents by providing practical solutions to enhance productivity. The main practical implication is that the Agricultural Institute of Education and Extension in Iran can objectively introduce farmers to the best agricultural techniques and accelerate the adoption of innovations in agriculture by creating and expanding these sites for various products and topics. The study offers new insights into agricultural technology transfer models and addresses a gap in the existing literature regarding model sites as an Iranian agricultural extension initiative. This is particularly significant in the context of international academic literature. This study investigates the factors and challenges associated with the Agricultural Extension Model Sites (AEMS) approach, which has been designed and implemented in agricultural extension in Iran. While similar extension models are utilized internationally, AEMS demonstrates a superior ability to persuade farmers to adopt optimal agricultural practices. This paper presents evidence of the effectiveness of agricultural extension models, particularly in enhancing the productivity of the strategically important wheat crop.

Introduction

The global community has acknowledged the need to transition to more comprehensive, flexible, and sustainable food systems^1,2,3,4. Achieving the Millennium Development Goals (MDGs), such as alleviating hunger and promoting food security, heavily depends on increasing agricultural productivity, especially in the agronomic sector^5,6,7,8. Agriculture is considered a primary engine of growth in most developing economies, particularly in Asia^9,10. Despite ongoing efforts¹¹, significant challenges remain. Since, Agroecosystems offer a range of services to communities, encompassing both provisioning and regulating aspects, such as food, fiber, and climate mitigation^12,13,14,15. A key strategy for improving agricultural productivity is agricultural extension, which is expected to play a vital role in this transition¹⁶. Effective extension services are crucial for supporting farmers in adapting to new global policies^17,18,4. Therefore, to enhance sustainability on a global level, agricultural extension services play a crucial role in connecting research with practical farming¹⁹. The FAO²⁰ defines agricultural extension as a system designed to provide farmers, relevant organizations, and market players with access to knowledge, information, and technologies, facilitating their interaction with partners in agricultural research, education, and trade. Agricultural extension services must also adapt to global changes, as higher adaptability is essential for sustainable agricultural and rural development^21,5,22. By enhancing farmers’ knowledge and awareness, extension services can improve farm and agricultural technology management, leading to increased productivity²³ and greater profitability for farmers²⁴. However, the transfer of agricultural knowledge must be accompanied by strengthening farmers’ problem-solving, decision-making, and management skills²⁵. This is especially important in developing countries, where agriculture is becoming increasingly knowledge-intensive²⁶ and smallholders often lack access to essential information, skills, and technologies^27,28,29. In these contexts, governments can target farmers’ needs more effectively through agricultural extension services³⁰. In countries like Iran, where smallholdings dominate the agricultural landscape, improving productivity can only be achieved through the dissemination of agricultural technologies among rural smallholders^31,32. Focusing on agricultural knowledge generation and interactive learning networks can help collect and integrate knowledge from different stakeholders³³.

The theoretical framework underlying the rural extension approach emphasizes the importance of interactive learning and participatory engagement between extension agents and farmers³⁴. This interaction enables farmers to participate in their own learning processes, thereby fostering a more sustainable agricultural system^35,13. In this context, the AEMS initiative in Iran represents a modern solution aimed at bridging the gap between traditional practices and contemporary agricultural demands. While the concept of AEMSs may seem relatively new in the Iranian context, it is important to note that farmer-owned demonstration plots have long been a traditional method in agricultural extension. The novelty of this research lies in its exploration of AEMSs in Iran, a context where the strategy has not been comprehensively studied.

However, it is crucial to justify why this investigation is innovative not only within Iran but also in the broader context of international academic literature. Although AEMS may be a relatively recent undertaking in Iran, their implementation on a global scale is not new. Therefore, a review of the research literature revealed that most studies in the field of agricultural extension focus on identifying the factors influencing the provision of agricultural extension services^{36,37,38,39,40,41,42} or examining the obstacles and challenges associated with agricultural extension^{43,44,45,46,47,48,49,24}. Additionally, some research investigates the performance or effects of extension services^{50,51,19,52,53,54,55,56,57,9,58}. The novelty of this research lies in its comprehensive approach: not only does it examine the current status of agricultural extension services on the site, but it also explores both the factors affecting its performance and the challenges the AEMS faces in providing these services. In addition, we conducted a thorough comparison of various agricultural extension models with AEMS, identifying the advantages and disadvantages of each. Hence, this research fills a gap in knowledge in the agricultural extensi.

n literature and can serve as a valuable resource for other researchers. By building on the findings and field results of this study, future research can further advance the understanding and effectiveness of agricultural extension services.

The research question guiding this study is: How effective are AEMSs in improving agricultural productivity and sustainability in irrigated wheat farming in Iran?

Literature review

AEMS is a complex of production units belonging to about 20–25 farmers from one to three neighboring villages in which the technical recommendations of subject matter experts, the research findings of the research institute and policies executive parts agricultural ministry are implemented and promoted through accumulating the resources and facilities of the executive, research, and extension departments^59,60. while these sites are similar to some other extension method such as demonstration farms in transferring knowledge and skills to farmer, the main philosophy behind their planning was to reduces discoordination among three main actors of extension in ministry of agriculture i.e. extension, research and executive bodies. These sites were to synergies the different department extension activates based on the main principles of extension like participation and learning by doing or seeing. AEMSs serve as a coordinated platform where technical recommendations from subject matter experts, research outcomes from institutes, and executive policies from agricultural ministries are integrated and implemented. These model sites bring together resources from extension, research, and executive branches to synergize their efforts⁶¹. While similar to traditional demonstration farms that have been used worldwide for knowledge transfer, AEMSs aims to reduce fragmentation among these three key actors in agricultural development⁶².

AEMSs Framework provides a visual representation of the interactions and integration between research, extension, and executive policies within AEMSs. It highlights the central role of collaboration and feedback among these components, ensuring that technical recommendations are effectively applied in the field. The model also emphasizes the flow of knowledge between subject matter experts, agricultural policies, and farmers, creating a continuous cycle of learning and improvement. The main production unit is managed by extension agents, while farmers are involved in subunits, receiving guidance and adapting to new practices, which in turn enhances overall productivity and sustainability (Fig. 1).

Fig. 1

A schematic of the production area of AEMSs, the main unit (MU), and the sub-units (SU).

The main distinction of AEMSs lies in its holistic approach that combines participation, learning-by-doing, and fostering collaboration between research and extension, a framework crucial for addressing the challenges of modern agriculture. Furthermore, this integrated model enhances not only the adoption of innovations by farmers but also ensures that research findings are directly applicable and effectively implemented in the field⁶³. AEMSs typically involves a main production unit managed by an extension agent, along with subunits owned by other farmers, ensuring a consistent application of technical recommendations across a larger area⁶⁴. In Iran, AEMSs has been adopted as a key strategy to improve agricultural productivity and sustainability, providing a collaborative platform where local knowledge is integrated with national agricultural policies to ensure effective implementation in rural areas⁶⁵.

Demonstration has long been a crucial method for knowledge transfer and learning in agriculture, dating back centuries as a means of engaging communities in tangible, hands-on experiences⁶⁶. Recently, there has been a resurgence of interest in Europe regarding demonstration-based methods for disseminating research-based and practice-oriented agricultural innovations⁶². This approach goes beyond mere information dissemination by providing measurable outcomes, such as the number of farmers acquiring new skills, the expansion of improved agricultural lands (including crops, horticulture, and natural resources), and productivity gains through optimized resource use (e.g., reductions in water, pesticides, and fertilizers) as well as the production of safer crops⁶⁷. Face-to-face interactions and direct observation of techniques are considered among the most effective means of knowledge sharing, providing farmers with reliable, context-specific advice⁶⁸. In Iran, similar approaches have been adopted in various agricultural extension projects, emphasizing practical learning and skill development for improving agricultural sustainability⁶⁰.

AEMSs in Iran further expand on these approaches by integrating research, technical recommendations, and executive policies to enhance agricultural productivity and sustainability. Unlike traditional methods, these models employ a more comprehensive, participatory approach, ensuring close collaboration between farmers, research institutions, and executive bodies. Studies indicate that these models help improve farmers’ performance.

Demonstration plays a pivotal role in promoting sustainable agricultural practices by facilitating the adoption of environmentally friendly and economically viable production methods^{69,70,71,72,73}. This approach enables a broader dissemination of best practices across other production units⁷⁴. While the overarching goal of AEMSs is to reach all target producers, initial efforts focus on a selected group, ensuring they receive intensive training to facilitate significant behavioral change. These trained individuals can then influence their peers within rural communities, thereby amplifying the impact of sustainable practices^75,76. Given the critical importance of demonstration sites in advancing sustainable agriculture, this research seeks to investigate the status, challenges, and effects of irrigated wheat extension model sites in the agricultural sector of Iran, aiming to better understand current conditions and develop strategies for their improvement.

Despite the lack of comprehensive research in the area of agricultural extension training, some studies have explored its effects, particularly in the context of Iran. For instance, Shahabi et al.⁷⁷ investigated the impact of extension training on rural families in Faridan County, Isfahan. Their findings indicated that training activities significantly enhanced participants’ levels of information, job and skill awareness, adoption of modern animal husbandry practices, creativity, management skills, and self-confidence. Similarly, Alibaigi and Ghanbarali⁷⁸ examined the effects of agricultural extension programs on the performance of wheat farmers in Kermanshah. Their study revealed that the farmer field school approach markedly improved farmers’ performance and income levels. Through a t-test analysis, they found significant differences in age, educational background, and the area of irrigated lands between farmers who participated in the field school and those who did not. The research highlighted that contact with extension agents, participation in extension courses, and recommendations from neighboring farmers and local leaders were crucial factors influencing farmers’ engagement in the field school program⁷⁹. These studies underscore the importance of extension training in enhancing agricultural practices and outcomes, yet they also reveal the necessity for more extensive research to further understand its broader implications in various agricultural contexts.

The implementation of AEMSs in Iran represents a relatively new strategy in agricultural extension, yet it draws from longstanding traditional methods, such as farmer-owned demonstration plots, that have been utilized for decades. The objective of this article is to illuminate the status, challenges, and effects of irrigated wheat AEMSs in the agricultural sector, contributing to a deeper understanding of their current status and potential improvements. Despite limited existing studies, such as those by Shahabi et al.⁷⁷ and Alibaigi and Ghanbarali⁷⁸ which evaluated the impact of extension training and farmer field schools in Isfahan and Kermanshah, respectively, the discourse on AEMSs specifically remains sparse. Notably, Faraj Elah Hosseini et al.⁸⁰ found that AEMSs played a critical role in enhancing wheat farmers’ knowledge in Khorram Abad County, Prioritization management as the most significant factor among various agricultural and educational determinants. Furthermore, Nowrozi et al.⁸¹ emphasized the importance of agricultural extension education in promoting sustainable rural development, highlighting its influence on environmental dimensions over social aspects. In their assessment of agricultural extension challenges in Iran, Alizadeh et al.⁸² identified gaps in infrastructure and human resources despite advancements in knowledge and communication management. Collectively, these studies illustrate the pressing need for comprehensive research on AEMSs, not only to address the specific challenges faced in Iran but also to contribute to the broader international academic dialogue regarding agricultural extension practices. The objective of this article is to illuminate the status, challenges, and impacts of irrigated wheat AEMSs in Iran, aiming to offer insights and solutions for their improvement. However, to substantiate the novelty of this research within the academic literature, it is crucial to situate these findings in the broader context of international studies on agricultural extension. While AEMSs implementation is a relatively new undertaking in Iran, existing literature must be critically reviewed to clarify its unique contributions and limitations compared to global practices. In addressing the challenges faced by agricultural extension in Iran, Yazdanpanah and Rahimi fayzabad⁸³ identified several critical factors contributing to its inefficacy, including “inattention to the agricultural sector,” “executive policies,” and “economic factors,” alongside structural and managerial weaknesses. Similarly, Salehi et al.⁸⁴ analyzed agricultural demonstration plots in Hamedan province, concluding that the implementation of extension models significantly reduced the application of inputs while increasing both revenue and crop yields. Ghasemi and Tavakoli⁸⁵ further demonstrated that model sites in land reclamation projects across Khuzestan and Ilam provinces led to heightened satisfaction among users and improved performance.

Studies indicate that since the democratization of African countries in the 1990s and 2000s, the models of agricultural extension services have undergone significant changes⁸⁶. The democratization process led to reforms in agricultural extension, resulting in decentralized local governance and pluralistic extension systems^35,86. These shifts from centralized to decentralized approaches have produced varying local outcomes over time⁸⁶. In Uganda, the National Agricultural Advisory Services (NAADS) program enables farmers to receive extension services for specific agricultural enterprises, either individually or in groups. This approach aims to address farmers’ priorities, interests, and perceptions, including their views on agricultural extension services^87,88. In contrast, Kenya offers a variety of extension services, such as public, commodity-based, and community-based services, primarily for high-value crops like coffee, pyrethrum, and tea, which are cultivated using technologies developed by government laboratories⁸⁹. A small segment of Kenyan smallholder farmers benefits to some extent from commodity-based extension in poorer areas, mainly from non-commercial providers not driven by profit⁸⁹. Ethiopia’s agricultural extension system, the largest in Africa, has remained largely unchanged for decades⁹⁰. A recent study in Ethiopia recommended transitioning from the current burdensome system to a customized, demand-driven agricultural extension system to better respond to farmers’ diverse environments and socio-economic conditions in accessing agricultural technologies⁹¹.

Internationally Marsh and Pannell⁹² highlighted systemic challenges within extension services, such as insufficient audience-oriented delivery and weak ties between research and extension programs. Addressing these complexities through a thorough literature review will strengthen the argument for the uniqueness and relevance of AEMSs in both the Iranian context and the broader academic landscape.

Cawley et al.⁹³ conducted a study to assess the influence of agricultural knowledge transfer sources on farm profitability during the economic recession period (2008–2014) in Ireland. Their findings indicated that farmers who utilized these knowledge transfer sources increased their marginal profit by 12.3% during this challenging period. This highlights the critical role of effective agricultural extension systems in enhancing farm productivity and financial stability.

Alexopoulos et al.⁹⁴ investigated the factors influencing farm demonstrations and found that the initial steps in organizing on-farm demonstrations are crucial for achieving their objectives. Specifically, decisions regarding the relevance to farmers’ needs and the structure of the event are of paramount importance.

In Ethiopia Wordofa et al.⁵⁶ examined adoption of improved agricultural technology and its impact on household income. The research indicates that households utilizing advanced agricultural technologies had an average annual farm income increase of 23,031.28 Birr (the official currency of Ethiopia, with an exchange rate of 1 USD = 27.6017 Birr as of 04 October 2018) compared to those not employing such technologies. The findings underscore the significance of encouraging the adoption of diverse and complementary agricultural technologies among rural smallholders.

In Ghana, Addison et al.⁹⁵ investigated the impact of adopting selected agricultural technologies on rice farmers’ income distribution. The study found that education, farm size, land ownership, and participation in extension training programs promoted technology adoption, while being female hindered it. The adoption of improved rice seeds and fertilizers notably increased farmers’ net income and reduced income inequality, demonstrating that technology uptake has an equalizing effect on income distribution among rice farmers.

In China, Fan et al.⁹⁶ examined the agricultural extension system’s impact on conservation agriculture in the Shaanxi Plain. Their research revealed a significant communicational gap between extension staff and the overall extension system. They suggested that improving the efficiency of this system and fostering greater adoption among farmers could be achieved by emphasizing farmers’ information exchange and enhancing environmental awareness. This finding is particularly relevant in the context of Iran, where similar challenges in communication and innovation transfer exist within agricultural extension frameworks, potentially hindering the adoption of sustainable practices.

In Uzbekistan, Djuraeva et al.⁹⁷ investigated the influence of various agricultural extension types and forms on technical efficiency in wheat production during Uzbekistan’s transition period. Their empirical assessment uncovered considerable disparities in the technical efficiency of water usage among wheat farmers. They found that not only did the characteristics of farmers play a role, but also the frequency of extension visits and cooperative extension approaches significantly affected the differences in technical efficiency.

Landini and Conti⁹⁸ by exploring the Factors contributing to rural extension agents’ support for a transfer of technology (ToT) approach, find that the primary factors supporting the ToT approach include a tendency to blame farmers, viewing extension as a collaborative process involving dialogue and inter-institutional coordination, prioritizing the modernization of agricultural production, and endorsing conventional modern agriculture. Conversely, factors that diminish support for the ToT approach include possessing a self-critical attitude, prioritizing the formation and strengthening of farmer organizations, holding a university degree, and having extensive experience as an extension agent.

In Iraq, Khamis et al.⁹⁹ identified the challenges and obstacles facing the Agricultural Extension Organization. They concluded that the organization today faces a set of issues affecting the work process, the implementation of activities, and the efficiency of agricultural promoters across various extension programs. These challenges impact the effectiveness of carrying out extension tasks, and the adopted approach does not align with the current stage’s requirements. The organizational structure of the extension organization includes numerous work-related problems and obstacles, with a clear lack of delegation of authority. Additionally, there is a significant weakness in the use of information and communication technology on an international level, as well as with relevant authorities. The study provided recommendations aimed at improving and developing the performance of the extension organization.

In Saudi Arabia, Ismail & Ahmed⁵¹ by studing the agricultural extension implication on food security revealed that the spread and adoption of agricultural innovations, sources of agricultural extension information, extension campaigns, expert agricultural systems, and the privatization of extension services, and their impact on food security in agricultural communities. Agricultural extension is crucial for developing the agricultural and rural sectors. The Kingdom of Saudi Arabia has a strategic plan to provide extension services through the General Administration of Agricultural Extension and enhance cooperation with governmental and private institutions to advance the agricultural sector and achieve food security.

A study by Kalogiannidis and Syndoukas¹⁹ on the Greek experience with the Demo Farms model revealed that agricultural workshops and training significantly improve farm productivity. Additionally, access to government demonstration farms and media-based agricultural programs positively impact farm productivity.

These insights underscore the necessity of tailored extension strategies that consider local contexts, which can be beneficial for improving water management practices in Iran’s wheat production sector as well.

Following the review of global previous research on the effects and challenges of providing agricultural extension services in various models, different types of global extension models were introduced. Subsequently, all these models were compared with AEMSs, and their similarities and differences with the sites were presented in Table 1.

Integrated crop management (ICM)

ICM involves the application of various techniques and practices to optimize crop production while minimizing environmental impacts. It includes the use of appropriate crop varieties, efficient irrigation practices, pest management, and soil fertility management. ICM focuses on the sustainable use of resources and aims to achieve both high productivity and environmental sustainability¹⁰⁰.

Integrated pest and crop management (IPCM)

IPCM is an approach that combines crop management with pest control practices. It involves the use of multiple methods, such as biological, chemical, and cultural techniques, to manage both crops and pests in an integrated way. The goal is to optimize crop production while minimizing the use of harmful pesticides and reducing environmental impact¹⁰¹.

Demonstration farms (DF)

The idea of teaching farmers through demonstrations began with the formation of Agricultural Societies in the eighteenth century, driven by an increasing number of public-spirited individuals¹⁰². Farm demonstrations operate on the principle that “seeing is believing,” allowing farmers to witness new technologies, practices, or systems in action on similar farms. This interaction with peers helps farmers decide whether to adopt these innovations, particularly when they are costly, complex, or require major changes^62,34,103. Dr. Seaman Knapp, known for pioneering the concept of demonstration farms, articulated the purpose of demonstration farming as providing a practical example to farmers. His goal was to showcase the best and most profitable methods of growing standard crops, encouraging farmers to actively participate in these demonstrations. Knapp believed this approach would demonstrate that farmers could significantly increase their annual crop yields and achieve higher returns for their efforts¹⁰⁴. Demonstration farms, also known as model or pattern farms, serve as venues for sharing knowledge, offering advice, and conducting on-farm research^105,63. These farms facilitate the dissemination of practical knowledge and are used primarily for research and demonstration of agricultural practices^94,33. Managed by universities or government agencies, demonstration farms aim to promote innovation and improve farm productivity¹⁰⁶.

Integrated pest management (IPM)

IPM is a sustainable approach to managing pest populations in agricultural systems. It combines biological, cultural, mechanical, and chemical control methods to minimize the impact of pests while ensuring that the environment and human health are protected. IPM is an important tool for promoting sustainable agriculture by reducing reliance on chemical pesticides¹⁰⁷.

Participatory variety selection (PVS)

PVS is a process that involves farmers in selecting crop varieties based on their preferences and local conditions. It includes three main phases: identifying the needs of farmers, searching for suitable crop varieties to test, and conducting experiments on farmers’ fields to disseminate the best-performing varieties. PVS empowers farmers to make decisions on the varieties they grow, enhancing both productivity and sustainability¹⁰⁸.

Agricultural innovation platforms (AIPs)

AIPs consist of diverse actors who collaborate to facilitate innovation. Key principles include having a wide range of stakeholders, addressing common issues rather than individual agendas, and being facilitated by a neutral party with authority. Early successes help motivate continued commitment. The benefits of innovation should extend to many members, with ongoing learning and knowledge exchange being central. Mutual respect among members, despite differing opinions, and systems ensuring transparency and accountability, are crucial^{109,110,111,112}.

Farmer field schools (FFS)

FFS provide participatory training for farmers, focusing on field-based experiential learning to improve their decision-making and agricultural practices. Originating in the 1980s, this approach enables farmers to analyze their ecosystems and adapt techniques for pest management, soil fertility, and sustainable production. Regular group meetings during crop cycles foster peer learning and empowerment, promoting long-term community-driven agricultural improvements^113,114.

Integrated pest management (IPM) farmer field schools (IPMFFS)

IPMFFS build on the FFS model by emphasizing sustainable pest management practices that minimize chemical pesticide use. Through observation and analysis of agroecosystems, participants learn integrated techniques such as biological control, crop rotation, and habitat management. This model enhances ecological resilience and productivity while mitigating environmental impacts and reducing farmer dependency on costly external inputs^115,116,117.

Crop demonstrations (CD)

CD emphasize a “learning by doing” approach, where farmers directly observe and implement advanced agricultural techniques on their fields. By comparing treated and control plots, participants gain firsthand experience with practices like precision seed sowing, appropriate fertilizer use, pest control, and soil fertility management. This method encourages adoption of sustainable technologies and practices, resulting in improved productivity and higher incomes for participating farmers^118,119,120.

Transfer of technology (ToT)

The Transfer of Technology (ToT) approach in agriculture views innovation as a straightforward and one-way communication process, where agricultural experts develop innovations, extension agents transmit or transfer these innovations, and farmers adopt them^121,122,123. This approach often overlooks the local knowledge of farmers, aiming to replace it with scientific agricultural knowledge, primarily associated with Green Revolution principles. It seeks to alter the perceived passive attitudes and traditional practices of farmers^124,123. Moreover, several authors point out that the ToT approach relies heavily on the benefits of modern science without recognizing its potential negative indirect effects¹²⁵ or the limited flexibility in adapting technologies to diverse environmental and socio-productive contexts¹²⁶.

Table 1 Comparison of AEMSs with Oth¹²⁶er agricultural extension models.

Materials and methods

Type of study

This research is applied in nature, aimed at practical outcomes. It is a field study, indicating that the variables were observed and controlled under real-world conditions. In terms of methodology, it is descriptive-correlational, meaning it seeks to explore relationships between variables, and data collection was conducted through survey methods, which is common in descriptive research.

Statistical population and sample size

To determine the appropriate sample size for the study, the standard deviation of the dependent variable (wheat yield) was first calculated. Calculating the standard deviation is essential as it helps determine the degree of variation in wheat yields across the population. A larger standard deviation indicates more variability in yield, requiring a larger sample size to achieve an accurate estimate. The wheat yield standard deviation was calculated from the available data on irrigated wheat across different regions of Iran, ensuring that the sample size estimation accounted for the diversity of climatic conditions. In this study, the Cochran sampling formula was utilized to determine the sample size for several key reasons: (1) Precision and Confidence: The Cochran formula enables researchers to calculate an optimal sample size based on a desired level of precision, a desired level of confidence, and an estimated proportion of the population. (2) Known Population Variance: When the population variance is known, this formula is the most effective method for determining the sample size. (3) Reduced Bias: This approach is less biased compared to other methods of determining sample size¹²⁷.

Cochran’s formula (Eq. 1) was then used to estimate the required sample size. The calculation yielded a sample size of approximately 170 farmers. However, in order to ensure a more representative and robust sample, and to account for potential non-responses or other unforeseen factors, the sample size was slightly increased to 180 farmers. This adjustment ensures that the results remain valid even in cases where a small portion of the sample may not respond or participate in the full survey process.

$$\begin{aligned} n&=\frac{{N\left(t.s\right)}^{2}}{\text{N}{\text{d}}^{2}+{\left(\:t.s\:\right)}^{2}}\nonumber\\ n&=\frac{355{\:(1.96\times\:\:2.02)}^{2}}{\left(\:355\:\times\:{0.21}^{2}\right)+{\:(1.96\times\:\:2.02)}^{2}}=166.67\approx\:170 \end{aligned} $$

(1)

In Table 2, the provinces listed represent different regions of Iran, selected based on a fourfold climatic classification. This classification follows a modified version of Kipper’s climate classification, adjusted for Iran’s geographical conditions¹²⁸. These provinces are grouped into four main climate zones: cold climate, hot and arid climate, hot and humid climate, and temperate and humid climate, which ensures that the sample covers the full climatic diversity of the country.

Table 2 The irrigated wheat AEMSs in the 2021–2022 cropping year based on the fourfold Climatic classification along with sample sizes.

According to the fourfold climatic classification, Iran was divided into four zones whose information is provided in Table 2 in detail. Regarding the climatic classification, Koppen’s classification was used with slight modifications based on the geographical conditions of Iran¹²⁸:

• Temperate and humid climate (southern coasts of the Caspian Sea).

• Cold climate (western mountainous areas).

• Hot and arid climate (central plateau).

• Hot and humid climate (southern coast).

Finally, 10 provinces were selected for the study, and their geographical location in Iran is as follows. The 10 provinces studied are located in Iran and were designed using Geographic Information Systems (GIS10.5) software (https://soft98.ir/software/engineering/3526-arcgis.html) (Fig. 2).

Fig. 2

Study area.

Sampling method and the reliability and validity of data collection instrument

The research sample was taken by a multistage technique. First, some provinces were purposefully selected from each climate. Then, the samples were selected by systematic randomization with proportional allocation. The research instrument was a questionnaire whose validity was checked by a panel of extension experts. According to Table 3, The reliability of the questionnaire was estimated at > 0.9 by Cronbach’s alpha.

Table 3 Cronbach’s alpha for different sections of the research instrument.

It should be noted that this research was conducted during the 2021–2022 cropping year. The research instrument used was a structured questionnaire, designed with input from experts in agricultural extension and climatology to capture both the challenges and potential impacts of AEMSs. The questionnaire was primarily based on a Likert scale with five response levels, ranging from strongly disagree to strongly agree. Cronbach’s alpha was used to test internal consistency, with a value exceeding 0.7, indicating good reliability.

The questionnaire was administered in person at the farmers’ sites, and follow-up visits or phone calls were made in cases of non-response. Any non-responses were treated as missing data, with imputation methods used where necessary to maintain sample validity. Additionally, the sample size was slightly increased to account for potential non-responses.

Data analysis

SPSS software was employed to analyze the data. In the descriptive section, statistical measures such as mean, standard deviation, and coefficient of variation were utilized. Additionally, exploratory factor analysis (EFA) was conducted in this section. EFA is employed when there is no pre-established model for the relationships between scale variables and no assumption that multiple latent factors exist¹²⁹. EFA is a statistical technique used to identify the underlying categories of a set of items. In some studies, researchers first prepare a large number of items to measure the main phenomenon under investigation. These items then need to be categorized systematically. Therefore, exploratory factor analysis aids in identifying different clusters based on the internal correlations between the items. Each cluster comprises a number of elements that are semantically related¹³⁰. The Kaiser-Mayer-Olkin (KMO) coefficient and Bartlett’s Test of Sphericity were used to determine the suitability of the data for factor analysis. Researchers considered a KMO value of 0.7 or higher to be statistically acceptable¹³¹. Varimax rotation was used to extract the factors. Varimax is the most common orthogonal rotation method in factor analysis. The goal of this rotation is to achieve a simple structure by keeping the factor axes orthogonal. Varimax minimizes the complexity of the components by making large loadings larger and small loadings smaller within each component (column)¹³⁰. One of the subtle features of the Varimax method is that squared loadings are used instead of actual loadings. In the Varimax method, negative signs in the columns are removed, and the effects of these negative signs in the variance are eliminated. Varimax is the best method for achieving a simple orthogonal structure¹³¹. The most common criterion for selecting factors in exploratory factor analysis is the Kaiser criterion, also known as the eigenvalue rule. This criterion selects and retains only factors whose eigenvalue is higher than 1¹³². In this study, the criterion for selecting factors was the eigenvalue rule.

Findings and discussion

Based on the results from the frequency distribution of main farmers in the studied irrigated wheat AEMSs, the age group of 41–50 years had the highest frequency of respondents (31.7%). The average age of the respondents was 50 years. In terms of education, the majority (32.2%) had completed middle school. Crop farming provided 96.4% of the livelihood for these farmers, with the remaining relying on gardening, ranching, and other activities. Among them, 71.7% had no secondary jobs, with crop production being their only source of income. The average number of family laborers involved in agriculture was two. The average farm size for irrigated wheat parcels was 15.95 ha. Almost all wheat farms (96.7%) were selected for their agricultural features. AEMSs were operational for an average of two years, handled by one researcher, one extension agent, and three experts. Here, it is crucial to clarify that the term experts refer to individuals who are specialists in specific agricultural techniques or management practices, distinct from “researchers” who are primarily involved in scientific studies and “extension agents” who are responsible for transferring technology to farmers. According to Table 4, These AEMSs averaged 18 subunits. In total, 2139 extension services were provided to main farmers and sub-farmers through these AEMSs.

Table 4 The status of extension activities in the studied provinces.

Prioritization of the challenges of AEMSs

According to Table 5, The main challenges identified in the sites include the mismatch of budget al.location with cropping seasons, a general lack of financial support, and insufficient access to modern equipment and agricultural machinery for service delivery. Building on the above findings, it is important to recognize that, as indicated by previous research, agricultural extension services encounter a variety of challenges that hinder their effectiveness and adaptability. These challenges can be categorized into economic, social, cultural, organizational, and technological dimensions. Hence, Financial limitations have consistently emerged as key obstacles in agricultural extension and development, consistent with findings by Ghasemi and Tavakoli⁸⁵ who highlighted the need for input aids to support farmers in adopting expensive techniques. Therefore, Limited budgets and resources significantly impact the provision of extension services. This inadequacy in equipment leads to a reduction in the motivation of extension professionals^{133,134,135,136,46,47,48}. This is particularly relevant here, as many techniques recommended in AEMSs were found to be unaffordable for farmers, which is a significant limitation for the successful implementation of modern practices. Therefore, addressing these challenges necessitates a multifaceted approach that encompasses economic support, institutional reforms, technological integration, and enhanced collaboration among all stakeholders involved in agricultural extension^12,133,137.

Table 5 The prioritization of the challenges of AEMSs.

Prioritization of the effects of AEMSs

According to the prioritization of the effects of AEMSs in Table 6, the most significant impacts include accelerating technology transfer from extension agents to farmers, improving synergy among extension experts, researchers, and managers, and enhancing farmers’ trust in extension agents through practical solutions that increased productivity. This finding supports previous research, including Molannejad and Yaqubi¹³⁸ and Ghasemi and Tavakoli⁸⁵ which reported significant improvements in farmers’ knowledge levels after participation in extension programs. These results challenge the assumption that stakeholders resist new methodologies, indicating that farmers are receptive to change, particularly when solutions are demonstrated effectively. Additionally, the cost-efficiency and demonstrated success of AEMSs suggest that developing countries could adopt this model to disseminate technical knowledge in rural areas, as also recommended by Shahpasand⁶⁰.

Table 6 The prioritization of the effects of AEMSs.

Factor analysis of the challenges of AEMSs

Factor analysis was employed to identify and categorize the challenges of AEMSs, accounting for the variance in the data. According to Table 7, The Kaiser-Meyer-Olkin (KMO) value was 0.800, with Bartlett’s test yielding a value of 3105.047, which was statistically significant at the 1% level.

Table 7 The KMO value and Bartlett’s test for the challenges of AEMSs.

From the 26 variables identified as challenges, those with factor loadings greater than 0.5 were included in the analysis. According to Table 8, These were categorized into six factors using Kaiser’s criterion with eigenvalues greater than 1.

Table 8 The results of factor analysis for the challenges of AEMSs.

The six factors explained 68.55% of the total variance. The first factor, with an eigenvalue of 9.01, accounted for 34.65% of the variance and was identified as the most important, representing technical-structural challenges. The second factor explained 11.61% of the variance and was classified as planning-related. The third factor, explaining 7.18%, was political-motivational. According to Table 9, The fourth, fifth, and sixth factors financial-credit, agronomic, and cooperation-coordination explained 5.97%, 4.96%, and 4.19% of the variance, respectively.

Table 9 The factors extracted from the factor analysis of the challenges of AEMSs.

As the research results showed, technical-structural challenges were the most important challenges for the sites. Because, numerous factors have historically hindered the efficacy of agricultural extension services, such as constrained institutional capacity and limited outreach¹³⁹. The most influential item in the technical-structural factor was the underutilization of extension agents for teaching extension courses, with a focus on researchers, which emerged as the most significant challenge. In the planning dimension, the mismatch between research findings and regional needs had the highest factor loading. Various studies indicate that the weakness of infrastructure in agricultural extension not only creates problems and challenges for the provision of agricultural extension services but also that the use of technology can significantly improve these services. These technologies help disseminate information more efficiently and address the communication challenges faced by farmers. For example, ICT-based media are employed to inform, educate, and create satisfaction among farmers^{99,140,141,142,143,144}. Hence, it is anticipated that the more intensive use of modern ICT will help improve the performance of agricultural extension^145,146. New digital agro-advisory services encompass a variety of tools, such as SMS-based market information systems, technical farm advice call centers, platforms for farmer-to-farmer technology exchange via participatory videos, and decision support systems accessible through smartphone apps^147,140,148.

In the political-motivational factor, international sanctions that restricted the import of agricultural machinery were the most significant variable. For the financial-credit dimension, the lack of a regular, seasonally tailored budget al.location was the primary issue. Soil fertility differences were the most pressing agronomic challenge, while the lack of coordination among departments hindered the successful implementation of AEMS programs in the cooperation-coordination factor. Overlooking key challenges can cause significant issues for agricultural extension. Many extension models have failed globally, including the demonstration farming model. Although developed in the eighteenth century, demonstration farming wasn’t implemented until the early nineteenth century with the advent of model farms. These initiatives were conducted by various agricultural organizations to improve farming through imitation. Despite the decline in interest by the late nineteenth century, the failure of demonstration farming on model farms provides valuable lessons for today’s practices⁶⁶.

Factor analysis of the effects of AEMSs

Exploratory factor analysis was employed to identify and categorize the effects of AEMSs and determine the amount of variance accounted for. According to the results in Table 10, the KMO value was estimated at 0.843. In addition, Bartlett’s test value for the matrix of data correlation was found to be 2851.736, which was significant at the 1% level.

Table 10 The KMO value and Bartlett’s test for the effects of AEMSs.

According to Table 11, Among the 42 effects identified in the research, 25 variables with factor loadings greater than 0.5 were included in the analysis, categorized into six factors using Kaiser’s criterion with eigenvalues greater than 1.

Table 11 The results of factor analysis for the challenges of AEMSs.

According to Table 12, These factors explained 70.03% of the total variance. The first factor, technical-managerial, accounted for 41.63% of the variance. The second, employment creation, accounted for 7.70%. The remaining factors social, centralism, economic, and knowledge management accounted for smaller portions of the variance. The technical-managerial factor was dominant, indicating that stakeholders are focused on strengthening technical capacities for long-term sustainability, a trend consistent with Djuraeva et al.⁹⁷. The cooperative approach inherent to AEMSs was a key driver in improving technical efficiency, optimizing knowledge transfer, and bridging productivity gaps.

Table 12 The factors extracted from the factor analysis of the effects of AEMSs.

In response to the main research question of how effective AEMSs are in improving agricultural productivity and sustainability in irrigated wheat farming in Iran, the research results indicate that the most influential item within the technical-managerial factor was the enhancement of farm management, which was identified as the most significant effect of AEMSs. The findings of other research are consistent with this study, indicating that farm management can be enhanced by increasing agricultural labor productivity through the provision of agricultural extension services^{52,149,150,95,151,144}. Also, the productivity of various crops is significantly improved through agricultural extension, ultimately contributing to the reduction of the food gap in societies^51,48,45. The second most important factor, employment creation, had its strongest variable in the provision of job opportunities for agriculture graduates. Regarding this research finding, it is also worth mentioning that by enhancing agricultural practices and productivity, extension services play a pivotal role in the economic development of rural areas. This growth fosters the creation of diverse employment opportunities, not only within farming but also in related sectors such as processing, marketing, and distribution^57,136,51. In the social dimension, fostering a collaborative atmosphere among farmers was the most effective variable, while synergy through centralized extension services was most effective in the centralism dimension. These researches indicate that focusing on the social aspects of agricultural extension and promoting equity empowers farmers to combat the injustices linked to industrial food systems. Additionally, it helps to pinpoint the specific contextual factors required for agroecology to flourish^{152,153,154,155}. Safe crop production training was the most important variable in the economic dimension, contributing to food security, consistent with findings from Folke Larsen and Bie Lilleor¹⁵⁶, Ismail & Ahmed⁵¹ and Guo et al.⁵⁸. Lastly, technology transfer from farmers to farmers was the most important factor in the knowledge management dimension. This research finding aligns with the results of Kangmennaang et al.¹⁵², which suggest that investing in community agricultural programs promoting farmer-to-farmer technology exchanges enables farmers to gain insights from extension officers and the experiences of fellow farmers. Additionally, these findings align with Jamil et al.⁵⁴, which indicate that higher education for farmers enhances human capital and that increased experience in agriculture is associated with greater human capital. Therefore, strengthening human resources will improve the effectiveness of agricultural extension.

Conclusion

This study has revealed significant insights regarding the challenges and effects of AEMSs in irrigated wheat farming. The findings indicate that the most pressing issues faced by these sites include the failure to allocate a regular budget tailored to the cropping season, a general shortage of funds, and a lack of modern agricultural machinery. These challenges impede the effective delivery of extension services to both main and sub-farmers. The findings of this study provide practical insights that can be directly applied to improve the extension services in Iran by addressing the specific challenges identified, such as budget al.location, machinery availability, and educational alignment with farmers’ needs. Hence, It encounters obstacles such as an absence of a unified command structure, dilution of efforts due to overburdening extension workers with multiple tasks, extensive operational areas without logistical support, insufficient regular training for extension workers to keep their knowledge current, inadequate research findings relevant to farmers’ field conditions, and overlapping services provided by different agencies^157,158,159.

In terms of the effects of the sites, the results highlight key outcomes such as the acceleration of technology transfer from extension agents to farmers, enhanced agricultural productivity, and an increase in farmers’ trust in extension agents, who provide objective solutions for improving productivity. This trust is crucial and can be fostered through well-structured extension services. The evidence suggests that AEMSs play a vital role in the sustainable development of agriculture, underscoring the need for effective planning that considers both the benefits and challenges of these models. Field experiences from the current research indicated that some farmers perceived that previously conducted extension courses did not align with their educational needs. Planning educational and extension programs that are in sync with market demands and the specific needs of producers can significantly enhance their effectiveness¹⁶⁰. This misalignment points to a common issue in extension services, as seen in studies like Fan et al.⁹⁶ where farmers reported dissatisfaction with the extension system due to untimely and disorganized activities, leading to a loss of trust in extension agents. Our analysis revealed that 40.4% of extension activities were in the form of educational courses. However, given that agriculture is a practical field, it is essential that most extension services focus on practical training rather than theoretical education. The study of the status of irrigated wheat extension services indicated that a significant portion (45.72%) was concentrated on the sowing stage, showing that the extension services do not comprehensively cover the entire crop supply chain. This gap is concerning, as it limits the effectiveness of extension efforts. Moreover, the adoption status revealed that only 51.66% of the extension services were ultimately accepted and implemented by main farmers. This implies that nearly 50% of farmers avoided final acceptance and implementation of the findings for various reasons. This finding aligns with Khoshnodifar¹⁶¹, who noted that while farmers expressed interest in educational courses, they often did not apply the extension recommendations on their farms. Additionally, the results indicated a notable decrease in the seeding rate from 325 kg/ha before the extension period to 207 kg/ha after, reflecting an average reduction of 118 kg/ha. This reduction in seed consumption is economically significant, as supported by Salehi et al.⁸⁴. Similarly, the analysis of water consumption showed a decline from 6175 m³/ha to 5816 m³/ha, marking an average reduction of 359 m³/ha (approximately 6%) attributed to the implementation of strategies provided by extension agents. This reduction is critical given the increasing global and regional water scarcity issues, aligning with findings from Shahpasand¹⁶² and Salehi et al.⁸⁴. Although reducing water consumption was not a primary objective of these sites, in the context of ongoing water crises, it is advisable to align water usage reduction policies with the cooperative extension approach. This integration can contribute to more sustainable practices among farmers. Furthermore, identifying and adapting to the general goals, local capabilities, infrastructure, and expertise of the community will enhance the effectiveness of extension efforts. It is essential to acknowledge that the link between the extension system and agricultural stakeholders has weakened in recent years, primarily due to a mismatch between the services provided and the actual needs of farmers. This research advocates for a thorough needs analysis for each site, as diverse strategies may be required based on specific local conditions. Moreover, since agriculture is an interconnected process, it is crucial not to focus solely on one aspect of production. As evidenced, the sites primarily address certain stages, emphasizing the need for a holistic extension policy that balances the various components of the crop production and supply chain.

The lack of infrastructure and financial resources remains a significant barrier to the effective extension of modern approaches. Insufficient modern machinery and financial support for stakeholders hinder the adoption of innovative methods, which can jeopardize the planning and success of model sites. The historical trend of investment shows that the mere presence of governmental and private-sector resources does not guarantee high efficiency. Thus, fostering collaboration between public and private sectors is imperative, with both sectors working together to provide comprehensive training and extension services to farmers. In conclusion, the existence of significant non-acceptance of model site results among farmers highlights the need for targeted outreach and support measures. Approximately 50% of farmers avoiding the adoption of model site findings suggests that investments in research and development have not yielded the expected returns for half of the agricultural community. Therefore, it is critical to implement advertisement, sociological, psychological, and economic policies alongside extension services to facilitate the acceptance of results. Conducting a thorough needs assessment for site development is essential for optimizing outreach and ensuring the practical application of research findings. Based on the findings of this study, several policy implications emerge that could significantly improve agricultural extension practices. The study highlights the significant impact of Agricultural Extension Model Sites (AEMSs) on agricultural efficiency, emphasizing the need for investment in these sites to enhance irrigated wheat production in Iran. So that, Farmers’ vulnerability to the risks and uncertainties posed by climate change is often exacerbated by a lack of understanding about weather patterns and effective farm management^163,164. Akpotosu et al.¹⁵⁸contended that timely access to relevant information is crucial for carrying out administrative tasks such as planning, organizing, directing, and managing agricultural operations. It is essential for agricultural extension agencies to disseminate technology to farmers to support effective learning and foster social change^165,157.

It identifies six key challenges faced by AEMSs, including technical-structural, planning, political-motivational, financial and credit, agronomic, and cooperation and coordination factors. To address these challenges, policies should foster collaboration among stakeholders, provide financial support and incentives, align educational programs with market demands, and implement robust monitoring and evaluation systems. By addressing these challenges, AEMSs can better support farmers, improve productivity, and promote the adoption of innovative agricultural practices. Finally, in order to improve the performance of AEMS, it is suggested that the following policy recommendations be followed by the Agricultural institute of education and extension as follows:

• 1. Improve physical and human infrastructure.

• Conduct regular training sessions for extension agents to enhance their technical skills.

• Invest in modern agricultural equipment and infrastructure to support AEMS.

• Develop a robust maintenance program to ensure that all equipment remains in good working condition.

• 2. Properly organize extension services.

• Establish a comprehensive planning framework that includes clear objectives and timelines.

• Strengthen collaboration among various stakeholders, including farmers, extension agents, and agricultural researchers.

• Implement a monitoring and evaluation system to assess the effectiveness of extension activities and make necessary adjustments.

• 3. Adapt to political and motivational challenges.

• Support policy reforms that support agricultural extension services and remove bureaucratic barriers.

• Establish incentive programs to encourage extension agents and farmers to actively participate in AEMS.

• Encourage farmers to participate in decision-making processes to strengthen their sense of ownership and commitment.

• 4. Expand financial and credit resources for extension.

• Develop financial support programs, such as grants or low-interest loans, to help farmers invest in modern farming practices.

• Partner with financial institutions to provide farmers with appropriate financial products.

• Train farmers on financial management and the use of credit to increase their financial literacy.

• 5. Implement bias-reduction and ethical strategies.

• Ensure accurate and transparent documentation of extension services provided to farmers.

• Introduce systematic monitoring and evaluation processes to minimize bias and improve data reliability in AEMS.

• Address ethical considerations in periodic evaluation of AEMS performance, maintaining confidentiality, and obtaining informed consent from participants.

By addressing these factors and implementing the suggested practical steps, the Iranian Agricultural Institute of Education and Extension can increase the effectiveness of AEMS, improve agricultural productivity, and promote sustainable development in irrigated wheat cultivation. In light of the findings of this study, it is essential to consider future research directions to address existing gaps. We recommend investigating the application of Agricultural Extension Model Sites (AEMSs) to other crop types, expanding the study to different geographical regions, examining the role of AEMSs in promoting sustainable agricultural practices, and conducting longitudinal studies to evaluate the long-term impacts of AEMSs. These future research directions will contribute to advancing agricultural extension practices and enhancing the overall effectiveness of AEMSs in various agricultural contexts.

Research limitations

It is important to note that all research efforts are subject to limitations. This study faced challenges such as the unavailability of historical data on extension services and a lack of authentic data for comprehensive statistical analysis. Additionally, the absence of related studies on other crops in model sites hindered a comparative assessment of this approach’s effects and challenges across different agricultural contexts. Moreover, the methodology used in this research can be adapted to evaluate other extension models, providing a framework for recognizing their effects and identifying areas for improvement across different agricultural practices. Another limitation that we must acknowledge in this research is that unfortunately, the extension services provided to farmers are not well documented. This limitation means that we as researchers cannot accurately measure the effects of extension services and have to rely on farmers’ memories. Consequently, this reduces the accuracy of measuring the impact of extension services. As a result, the contribution of extension to agricultural development may be overlooked due to these issues. Furthermore, the lack of proper documentation can lead to inconsistencies in data collection, making it difficult to conduct comprehensive analyses and draw valid conclusions. This can also hinder the ability to identify successful practices and areas that need improvement, limiting the potential of extension services to positively impact agricultural productivity and sustainability. Furthermore, relying on farmers’ memories may introduce recall bias, as their memories may not accurately reflect the full extent of services received or their impacts. Addressing this limitation requires improving record-keeping practices and implementing systematic monitoring and evaluation processes to ensure that the actual impacts of extension services are captured and used for future improvements. It is also important to consider possible biases and ethical considerations that could affect our findings. In particular, selection bias could have arisen from the criteria used to select participants, potentially affecting the representativeness of our sample. In addition, interviewer bias may have been introduced through interactions between interviewers and participants and may have influenced the responses collected. Ethical considerations were carefully addressed by obtaining informed consent prior to data collection. These measures were implemented to minimize any ethical concerns and biases that could affect the validity of our findings. By acknowledging these potential biases and ethical considerations, we aim to provide a more comprehensive understanding of the limitations of the study and highlight areas for future research and improvement.