A Systematic Review of Mobile Agricultural Service Applications for Smallholder Farmers in Sub-Saharan Africa: Perspectives from the Technology Acceptance Model

A Systematic Review of Mobile Agricultural Service Applications for Smallholder Farmers in Sub-Saharan Africa: Perspectives from the Technology Acceptance Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review A Systematic Review of Mobile Agricultural Service Applications for Smallholder Farmers in Sub-Saharan Africa: Perspectives from the Technology Acceptance Model Pascal MUROMBA, Minakshi KEENI, Katsuhito FUYUKI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5911289/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Agriculture & Food Security → Version 1 posted You are reading this latest preprint version Abstract The growth in mobile phone use in Sub-Saharan Africa has seen Mobile Agricultural Service Applications (MASA) emerge as a potential solution to address agricultural challenges and enhance the productivity of smallholder farmers in the region. While their potential is undeniable, these technologies often struggle to achieve sustained adoption without external support. In response, researchers have turned to the Technology Acceptance Model (TAM) to better understand the behavioural factors that influence farmers' decisions to adopt such applications in a bid to find solutions and interventions. A key observation is that existing research is scattered and lacks a comprehensive synthesis, making it difficult for stakeholders to grasp the broader behavioural influences on adoption. This study addresses that gap by systematically reviewing empirical studies that apply the Technology Acceptance Model to examine mobile agricultural service adoption among smallholder farmers across Sub-Saharan Africa. Specifically, the study addresses two questions: (1) Which TAM construct perceived usefulness or perceived ease of use exerts a greater influence on smallholder farmers' adoption of mobile agricultural service applications? and (2) What are the key determinants of usefulness and ease of use that shape farmers' adoption decisions of MASA? A total of 14 empirical studies published between 2010 and 2024 were analysed. The findings reveal that perceived usefulness is the more influential factor, with farmers primarily motivated by tangible benefits such as increased productivity, better access to agricultural information, and cost savings. Perceived ease of use is shaped by factors like user-friendliness, simplicity, and access to supportive infrastructure. Additional external influences include education level, income, device type, and network availability. Based on these insights, we propose a contextual framework to guide future design and policy interventions aimed at promoting the sustainable use of mobile agricultural services by smallholder farmers. Mobile Agricultural Service Applications (MASA) Technology Acceptance Model (TAM) Sub-Saharan Africa Smallholder Farmers Figures Figure 1 Figure 2 Introduction Smallholder farmers form the backbone of agricultural production in Sub-Saharan Africa (SSA). Data [1, 2] reveals that an estimated 33 million smallholder farmers constitute 80% of the region's agricultural labour force. These farmers contributing 30–40% to regional Gross Domestic Product (GDP) and representing 65–70% of the total labour force. In many areas, these farmers produce up to 80-90% of the region's food supply. However, despite their fundamental importance for food security and economic stability, smallholder farming productivity remains persistently low. This hampers the region’s ability to meet its food production targets and ensure food security for its growing population. The challenges faced by these farmers are multifaceted and deeply rooted. Scholars [3–5] have identified multiple barriers, including limited market access, inadequate availability of inputs and services, restricted financial services, insecure land tenure, low technology adoption, and the adverse impacts of climate change. Infrastructural deficits, low digital literacy, and limited education compound these hurdles. Together, these factors impede the region's agricultural potential. Digital technology has emerged as a powerful amplifier of agricultural development in SSA. Its impact particularly pronounced when integrated with existing agricultural systems and human capabilities. The region's remarkable digital revolution, driven primarily by mobile phone adoption, illustrates this transformative potential. Between 2002 and 2007, Africa recorded a 49% annual increase in mobile phone subscriptions. This was nearly triple Europe's 17% growth rate during the same period [6]. This momentum has been sustained, with mobile subscriptions expanding dramatically from 16 million in 2000 to 515 million in 2021 [7]. This digital infrastructure has enabled the rise of mobile-based solutions. These tools are key to enhancing existing agricultural practices and institutions. Since the early 2000s, two main categories of mobile applications have evolved to support agricultural development: (1) mobile service applications designed for collecting and transmitting data for economic and social activities [2, 8], and (2) specialized mobile agricultural and rural development applications that provide rural communities with access to information. Researchers Qiang et al. [2] describe them as targeted solutions that provide rural communities with access to information, markets, and services. When integrated with current agricultural practices, these mobile technologies can transform smallholder farmers' capabilities. They help overcome challenges related to market access, information gaps, and financial inclusion [2, 6, 9–11]. In this study, the term Mobile Agricultural Service Applications (MASA) is used broadly to encompass both general mobile service applications and specialized digital tools designed specifically for agriculture and rural development. To enhance readability and reduce repetitive abbreviation, we occasionally refer to them interchangeably as mobile agricultural services, tools, or technologies all of which carry the same meaning. MASAs are widely regarded as key enablers for transforming smallholder farming systems. However, despite their potential, these applications have yet to achieve widespread or sustained adoption at scale. This is evident in both practitioner-oriented reviews [12, 13] and academic studies [14–16]. Early studies [17–20] attributed this gap to design-user mismatches, but more recent research points to the persistence of these challenges in more complex forms. For example, a 2022 Kenyan study by [21] found that even among farmers with growing digital awareness and smartphone ownership, adoption was hindered by poor service discoverability, weak value propositions, and limited digital literacy. These factors slow uptake of MASAs. They also diminish the perceived relevance of the tools in daily farming decisions. Broader scholarly work [22] further highlights structural constraints including inadequate Information and Communication Technology (ICT) infrastructure, high data costs, and fragmented digital support systems that continue to disproportionately affect rural users. In response, various interventions have been introduced to address these barriers. These include digital extension services, farmer training programs, and low-tech interfaces such as Unstructured Supplementary Service Data (USSD ) and Interactive Voice Response (IVR). Yet, their effectiveness remains mixed: while some initiatives have succeeded in raising awareness and short-term use, few have led to long-term, self-sustained adoption. These ongoing challenges underscore that technical fixes alone are insufficient and point to the need for a deeper, user-centered understanding of the behavioral factors shaping MASA uptake. Although a number of studies [23–36] have examined these issues using the Technology Acceptance Model (TAM), most remain context-specific and fragmented. As a result, there is limited cross-cutting insight into how smallholder farmers in Sub-Saharan Africa make decisions about adopting MASAs. Addressing this gap, the present review applies Davis's Technology Acceptance Model [37], as a behavioral lens to systematically synthesize existing evidence and identify the key factors influencing MASA adoption in the region. TAM's focus on perceived usefulness (PU) and perceived ease of use (PEOU) makes it particularly relevant for examining MASA adoption, as these core constructs directly address the challenges identified in prior research. Note that throughout this work, we may refer to PU simply as "usefulness" and PEOU as "ease of use". TAM provides a foundational framework. However, researchers applying it to MASA adoption in SSA have recognized the need for contextual adaptation. They have frequently modified the original model by incorporating additional variables such as socioeconomic factors, perceived risk, and cultural considerations, arguing that these enhancements better capture the unique regional adoption dynamics. They argue that this contextual adaptation of TAM provides a more nuanced understanding of technology acceptance in the SSA agricultural context. This review conducts a systematic analysis of empirical studies applying TAM to MASA adoption, including both original and modified versions of the model that met established inclusion criteria. Given the prevalence of contextual modifications in this research domain, studies were included regardless of whether they employed the original TAM or adapted versions, provided they maintained the core TAM constructs of perceived usefulness and perceived ease of use. The aim is to identify the factors that influence adoption decisions among smallholder farmers in SSA. By doing so, it bridges the gap in understanding between technological capabilities and user design needs. Two central research questions guide this investigation: (1) Which TAM construct perceived usefulness or perceived ease of use exerts a greater influence on smallholder farmers' adoption of mobile agricultural service applications? (2) What are the key determinants of usefulness and ease of use that shape farmers' adoption decisions of MASA? While there has been growing research on technology adoption in Sub-Saharan Africa, few studies have specifically examined the use of mobile agricultural service applications through the lens of the Technology Acceptance Model. Existing evidence is limited, fragmented, and often context-specific, making it difficult to draw broader conclusions about adoption behaviour across the region. This review addresses that gap by offering the first comprehensive synthesis of behavioural factors influencing MASA adoption among smallholder farmers in SSA. Our systematic search identified just 14 empirical studies published between 2010 and 2024 that applied TAM constructs to this context highlighting a significant gap in the literature. This study makes two key contributions. First, it evaluates how perceived usefulness and perceived ease of use influence smallholder farmers’ adoption of mobile agricultural applications. It clarifies which TAM construct more strongly drives adoption in this context. Second, it systematically identifies and analyzes the key determinants underlying both perceived usefulness and ease of use across all available empirical studies. This provides a comprehensive synthesis that has been absent from the literature. By synthesizing findings from the limited but relevant empirical studies [23–36], this review establishes a foundation for improving user-centric design approaches. It offers developers, extension services, and policymakers evidence-based insights to enhance mobile agricultural application design and implementation strategies. Furthermore, this study conceptualizes a tailored TAM framework for MASA adoption, which we believe will serve as a valuable tool for understanding user behavior in this context. This research not only addresses a critical gap in the literature but also provides a foundation for future studies to build upon, particularly in exploring additional factors that may influence MASA adoption in SSA. Theoretical Framework - Technology Acceptance Model (TAM) This study employs the original Technology Acceptance Model [37] as the optimal framework. Its proven ability to capture adoption behaviours while avoiding unnecessary complexity is a critical consideration when studying emerging technologies. The model is widely recognized for its simplicity, generalizability, and foundational relevance in explaining technology adoption behaviours across diverse settings [38, 39]. Its parsimonious structure enables a focused analysis of the core adoption constructs: usefulness and ease of use. This makes TAM especially appropriate for studying emerging technologies in resource-constrained environments, where overly complex models may not be practical. Several extended models incorporate additional variables such as subjective norms, voluntariness, social norms, habits, and experience. These include enhanced versions with social influence factors [38], the Unified Theory of Acceptance and Use of Technology [40] , and advanced iterations with additional cognitive constructs [41] . However, these models are often ad hoc and tailored to specific organizational or consumer environments. Empirical evidence [42] suggests that these later theoretical developments do not necessarily improve explanatory power. In some cases, they even reduce the original framework's effectiveness. Given these considerations, TAM was deliberately chosen over its successors for its balance of theoretical robustness and practical applicability. Figure 1 presents the adapted TAM model, reflecting MASA adoption in smallholder farming in SSA. At its core, this theoretical framework conceptualizes technology acceptance as a systematic three-stage process. External factors influence cognitive responses, which then shape user attitudes toward technology use. These attitudes ultimately lead to behavioural intentions and actual usage patterns [37, 43]. The model focuses on two key constructs: PU and PEOU. These constructs are equally significant in determining users' behavioural intentions toward technology adoption, operating synergistically to highlight their interdependent roles in the acceptance process. Perceived usefulness (PU) is defined as the degree to which a person believes that using a particular system would enhance their job performance [37]. It reflects users' assessments of a technology's potential to improve operational efficiency and effectiveness. The construct suggests that for optimal utility, users must perceive a strong correlation between system use and performance improvement [37, 38]. Perceived Ease of Use (PEOU) is defined as the degree to which a person believes that using a particular system would be effortless [37]. It is a critical dimension in technology adoption. The construct indicates that user acceptance is heavily influenced by system accessibility, as even highly useful technologies may face resistance if perceived as overly complex. The construct operates through two primary mechanisms: self-efficacy and instrumentality. Self-efficacy , refers to an individual's self-assessed ability to perform tasks successfully. This construct suggests that improved system usability is positively correlated with users' perceived competence, establishing a direct relationship between ease-of-use perceptions and individual confidence [44]. This increased sense of capability fosters intrinsic motivation, which plays a key role in developing positive attitudes toward technology adoption. Instrumentality refers to the practical benefits resulting from improved usability. The construct suggests that systems with high ease of use reduce cognitive effort. This allows users to allocate mental resources more efficiently across tasks, thereby improving overall operational efficiency [37]. As a result, PEOU not only boosts user confidence but also leads to broader improvements in task management and operational effectiveness. Methodology This study employs a systematic literature review (SLR) methodology based on the framework proposed by Kitchenham et al. [45]. It is tailored to the context of MASA, as illustrated in Figure 2. Drawing on the approach of Ayim et al. [46], the review begins with the formulation of two research questions to define the scope of the study. A preliminary literature search was conducted to align the search strategy with the research questions. This step also helped refine the search parameters. Inclusion and exclusion criteria were systematically developed to identify relevant studies. A quality assessment checklist was then used to evaluate methodological rigor, relevance, and reliability. Pilot data extraction was performed on a sample of studies to ensure consistency in data collection, followed by robust synthesis methods to analyze findings and derive insights addressing the research questions. [Figure 2 will be inserted here ] Search strategy The search strategy was structured around three components. These included scope, methods, and search string construction. The scope was limited to literature published between 2010 and 2024. This reflects the absence of studies applying TAM to mobile agricultural services in SSA prior to 2010. Both web-based and database-driven methods were used to ensure comprehensive coverage. Searches were conducted in the academic databases Web of Science (n = 28), Scopus (n = 32), and ScienceDirect (n = 15), and were complemented by additional searches in Google Scholar (n = 14) to capture further relevant studies. The search strings were designed using keywords, abbreviations, and synonyms derived from the research questions. Examples include '(Technology Acceptance Model) AND (mobile phone) AND (smallholder farming) AND (Africa)' and variations such as '(Technology Acceptance Model) AND (adoption) AND (behaviour) AND (mobile) AND (farming).' For Google Scholar, terms like 'm-services,' 'agriculture,' and 'TAM alternatives' were added to retrieve more relevant studies. Study Selection Criteria Systematic exclusion criteria were applied to ensure alignment with the research questions. An initial pool of 89 studies was screened based on titles and abstracts, followed by full-text reviews. Studies were excluded if they were not in English, if full-text was unavailable, or if they were unrelated to Sub-Saharan Africa. Additional exclusions were applied to studies that did not use TAM as a conceptual framework, analytical tool, or methodology. Studies that failed to address TAM in the context of mobile agricultural services in SSA were also excluded. Papers from non-indexed journals were excluded. Studies with unclear findings were also removed. This process resulted in 14 studies that met the inclusion criteria. The 14 selected studies were drawn from Scopus (n = 5), Web of Science (n = 6), and Google Scholar (n = 3). They were published in the following journals: Cogent Social Sciences [33, 34], Gender, Technology and Development [25], Information Development, African Journal of Agricultural Research [35] , Ghana Journal of Agricultural Science [27], African Journal of Science, Technology, Innovation and Development [28], Electronic Journal of Information Systems in Developing Countries [23], MDPI Agriculture [29], Journal of Strategy and Management [30], Library Philosophy and Practice (e-journal) [31], Technological Forecasting and Social Change [32], Technological Sustainability [36], and Agriculture and Food Security [24] . Quality Assessment Criteria The selected studies were evaluated using a quality assessment framework adapted from previous works [45, 46]. Four dimensions were assessed: reporting quality, methodological rigor, relevance, and credibility. Reporting quality examined the clarity and coherence of the studies, while methodological rigor focused on the validity of their approaches. Relevance assessed how well the studies addressed their objectives, and credibility evaluated the consistency of their findings. Each dimension was scored on a three-point scale: 0 (no), 0.5 (partial), and 1 (yes), to ensure objectivity. Data Extraction Data from the 14 studies were extracted and categorized into seven areas. These included the country of study, citation details, specific mobile agricultural services examined, influential TAM constructs, methodological approach, and the determinants of PU and PEOU. This structured approach facilitated systematic data analysis and organization into a table. Data Synthesis The objective of the data synthesis was to organize and interpret findings from the 14 studies to address the research questions. A mixed-methods approach was adopted, integrating quantitative and qualitative techniques for comprehensive analysis. The quantitative analysis began with a frequency count to identify the most recurring TAM constructs in the studies. The determinants were grouped based on their impact. One group influenced perceived usefulness, while the other affected perceived ease of use. These determinants were further clustered by linguistic and conceptual similarity for accurate categorization, followed by descriptive statistical analysis to quantify the geographical distribution of studies and the prevalence of determinants. Key findings were organized into a comprehensive table summarizing essential dimensions: citation details, country context, methodological approaches, specific mobile agricultural services examined, influential TAM constructs, and determinants of these two key constructs. The qualitative analysis built upon these structured data to explore patterns and relationships among determinants and their influence on mobile service adoption within this theoretical framework. Insights were drawn into how these determinants varied across contexts. The analysis also explored how frequently specific constructs appeared and how they interrelated to explain adoption behaviour. This dual approach enabled a holistic understanding of the literature and provided evidence-based answers to the research questions. Results and Discussion Overview of Included Studies This paper reviewed 14 empirical studies that employed the TAM as their conceptual framework to explore factors influencing the adoption decisions of MASA among smallholder farmers in sub-Saharan Africa. Of the 14 studies, 70% used quantitative methods. The remaining 30% employed mixed methods. Geographically, the majority of the studies were conducted in Nigeria (n=4) [23, 24, 30, 35], followed by South Africa (n=3) [29, 33, 34], Ghana (n=2) [27, 28, 31], and one study each in Ethiopia [28], Benin [26], Tanzania [36], Uganda [32], and a combined study in Malawi and Zambia [25]. The analysis revealed that perceived usefulness was the more dominant construct, cited in 57% of the studies (8/14). In comparison, perceived ease of use was dominant in 43% (6/14). Key determinants of perceived usefulness included increased productivity (57%, n=8), better access to information and inputs (50%, n=7), and cost savings (43%, n=6). For perceived ease of use, the primary factors were user-friendliness (50%, n=7), low effort requirements (43%, n=6), and accessibility (43%, n=6). External factors also significantly influenced technology acceptance constructs, with education (57%, n=8) having the greatest impact, followed by income (50%, n=7), type of device owned (43%, n=6), and network access (36%, n=5). In terms of technologies, mobile phones as standalone devices were the most studied (n=7) [26, 28, 31, 32, 34–36], while the remaining studies focused on mobile-enabled applications and services such as digital agricultural applications [30], e-agriculture services [27], smartphone agriculture applications [24, 33], social media platforms [33] , climate-smart agriculture tools [25], and e-wallet services [23]. Influence of TAM Constructs on MASA Adoption Our literature review focused on empirical studies that used the TAM as a conceptual framework to evaluate issues surrounding the adoption decisions of MASA among smallholder farmers in SSA. Using the 14 papers [23–36] included in our systematic review, we aimed to answer a key question: Which construct perceived usefulness or perceived ease of use has a greater influence on smallholder farmers' adoption of mobile and smartphone agriculture? The reviewed studies revealed that PU is a more influential factor than PEOU in driving MASA adoption among smallholder farmers. Specifically, 57% of the studies [23, 25, 27–30, 35, 36] identified PU as the primary factor influencing farmers' decisions to adopt MASA. While numerous determinants of usefulness emerged from the studies (which we discuss in detail later), the results suggest that smallholder farmers in sub-Saharan Africa prioritize tangible outcomes. These include increased productivity, improved access to agricultural information, and cost-effectiveness when assessing the usefulness of mobile agriculture technologies. These practical benefits significantly shape their adoption behaviour. Although both perceived use and perceived ease of use hold equal theoretical weight in the TAM, our review found that while ease of use is important, it is not the main reason farmers adopt MASA. Instead, ease of use supports adoption by making the technologies more accessible. This allows farmers to experience their practical benefits, such as higher productivity or better access to agricultural information. This finding aligns with later extensions of the TAM model, which emphasize ease of use as a facilitator rather than a primary driver of adoption. The determinants of the perceived usefulness of mobile agricultural technologies varied across the nine countries and 14 empirical studies reviewed (see Appendix). Despite these variations, we identified three key determinants that consistently emerged as central to farmers’ evaluations of MASA’s usefulness: productivity enhancement, information access, and cost-effectiveness. Productivity enhancement was the most frequently cited determinant, appearing in approximately 60% of the studies (see Appendix). This high frequency reflects a general pattern highlighted in the studies, where farmers prioritized MASA technologies that demonstrated the ability to reduce operational inefficiencies and increase profitability. For instance, the studies highlighted that farmers particularly valued MASA when it provided timely agricultural information. This included planting schedules and pest management advice, which enabled better decision-making. Additionally, farmers valued MASA technologies when they facilitated market access by connecting them with buyers and streamlining post-harvest processes, further enhancing their perceived usefulness. Similarly, information access emerged as the second most significant determinant, highlighted in 50% of the studies (see Appendix). According to the studies, the prevalence of this determinant can be explained by farmers’ tendency to value technologies that bridge knowledge gaps and support informed decision-making in areas such as crop management, pest control, and market access. Across the studies, access to timely and accurate information such as weather forecasts, soil health data, and pricing trends was consistently cited as a critical factor enhancing usefulness. However, the specific types of information prioritized varied by region, reflecting localized agricultural challenges and needs. Cost-effectiveness was the third key determinant, documented in 40% of the studies (see Appendix). As evidenced by the studies, the recurring emphasis on cost-effectiveness stemmed from a general pattern in which farmers were more likely to adopt mobile agricultural services when it offered a favourable benefit-cost ratio, ensuring that the benefits outweighed the associated costs. While the specific cost-related concerns differed across studies ranging from data usage and subscription fees to initial purchase requirements the overarching focus on cost-effectiveness remained a consistent theme. Farmers were more likely to adopt mobile agricultural tools when they perceived its benefits such as yield enhancement and labour optimization to outweigh the associated costs. Our literature review of 14 empirical studies using the technology acceptance model as a conceptual framework to evaluate issues surrounding adoption decisions of MASA among smallholder farmers in sub-Saharan Africa revealed that usefulness is a stronger driver of adoption than ease of use in the region. Specifically, 57% of studies identified usefulness as the primary factor. Farmers prioritize practical benefits such as productivity enhancement, access to agricultural information, and cost-effectiveness when evaluating mobile agricultural technologies, with productivity enhancement being the most frequently cited determinant (60% of studies). While perceived ease of use plays a supporting role by making technologies more accessible, it is the tangible outcomes such as higher yields, better decision making, and favourable cost-benefit ratios that primarily shape farmers' adoption decisions. Key determinants of Perceived Usefulness (PU) Productivity enhancement Analysis of the empirical studies presented in Appendix identifies productivity enhancement as a crucial determinant of perceived usefulness in mobile agricultural service adoption decisions among Sub-Saharan African smallholder farmers, with 60% of studies confirming this relationship. The empirical studies converge on a key finding: mobile agricuture technologies show varying degrees of success as catalysts for improving farm productivity, often helping farmers manage their operations more efficiently and, in many cases, increase their harvest yields. Farmers who achieved tangible benefits from using MASA particularly through increased yields and more efficient farming processes were more likely to adopt these technologies. Studies conducted in Ethiopia and Tanzania [28, 36] revealed that farmers who found mobile agricultural services useful primarily valued their ability to provide timely agricultural information, enabling them to make better-informed decisions about planting schedules and pest management. As a result of these practical benefits derived from using mobile agricultural services, these farmers reported positive experiences and showed increased willingness to adopt these technologies. Other investigations [25, 30] revealed a positive inclination toward mobile agricuture technologies among farmers who successfully used it to access agricultural information such as weather forecasts, pest management tools, and agronomic advisory services. This information enabled them to improve their crop yields and reduce agricultural uncertainties. In these studies, mobile agricuture technologies ability to provide information facilitated better farm planning and improved productivity, triggering favorable perceptions of the technologies. Beyond information services, studies also revealed MASA's significant role in facilitating market access through buyer connections and streamlined post-harvest processes [23, 36]. Among farmers who realized these benefits, MASA was viewed as useful and worth adopting. Notably, the reviewed studies highlighted the role that external variables play in mediating the usefuless of mobile agricuture technologies. When factors such as education levels, household resources, and access to technology infrastructure [24, 30] were high, they directly influenced the success of farmers in using MASA. These conditions enabled the effective utilization of mobile agricuture technologies, leading to enhanced efficiency and productivity gains, thereby fostering positive perceptions and higher adoption rates. Consistent with results from the reviewed studies, evidence from other geographical contexts corroborates these findings. In Bangladesh, a study [47]that asked rice farmers to rank mobile phone productivity perceptions on a 7-point scale found agricultural information ranked second. Farmers viewed mobile phones as productivity enhancers when they provided actionable information from extension officers, enabled crop disease identification, and facilitated remote training and meetings. Overall, evidence from both our reviewed studies and those in other geographic contexts indicates that when farmers realize tangible efficiency and productivity gains enabled by using MASA in their farming operations, they develop a positive inclination toward the technologies and are more likely to adopt them. Supportive external factors, such as education and resources, further enhance the perceived usefulness of MASA. Access to Information and Inputs Results from 50% of the reviewed studies (Appendix) demonstrate that access to information and inputs significantly influences MASA's perceived usefulness among smallholder farmers. Two studies [27, 36] found that smallholder farmers primarily adopted these technologies to address agricultural knowledge gaps, particularly regarding pest management, market prices, and soil conditions. Farmers who successfully used MASA to address their information needs developed a positive association with these technologies, displaying stronger adoption intentions compared to those who had not effectively used the technologies to bridge their knowledge gaps. An empirical study from Nigeria [30] examining adoption patterns for two applications a herbicide calculator and an agronomy application tool found that adoption rates positively correlated with the applications` ability to support timely, informed farm-level decision-making. Specifically, farmers who viewed these applications as reliable channels for accessing decision-critical information showed a greater inclination to adopt the technologies. These findings align with a review study by Aparo et al. [48] on mobile phone adoption patterns, which emphasized that farmers' technology adoption decisions were primarily influenced by their perception of the tools' utility in facilitating timely, informed farming decisions. Regarding input access, two studies [23, 34] found that farmers developed positive utility perceptions of mobile agricultural services when they experienced enhanced supplier communication and procurement efficiency. The technologies' ability to streamline what would otherwise be lengthy and challenging input acquisition processes through traditional channels led farmers to view them as particularly useful. Overall, in cases where mobile agricultural services favorably facilitated timely agricultural knowledge dissemination and improved connections with input suppliers, farmers were able to make timely decisions and secure inputs efficiently. Among these farmers, experiencing this ability to overcome information and input access challenges translated into positive perceptions of the technologies' usefulness. Cost-effectiveness A synthesis of findings from empirical studies revealed that 40% of the reviewed literature (Appendix) highlighted cost-effectiveness as a key determinant shaping the perceived usefulness of MASA among smallholder farmers in sub-Saharan Africa. Empirical studies conducted in Nigeria and South Africa [30, 33] showed that farmers' MASA adoption decisions were predominantly based on an evaluation of benefits relative to costs. These costs encompassed various expenses, including data usage, subscription fees, and initial purchase requirements. Following this cost-benefit analysis, farmers who identified favorable benefit-to-cost ratios exhibited positive attitudes toward MASA adoption, ultimately integrating these technologies into their operations. Additional research [28] found that cost considerations significantly influenced MASA adoption in low-income agricultural regions. The researchers found that resource-constrained farmers methodically evaluated monetary expenditures against potential benefits within their financial limitations.. The perceived usefulness of MASA and subsequent adoption were primarily observed among farmers who concluded that the potential benefits justified the investment of their limited financial resources. Further evidence from the literature [25, 29] indicated that applications delivering tangible benefits in yield enhancement and labor optimization, while maintaining low operational costs, generated stronger farmer engagement. This pattern consistently demonstrated that positive adoption inclinations were most prevalent among farmers who perceived mobile agricuture technologies' utility to outweigh the associated costs. These observed cost sensitivities and analytical approaches to determining MASA usefulness are consistent with findings by [3], whose work documented that income constraints and dependence on non-cash income are predominant characteristics among sub-Saharan Africa smallholder farmers. These economic conditions predispose farmers to carefully scrutinize technology-related investments. However, cost-benefit assessments of mobile agricultural technologies do not yield uniform results across all contexts. Contrary to findings from the papers we reviewed, a study in India [49] found that despite farmers having access to mobile phones and internet, traditional methods like peer-to-peer communication and mass media remained more cost-effective and convenient for accessing agricultural information. This highlights that cost-benefit assessments of mobile phone adoption for agricultural information vary significantly across regions. The synthesis of empirical studies reveals that for resource-limited smallholder farmers, MASA adoption decisions are ultimately determined by whether the anticipated agricultural operational benefits justify the required financial investment in the technologies. However, this cost-benefit calculus varies considerably across different geographical and cultural contexts, suggesting that the perceived advantages of mobile agricultural technologies over traditional information channels are not universal but rather context-dependent. Key determinants of Perceived Ease of Use (PEOU) User-Friendliness User-friendliness emerged as a key determinant of ease of use, with about 50% of the studies highlighting this aspect as crucial when evaluating the ease of use of mobile agricultural technologies in SSA. Across the reviewed studies, a friendly interface was typically characterized by features that reduce cognitive load and operational complexity for the user. These include intuitive navigation, voice-guided instructions, pictorial interfaces, and support for local languages. Such design elements make the technology more accessible and less intimidating, especially for users with limited formal education or digital experience. Two studies [24, 32] revealed that demographic characteristics, specifically age and education level, influenced farmers' perceptions of the ease of use of mobile agricultural technologies. In these studies, farmers with an average age of 50 years were found to have relatively low levels of formal education and demonstrated strong preferences for technologies requiring minimal training and straightforward interfaces. In contrast, younger farmers, with an average age of 40 years and higher levels of formal education, prioritized functionality over simplicity. These findings suggest that educational attainment plays a significant role in shaping how different age groups of farmers in sub-Saharan Africa assess the usability of technology. Two investigations [30, 31] found that MASA adoption rates increased when these technologies incorporated accessibility features such as voice-guided instructions, pictorial interfaces, and local language support. These design elements enhanced both operational simplicity and practical utility. These findings align with broader research on mobile technology design in developing regions [19], which documented that in areas characterized by lower literacy rates, users demonstrate stronger engagement with technologies that balance simplicity with utility while minimizing user anxiety. The empirical evidence demonstrates that educational attainment and age function as significant behavioral moderators in determining mobile agricultural technology usability perceptions among distinct farmer demographics in sub-Saharan Africa. Furthermore, the analyses reveal that interface simplicity emerges as a central evaluative criterion through which farmers assess technological ease of use, ultimately influencing their behavioral disposition toward MASA adoption across the region Low Effort Requirement In 40% of the empirical studies, we reviewed (Appendix), smallholder farmers in SSA perceived minimal required effort as a key determinant in assessing mobile agricultural technologies as easy to use, which influenced their sustained adoption decisions. We observed in our review, Based on our review, the notion of “effort” encompasses physical, mental (cognitive), and emotional dimensions, with the dominant type varying across user contexts. In two of the reviewed studies [25, 34], farmers facing high workloads and limited time for acquiring new skills favored solutions that reduced physical labor and cognitive demands. For instance, a South African study[25] evealed that climate-smart digital tools requiring minimal additional physical work or mental processing were particularly beneficial to women farmers. These women, often burdened by domestic labor and constrained by patriarchal norms, expressed a need for technologies that minimize time and energy demands both physical (e.g reduced manual input) and mental (e.g low learning burden).These findings align with broader research [50] on women's agricultural productivity in SSA. That research shows that women's productivity is hampered by multiple domestic responsibilities such as childcare, cooking, water collection, and laundry which collectively cause physical fatigue, time scarcity, and emotional stress. These burdens leave little capacity for engaging with complex or time-intensive innovations. When these technologies reduce such burdens, they are perceived as “low effort” and are more readily adopted. While women's preference for low-effort technologies is primarily shaped by time poverty and domestic overload, studies like [24] suggest that male farmers also value ease of use, though driven more by practical efficiency seeking tools that save physical effort or streamline workflows. This underscores that while both genders seek low-effort solutions, the types of effort they aim to reduce and the reasons for doing so differ, reflecting broader structural and social dynamics in SSA farming communities. A study [51] examining behavioural intention to use mobile phone-accessible e-textbooks in Iran's agricultural sector provides additional support for our review findings. It showed that users favoured e-textbooks designed with intuitive navigation and self-explanatory features, which minimised cognitive effort and enabled independent learning. This demonstrates that the relationship between low-effort design and perceived ease of use extends beyond SSA contexts, indicating that users consistently favour technologies that reduce cognitive and operational demands, regardless of geographical or cultural setting. Accessibility Accessibility, encompassing both infrastructure and affordability, emerged as a significant determinant of ease of use in 40% of the analyzed studies (Figure 1). The evidence reveals two distinct but interconnected pathways through which accessibility influences perceived ease of use. This twofold relationship manifests in several ways. First, device accessibility directly shapes user perceptions. Studies by [30, 32] demonstrated that farmers with access to durable, low-cost mobile devices consistently reported higher perceived ease of use. This established a clear link between device affordability and technology acceptance. This relationship is particularly pronounced among resource-constrained smallholder farmers, where device cost represents a primary barrier to adoption. Second, infrastructure accessibility creates the enabling environment for sustained use. Research by [26, 34] provided compelling evidence that reliable network coverage and robust user-support infrastructure significantly enhance farmers' perceptions of mobile agricultural technologies. These factors make the tools seem more accessible and easier to use. These studies showed that even well designed technologies are perceived as difficult to use when infrastructure support is inadequate. The evidence further indicates that accessibility barriers compound rather than operate independently. Studies by [27, 31] demonstrated that training programs and local support services reduce perceived complexity. They do so by simultaneously addressing multiple accessibility dimensions, such as device familiarity, infrastructure knowledge, and ongoing support. This multidimensional nature of accessibility is reinforced by evidence from other regions. A study [51] on mobile phone-accessible e-textbooks in Iran's agricultural sector found content quality to be the strongest predictor of adoption. Specifically, update frequency and understandability influenced both behavioural intention and perceived ease of use. The convergence of findings across SSA and non-SSA contexts reveals that accessibility operates through context-specific but theoretically consistent mechanisms. While SSA studies emphasize physical infrastructure and device affordability, other regions prioritize content accessibility and quality. This suggests accessibility functions as an overarching concept that consistently influences ease of use by reducing cognitive and operational effort, regardless of context-specific manifestations. Notably, given that the broader agricultural technology adoption literature identifies social and cultural influence, government support, and access to technical assistance as important drivers [36, 52–55], we anticipated these institutional and contextual factors would feature prominently in the 14 studies included in our review. Contrary to our expectations, institutional and contextual factors like social influence or government support were either absent or appeared only occasionally in the studies. Social and cultural influence was associated with ease of use in just two studies [26, 35], while technical support was linked to perceived usefulness in only two others [27, 34], suggesting that these institutional and contextual factors are not being adequately captured within current TAM applications to MASA in Sub-Saharan Africa studies. One explanation we offer for this gap aligns with findings by [32]. These suggest that the original TAM does not capture the nuanced institutional contexts of Sub-Saharan Africa that shape farmer perceptions. For example, farmers may not expect digital support, while extension systems under-invest in digital tools. This reciprocal relationship represents an institutional dynamic that TAM's cognitive focus may overlook. Farmers who have long relied on traditional support may not expect similar structures for mobile tools. At the same time, under-resourced extension systems tend to focus on established methods that reach more people [55]. This could create a mutually reinforcing dynamic where mobile agricultural platforms are viewed as optional add-ons rather than essential tools requiring institutional support. The limited attention to institutional enablers in current MASA studies may therefore reflect that without extension to Sub-Saharan African contexts, TAM captures only individual-level cognitive factors, potentially overlooking how these established institutional determinants manifest in the region. Limitations of the Study This review aimed to answer two key questions: (1) Which TAM construct perceived usefulness or perceived ease of use exerts greater influence on smallholder farmers' adoption of MASA? And (2) what are the key determinants shaping these perceptions? However, several limitations should be noted. Although the synthesis offers valuable insights across 14 empirical studies, it did not independently analyse the heterogeneity of study contexts. These include differences in timeframes, cultural settings, and farmers’ exposure to technology. These contextual variables undoubtedly shape farmers’ perceptions and adoption behaviors. However, they were beyond the scope of this review, which focused on cross-study commonalities over intra-study contrasts. Second, the temporal range of included studies (2010–2024) spans a period of significant technological evolution in SSA, during which access to smartphones, mobile data, and digital literacy has changed considerably. While we noted the publication year of each study, we did not conduct a longitudinal or temporal comparison of how determinant importance may have shifted over time. Third, although cultural and infrastructural differences (such as network coverage or local norms) were occasionally discussed within individual studies, our review synthesized findings thematically rather than stratifying them by cultural region or technological maturity level. As such, some context-specific nuances may have been diluted in pursuit of broader conceptual convergence. Future research could build on our findings by conducting meta-analyses or comparative reviews that explicitly examine temporal and cultural variation, or by integrating studies using alternative conceptual frameworks to capture dimensions not addressed by TAM. Conclusion In this study, we conducted a systematic review of the literature using the Technology Acceptance Model as a conceptual framework to evaluate the adoption decisions of mobile agricultural services and applications among smallholder farmers in sub-Saharan Africa. By analyzing 14 empirical studies published between 2010 and 2024, we contributed to the limited research on the behavioral influences shaping technology uptake in this context. Our analysis revealed that perceived usefulness emerged as the most influential factor guiding farmers’ decisions. They tend to prioritize tangible benefits, such as increased productivity, better access to agricultural information, and overall cost-effectiveness, when judging whether to adopt these tools. In terms of ease of use, user-friendliness, minimal effort required, and easy access were the key determinants of positive perceptions. Additionally, external variables including education level, income, device type, and the availability of network infrastructure significantly shaped how farmers assessed both usefulness and ease of use. These findings offer practical insights for technology developers and development practitioners, enabling them to design solutions that better align with the needs and preferences of smallholder farmers across the region. Based on our results, we conceptualized an adapted framework tailored to the adoption of mobile agricultural technologies (see Fig. 1 ), which we believe will serve as a valuable tool for understanding user behavior in this setting. During our review, we observed that recent studies on technology uptake among smallholder farmers have increasingly turned to extended acceptance models to capture a wider range of influencing factors. Many of these studies highlight limitations in the original framework’s ability to fully explain adoption behavior in agricultural contexts. Despite these challenges, our research focused on synthesizing evidence grounded in the initial version, addressing a gap that had not yet been explored. Future studies could empirically test the adapted framework in diverse settings, use mixed methods to explore context-specific factors, and consider integrating elements from extended models. Longitudinal and comparative studies across countries, crops, or technology types could further refine the framework and support the design of more effective, user-centered mobile agricultural tools. Abbreviations MASA : Mobile Agricultural Service Applications TAM : Technology Acceptance Model SSA : Sub-Saharan Africa UTAUT : Unified Theory of Acceptance and Use of Technology PU : Perceived Usefulness P EOU : Perceived Ease of Use ICT : Information and Communication Technology USSD: Unstructured Supplementary Service Data IVR : Interactive Voice Response Declarations Acknowledgements Not applicable Author`s contributions 1* was responsible for all aspects of manuscript writing. 2 and 3 contributed by reviewing and revising all aspects of the research. All authors read and approved the final manuscript. Funding No funding was received for this research Availability of data and materials All data has been included in the manuscript Declarations Ethics approval and consent to participate Not applicable Consent for Publication All authors consent for Publication at Agriculture & Food Security and agree to BMC’s conditions of submission, copyright and license agreement. Competing interests All authors declare that they have no conflict of interest Author details All authors are affiliated with the Graduate School of Agricultural Science, Tohoku University, Japan. References Wiggins S, Keats S (2013) LEAPING & LEARNING LINKING SMALLHOLDERS TO MARKETS. Qiang CZ, Chew Kuek S, Dymond A, Esselaar S (2012) Mobile Applications for Agriculture and Rural Development. Jayne TS, Mather D, Mghenyi E (2010) Principal Challenges Confronting Smallholder Agriculture in Sub-Saharan Africa. World Dev 38:1384–1398 Collier P, Dercon S (2014) African Agriculture in 50 Years: Smallholders in a Rapidly Changing World? World Dev 63:92–101 Aker JC (2011) Dial “A” for Agriculture A Review of Information and Communication Technologies for Agricultural Extension in Developing Countries. Aker JC, Mbiti IM (2010) Mobile phones and economic development in Africa. Journal of Economic Perspectives 24:207–232 The Mobile Economy Sub-Saharan Africa 2022. 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Cogent Soc Sci. https://doi.org/10.1080/23311886.2018.1505415 Victor O, Nic JL, Xiaomeng L (2021) Factors affecting the adoption of mobile applications by farmers: An empirical investigation. Afr J Agric Res 17:19–29 Nyagango AI, Sife AS, Kazungu I (2023) Use of mobile phone technologies for accessing agricultural marketing information by grape smallholder farmers: a technological acceptance model (TAM) perspective. Technological Sustainability 2:320–336 Davis FD (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information. Venkatesh V, Davis FD (2000) A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Giovanis AN, Binioris S, Polychronopoulos G (2012) An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business 7:24–53 Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User Acceptance of Information Technology: Toward a Unified View. 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J Environ Manage. https://doi.org/10.1016/j.jenvman.2025.124140 Aker JC, Cariolle J (2023) Mobile Phones and Development in Africa. https://doi.org/10.1007/978-3-031-41885-3 Additional Declarations No competing interests reported. Supplementary Files AppendixSummaryofprimarystudiesselectedforreview.docx Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Agriculture & Food Security → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5911289","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":485036299,"identity":"7158ed41-94e2-42dc-87dd-c0664ac6ac96","order_by":0,"name":"Pascal MUROMBA","email":"data:image/png;base64,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","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"prefix":"","firstName":"Pascal","middleName":"","lastName":"MUROMBA","suffix":""},{"id":485036305,"identity":"c4fdc69d-bf4f-437e-9773-1950c626cfc3","order_by":1,"name":"Minakshi KEENI","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Minakshi","middleName":"","lastName":"KEENI","suffix":""},{"id":485036306,"identity":"f9ddae12-ab53-4a49-b045-9c416ea979a9","order_by":2,"name":"Katsuhito FUYUKI","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Katsuhito","middleName":"","lastName":"FUYUKI","suffix":""}],"badges":[],"createdAt":"2025-01-27 10:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5911289/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5911289/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40066-025-00563-y","type":"published","date":"2025-12-09T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86761904,"identity":"f56575f8-5046-4b79-a822-7e27a8deb9ee","added_by":"auto","created_at":"2025-07-15 10:26:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128269,"visible":true,"origin":"","legend":"\u003cp\u003eAdapted TAM model, incorporating SSA-specific determinants informed by studies on MASA adoption among smallholder farmers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5911289/v1/6209fc05e485de8b318e8a01.png"},{"id":86762629,"identity":"5e63bb7e-251b-4894-a514-eca762a3923a","added_by":"auto","created_at":"2025-07-15 10:34:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116627,"visible":true,"origin":"","legend":"\u003cp\u003eSystematic literature review process used in our study (Adapted from [45])\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5911289/v1/81433d4f0d2a2243eccccf4d.png"},{"id":98245163,"identity":"17b41a16-3afe-4546-93fa-8a3c2448df13","added_by":"auto","created_at":"2025-12-15 16:16:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1010961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5911289/v1/22023342-47e3-4a0d-b6b0-1d6fd6b8afcb.pdf"},{"id":86761907,"identity":"523b4fd7-4802-4ecd-8fd0-4969c614ca0e","added_by":"auto","created_at":"2025-07-15 10:26:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":348946,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixSummaryofprimarystudiesselectedforreview.docx","url":"https://assets-eu.researchsquare.com/files/rs-5911289/v1/5e556e33e263137d23e2a9be.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA Systematic Review of Mobile Agricultural Service Applications for Smallholder Farmers in Sub-Saharan Africa: Perspectives from the Technology Acceptance Model\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmallholder farmers form the backbone of agricultural production in Sub-Saharan Africa (SSA). Data \u0026nbsp;[1, 2]\u0026nbsp;reveals that an estimated 33 million smallholder farmers constitute 80% of the region\u0026apos;s agricultural labour force. These farmers contributing 30\u0026ndash;40% to regional Gross Domestic Product (GDP) and representing 65\u0026ndash;70% of the total labour force. In many areas, these farmers produce up to 80-90% of the region\u0026apos;s food supply. However, despite their fundamental importance for food security and economic stability, smallholder farming productivity remains persistently low. This hampers the region\u0026rsquo;s ability to meet its food production targets and ensure food security for its growing population. The challenges faced by these farmers are multifaceted and deeply rooted. Scholars [3\u0026ndash;5] have identified multiple barriers, including limited market access, inadequate availability of inputs and services, restricted financial services, insecure land tenure, low technology adoption, and the adverse impacts of climate change. Infrastructural deficits, low digital literacy, and limited education compound these hurdles. Together, these factors impede the region\u0026apos;s agricultural potential.\u003c/p\u003e\n\u003cp\u003eDigital technology has emerged as a powerful amplifier of agricultural development in SSA. Its impact particularly pronounced when integrated with existing agricultural systems and human capabilities. The region\u0026apos;s remarkable digital revolution, driven primarily by mobile phone adoption, illustrates this transformative potential. Between 2002 and 2007, Africa recorded a 49% annual increase in mobile phone subscriptions. This was nearly triple Europe\u0026apos;s 17% growth rate during the same period [6]. This momentum has been sustained, with mobile subscriptions expanding dramatically from 16 million in 2000 to 515 million in 2021 [7]. This digital infrastructure has enabled the rise of mobile-based solutions. These tools are key to enhancing existing agricultural practices and institutions. Since the early 2000s, two main categories of mobile applications have evolved to support agricultural development: (1) mobile service applications designed for collecting and transmitting data for economic and social activities [2, 8], and (2) specialized mobile agricultural and rural development applications that provide rural communities with access to information. Researchers Qiang et al. [2] describe them as targeted solutions that provide rural communities with access to information, markets, and services. When integrated with current agricultural practices, these mobile technologies can transform smallholder farmers\u0026apos; capabilities. They help overcome challenges related to market access, information gaps, and financial inclusion [2, 6, 9\u0026ndash;11].\u003c/p\u003e\n\u003cp\u003eIn this study, the term Mobile Agricultural Service Applications (MASA) is used broadly to encompass both general mobile service applications and specialized digital tools designed specifically for agriculture and rural development. To enhance readability and reduce repetitive abbreviation, we occasionally refer to them interchangeably as mobile agricultural services, tools, or technologies all of which carry the same meaning. MASAs are widely regarded as key enablers for transforming smallholder farming systems. However, despite their potential, these applications have yet to achieve widespread or sustained adoption at scale. This is evident in both practitioner-oriented reviews [12, 13] and academic studies [14\u0026ndash;16]. Early studies [17\u0026ndash;20] attributed this gap to design-user mismatches, but more recent research points to the persistence of these challenges in more complex forms. For example, a 2022 Kenyan study by [21] found that even among farmers with growing digital awareness and smartphone ownership, adoption was hindered by poor service discoverability, weak value propositions, and limited digital literacy. These factors slow uptake of MASAs. They also diminish the perceived relevance of the tools in daily farming decisions. Broader scholarly work [22] further highlights structural constraints including inadequate Information and Communication Technology \u0026nbsp;(ICT) infrastructure, high data costs, and fragmented digital support systems that continue to disproportionately affect rural users.\u0026nbsp;In response, various interventions have been introduced to address these barriers. These include\u0026nbsp;digital extension services, farmer training programs, and low-tech interfaces such as Unstructured Supplementary Service Data (USSD ) and Interactive Voice Response (IVR). Yet, their effectiveness remains mixed: while some initiatives have succeeded in raising awareness and short-term use, few have led to long-term, self-sustained adoption. These ongoing challenges underscore that technical fixes alone are insufficient and point to the need for a deeper, user-centered understanding of the behavioral factors shaping MASA uptake.\u003c/p\u003e\n\u003cp\u003eAlthough a number of studies [23\u0026ndash;36]\u0026nbsp; have examined these issues using the Technology Acceptance Model (TAM), most remain context-specific and fragmented. As a result, there is limited cross-cutting insight into how smallholder farmers in Sub-Saharan Africa make decisions about adopting MASAs. Addressing this gap, the present review applies Davis\u0026apos;s Technology Acceptance Model [37], as a behavioral lens to systematically synthesize existing evidence and identify the key factors influencing MASA adoption in the region. TAM\u0026apos;s focus on perceived usefulness (PU) and perceived ease of use (PEOU) makes it particularly relevant for examining MASA adoption, as these core constructs directly address the challenges identified in prior research. Note that throughout this work, we may refer to PU simply as \u0026quot;usefulness\u0026quot; and PEOU as \u0026quot;ease of use\u0026quot;. TAM provides a foundational framework. However, researchers applying it to MASA adoption in SSA have recognized the need for contextual adaptation. They have frequently modified the original model by incorporating additional variables such as socioeconomic factors, perceived risk, and cultural considerations, arguing that these enhancements better capture the unique regional adoption dynamics. They argue that this contextual adaptation of TAM provides a more nuanced understanding of technology acceptance in the SSA agricultural context.\u003c/p\u003e\n\u003cp\u003eThis review conducts a systematic analysis of empirical studies applying TAM to MASA adoption, including both original and modified versions of the model that met established inclusion criteria. Given the prevalence of contextual modifications in this research domain, studies were included regardless of whether they employed the original TAM or adapted versions, provided they maintained the core TAM constructs of perceived usefulness and perceived ease of use. The aim is to identify the factors that influence adoption decisions among smallholder farmers in SSA. By doing so, it bridges the gap in understanding between technological capabilities and user design needs. Two central research questions guide this investigation: (1) Which TAM construct perceived usefulness or perceived ease of use exerts a greater influence on smallholder farmers\u0026apos; adoption of mobile agricultural service applications? (2) What are the key determinants of usefulness and ease of use that shape farmers\u0026apos; adoption decisions of MASA?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile there has been growing research on technology adoption in Sub-Saharan Africa, few studies have specifically examined the use of mobile agricultural service applications through the lens of the Technology Acceptance Model. Existing evidence is limited, fragmented, and often context-specific, making it difficult to draw broader conclusions about adoption behaviour across the region. This review addresses that gap by offering the first comprehensive synthesis of behavioural factors influencing MASA adoption among smallholder farmers in SSA. Our systematic search identified just 14 empirical studies published between 2010 and 2024 that applied TAM constructs to this context highlighting a significant gap in the literature. This study makes two key contributions. First, it evaluates how perceived usefulness and perceived ease of use influence smallholder farmers\u0026rsquo; adoption of mobile agricultural applications. It clarifies which TAM construct more strongly drives adoption in this context. Second, it systematically identifies and analyzes the key determinants underlying both perceived usefulness and ease of use across all available empirical studies. This provides a comprehensive synthesis that has been absent from the literature. By synthesizing findings from the limited but relevant empirical studies [23\u0026ndash;36], this review establishes a foundation for improving user-centric design approaches. It offers developers, extension services, and policymakers evidence-based insights to enhance mobile agricultural application design and implementation strategies. Furthermore, this study conceptualizes a tailored TAM framework for MASA adoption, which we believe will serve as a valuable tool for understanding user behavior in this context. This research not only addresses a critical gap in the literature but also provides a foundation for future studies to build upon, particularly in exploring additional factors that may influence MASA adoption in SSA.\u003c/p\u003e"},{"header":"Theoretical Framework - Technology Acceptance Model (TAM)","content":"\u003cp\u003eThis study employs the original Technology Acceptance Model [37] as the optimal framework. Its proven ability to capture adoption behaviours while avoiding unnecessary complexity is a critical consideration when studying emerging technologies. The model is widely recognized for its simplicity, generalizability, and foundational relevance in explaining technology adoption behaviours across diverse settings\u0026nbsp;[38, 39]. Its parsimonious structure enables a focused analysis of the core adoption constructs: usefulness and ease of use. This makes TAM especially appropriate for studying emerging technologies in resource-constrained environments, where overly complex models may not be practical. Several extended models incorporate additional variables such as subjective norms, voluntariness, social norms, habits, and experience.\u0026nbsp;These include enhanced versions with social influence factors\u0026nbsp;[38], the Unified Theory of Acceptance and Use of Technology [40]\u0026nbsp;, and advanced iterations with additional cognitive constructs\u0026nbsp;[41] . However, these models are often ad hoc and tailored to specific organizational or consumer environments. Empirical evidence [42] \u0026nbsp;suggests that these later theoretical developments do not necessarily improve explanatory power. In some cases, they even reduce the original framework\u0026apos;s effectiveness. Given these considerations, TAM was deliberately chosen over its successors for its balance of theoretical robustness and practical applicability.\u0026nbsp;\u0026nbsp;Figure 1 presents the adapted TAM model, reflecting MASA adoption in smallholder farming in SSA.\u003c/p\u003e\n\u003cp\u003eAt its core, this theoretical framework conceptualizes technology acceptance as a systematic three-stage process. External factors influence cognitive responses, which then shape user attitudes toward technology use. These attitudes ultimately lead to behavioural intentions and actual usage patterns\u0026nbsp;[37, 43]. The model focuses on two key constructs: PU and PEOU. These constructs are equally significant in determining users\u0026apos; behavioural intentions toward technology adoption, operating synergistically to highlight their interdependent roles in the acceptance process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived usefulness (PU)\u0026nbsp;\u003c/strong\u003eis defined as the degree to which a person believes that using a particular system would enhance their job performance [37].\u0026nbsp;It reflects users\u0026apos; assessments of a technology\u0026apos;s potential to improve operational efficiency and effectiveness. The construct suggests that for optimal utility, users must perceive a strong correlation between system use and performance improvement\u0026nbsp;[37, 38].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Ease of Use (PEOU)\u0026nbsp;\u003c/strong\u003eis defined as the degree to which a person believes that using a particular system would be effortless\u0026nbsp;[37]. It is a critical dimension in technology adoption.\u0026nbsp;The construct indicates that user acceptance is heavily influenced by system accessibility, as even highly useful technologies may face resistance if perceived as overly complex.\u0026nbsp;The construct operates through two primary mechanisms: self-efficacy and instrumentality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-efficacy\u003c/strong\u003e, refers to an individual\u0026apos;s self-assessed ability to perform tasks successfully. This construct suggests that improved system usability is positively correlated with users\u0026apos; perceived competence, establishing a direct relationship between ease-of-use perceptions and individual confidence \u0026nbsp;[44].\u0026nbsp;This increased sense of capability fosters intrinsic motivation, which plays a key role in developing positive attitudes toward technology adoption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrumentality\u003c/strong\u003e refers to the practical benefits resulting from improved usability. The construct suggests that systems with high ease of use reduce cognitive effort. This allows users to allocate mental resources more efficiently across tasks, thereby improving overall operational efficiency [37]. As a result, PEOU not only boosts user confidence but also leads to broader improvements in task management and operational effectiveness.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employs a systematic literature review (SLR) methodology based on the framework proposed by Kitchenham et al. [45]. It is tailored to the context of MASA, as illustrated in Figure 2. Drawing on the approach of Ayim et al. [46], the review begins with the formulation of two research questions to define the scope of the study. A preliminary literature search was conducted to align the search strategy with the research questions. This step also helped refine the search parameters. Inclusion and exclusion criteria were systematically developed to identify relevant studies. A quality assessment checklist was then used to evaluate methodological rigor, relevance, and reliability. Pilot data extraction was performed on a sample of studies to ensure consistency in data collection, followed by robust synthesis methods to analyze findings and derive insights addressing the research questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 2 will be inserted here\u003c/strong\u003e]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch strategy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search strategy was structured around three components. These included scope, methods, and search string construction. The scope was limited to literature published between 2010 and 2024. This reflects the absence of studies applying TAM to mobile agricultural services in SSA prior to 2010. Both web-based and database-driven methods were used to ensure comprehensive coverage. Searches were conducted in the academic databases Web of Science (n = 28), Scopus (n = 32), and ScienceDirect (n = 15), and were complemented by additional searches in Google Scholar (n = 14) to capture further relevant studies. The search strings were designed using keywords, abbreviations, and synonyms derived from the research questions. Examples include \u0026apos;(Technology Acceptance Model) AND (mobile phone) AND (smallholder farming) AND (Africa)\u0026apos; and variations such as \u0026apos;(Technology Acceptance Model) AND (adoption) AND (behaviour) AND (mobile) AND (farming).\u0026apos; For Google Scholar, terms like \u0026apos;m-services,\u0026apos; \u0026apos;agriculture,\u0026apos; and \u0026apos;TAM alternatives\u0026apos; were added to retrieve more relevant studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Selection Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystematic exclusion criteria were applied to ensure alignment with the research questions. An initial pool of 89 studies was screened based on titles and abstracts, followed by full-text reviews. Studies were excluded if they were not in English, if full-text was unavailable, or if they were unrelated to Sub-Saharan Africa. Additional exclusions were applied to studies that did not use TAM as a conceptual framework, analytical tool, or methodology. Studies that failed to address TAM in the context of mobile agricultural services in SSA were also excluded. Papers from non-indexed journals were excluded. Studies with unclear findings were also removed. This process resulted in 14 studies that met the inclusion criteria. The 14 selected studies were drawn from Scopus (n = 5), Web of Science (n = 6), and Google Scholar (n = 3). They were published in the following journals: Cogent Social Sciences [33, 34], Gender, Technology and Development [25], Information Development, African Journal of Agricultural Research [35] , Ghana Journal of Agricultural Science [27], African Journal of Science, Technology, Innovation and Development [28], Electronic Journal of Information Systems in Developing Countries [23], MDPI Agriculture [29], Journal of Strategy and Management [30], Library Philosophy and Practice (e-journal) [31], Technological Forecasting and Social Change\u0026nbsp;[32], Technological Sustainability [36], and Agriculture and Food Security [24] .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Assessment Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selected studies were evaluated using a quality assessment framework adapted from previous works [45, 46]. Four dimensions were assessed: reporting quality, methodological rigor, relevance, and credibility. Reporting quality examined the clarity and coherence of the studies, while methodological rigor focused on the validity of their approaches. Relevance assessed how well the studies addressed their objectives, and credibility evaluated the consistency of their findings. Each dimension was scored on a three-point scale: 0 (no), 0.5 (partial), and 1 (yes), to ensure objectivity.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData Extraction\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eData from the 14 studies were extracted and categorized into seven areas. These included the country of study, citation details, specific mobile agricultural services examined, influential TAM constructs, methodological approach, and the determinants of PU and PEOU. This structured approach facilitated systematic data analysis and organization into a table.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe objective of the data synthesis was to organize and interpret findings from the 14 studies to address the research questions. A mixed-methods approach was adopted, integrating quantitative and qualitative techniques for comprehensive analysis. The quantitative analysis began with a frequency count to identify the most recurring TAM constructs in the studies. The determinants were grouped based on their impact. One group influenced perceived usefulness, while the other affected perceived ease of use. These determinants were further clustered by linguistic and conceptual similarity for accurate categorization, followed by descriptive statistical analysis to quantify the geographical distribution of studies and the prevalence of determinants. Key findings were organized into a comprehensive table summarizing essential dimensions: citation details, country context, methodological approaches, specific mobile agricultural services examined, influential TAM constructs, and determinants of these two key constructs. The qualitative analysis built upon these structured data to explore patterns and relationships among determinants and their influence on mobile service adoption within this theoretical framework. Insights were drawn into how these determinants varied across contexts. The analysis also explored how frequently specific constructs appeared and how they interrelated to explain adoption behaviour. This dual approach enabled a holistic understanding of the literature and provided evidence-based answers to the research questions.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eOverview of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper reviewed 14 empirical studies that employed the TAM as their conceptual framework to explore factors influencing the adoption decisions of MASA among smallholder farmers in sub-Saharan Africa. Of the 14 studies, 70% used quantitative methods. The remaining 30% employed mixed methods. Geographically, the majority of the studies were conducted in Nigeria (n=4) [23, 24, 30, 35], followed by South Africa (n=3) [29, 33, 34], Ghana (n=2) [27, 28, 31], and one study each in Ethiopia [28], Benin [26], Tanzania [36], Uganda [32], and a combined study in Malawi and Zambia [25]. The analysis revealed that perceived usefulness was the more dominant construct, cited in 57% of the studies (8/14). In comparison, perceived ease of use was dominant in 43% (6/14). Key determinants of perceived usefulness included increased productivity (57%, n=8), better access to information and inputs (50%, n=7), and cost savings (43%, n=6). For perceived ease of use, the primary factors were user-friendliness (50%, n=7), low effort requirements (43%, n=6), and accessibility (43%, n=6). External factors also significantly influenced technology acceptance constructs, with education (57%, n=8) having the greatest impact, followed by income (50%, n=7), type of device owned (43%, n=6), and network access (36%, n=5). In terms of technologies, mobile phones as standalone devices were the most studied (n=7) [26, 28, 31, 32, 34\u0026ndash;36], while the remaining studies focused on mobile-enabled applications and services such as digital agricultural applications [30], e-agriculture services [27], smartphone agriculture applications [24, 33], social media platforms [33] , climate-smart agriculture tools [25], and e-wallet services [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of TAM Constructs on MASA Adoption\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur literature review focused on empirical studies that used the TAM as a conceptual framework to evaluate issues surrounding the adoption decisions of MASA among smallholder farmers in SSA. Using the 14 papers [23\u0026ndash;36] included in our systematic review, we aimed to answer a key question: Which construct perceived usefulness or perceived ease of use has a greater influence on smallholder farmers\u0026apos; adoption of mobile and smartphone agriculture? The reviewed studies revealed that PU is a more influential factor than PEOU in driving MASA adoption among smallholder farmers. Specifically, 57% of the studies [23, 25, 27\u0026ndash;30, 35, 36] identified PU as the primary factor influencing farmers\u0026apos; decisions to adopt MASA. While numerous determinants of usefulness emerged from the studies (which we discuss in detail later), the results suggest that smallholder farmers in sub-Saharan Africa prioritize tangible outcomes. These include increased productivity, improved access to agricultural information, and cost-effectiveness when assessing the usefulness of mobile agriculture technologies. These practical benefits significantly shape their adoption behaviour. Although both perceived use and perceived ease of use hold equal theoretical weight in the TAM, our review found that while ease of use is important, it is not the main reason farmers adopt MASA. Instead, ease of use supports adoption by making the technologies more accessible. This allows farmers to experience their practical benefits, such as higher productivity or better access to agricultural information. This finding aligns with later extensions of the TAM model, which emphasize ease of use as a facilitator rather than a primary driver of adoption.\u003c/p\u003e\n\u003cp\u003eThe determinants of the perceived usefulness of mobile agricultural technologies varied across the nine countries and 14 empirical studies reviewed (see Appendix). Despite these variations, we identified three key determinants that consistently emerged as central to farmers\u0026rsquo; evaluations of MASA\u0026rsquo;s usefulness: productivity enhancement, information access, and cost-effectiveness. Productivity enhancement was the most frequently cited determinant, appearing in approximately 60% of the studies (see Appendix). This high frequency reflects a general pattern highlighted in the studies, where farmers prioritized MASA technologies that demonstrated the ability to reduce operational inefficiencies and increase profitability. For instance, the studies highlighted that farmers particularly valued MASA when it provided timely agricultural information. This included planting schedules and pest management advice, which enabled better decision-making. Additionally, farmers valued MASA technologies when they facilitated market access by connecting them with buyers and streamlining post-harvest processes, further enhancing their perceived usefulness. Similarly, information access emerged as the second most significant determinant, highlighted in 50% of the studies (see Appendix). According to the studies, the prevalence of this determinant can be explained by farmers\u0026rsquo; tendency to value technologies that bridge knowledge gaps and support informed decision-making in areas such as crop management, pest control, and market access. Across the studies, access to timely and accurate information such as weather forecasts, soil health data, and pricing trends was consistently cited as a critical factor enhancing usefulness. However, the specific types of information prioritized varied by region, reflecting localized agricultural challenges and needs. Cost-effectiveness was the third key determinant, documented in 40% of the studies (see Appendix). As evidenced by the studies, the recurring emphasis on cost-effectiveness stemmed from a general pattern in which farmers were more likely to adopt mobile agricultural services when it offered a favourable benefit-cost ratio, ensuring that the benefits outweighed the associated costs. While the specific cost-related concerns differed across studies ranging from data usage and subscription fees to initial purchase requirements the overarching focus on cost-effectiveness remained a consistent theme. Farmers were more likely to adopt mobile agricultural tools when they perceived its benefits such as yield enhancement and labour optimization to outweigh the associated costs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our literature review of 14 empirical studies using the technology acceptance model as a conceptual framework to evaluate issues surrounding adoption decisions of MASA among smallholder farmers in sub-Saharan Africa revealed that usefulness is a stronger driver of adoption than ease of use in the region. Specifically, 57% of studies identified usefulness as the primary factor. Farmers prioritize practical benefits such as productivity enhancement, access to agricultural information, and cost-effectiveness when evaluating mobile agricultural technologies, with productivity enhancement being the most frequently cited determinant (60% of studies). While perceived ease of use plays a supporting role by making technologies more accessible, it is the tangible outcomes such as higher yields, better decision making, and favourable cost-benefit ratios that primarily shape farmers\u0026apos; adoption decisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey determinants of Perceived Usefulness (PU)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProductivity enhancement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of the empirical studies presented in Appendix identifies productivity enhancement as a crucial determinant of perceived usefulness in mobile agricultural service adoption decisions among Sub-Saharan African smallholder farmers, with 60% of studies confirming this relationship. The empirical studies converge on a key finding: mobile agricuture \u0026nbsp;technologies show varying degrees of success as catalysts for improving farm productivity, often helping farmers manage their operations more efficiently and, in many cases, increase their harvest yields. Farmers who achieved tangible benefits from using MASA particularly through increased yields and more efficient farming processes were more likely to adopt these technologies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Studies conducted in Ethiopia and Tanzania\u0026nbsp;[28, 36]\u0026nbsp;revealed that farmers who found mobile agricultural services useful primarily valued their ability to provide timely agricultural information, enabling them to make better-informed decisions about planting schedules and pest management. As a result of these practical benefits derived from using mobile agricultural services, these farmers reported positive experiences and showed increased willingness to adopt these technologies. Other investigations\u0026nbsp;[25, 30]\u0026nbsp;revealed a positive inclination toward mobile agricuture technologies among farmers who successfully used it to access agricultural information such as weather forecasts, pest management tools, and agronomic advisory services. This information enabled them to improve their crop yields and reduce agricultural uncertainties. In these studies, mobile agricuture technologies ability to provide information facilitated better farm planning and improved productivity, triggering favorable perceptions of the technologies. Beyond information services, studies also revealed MASA\u0026apos;s significant role in facilitating market access through buyer connections and streamlined post-harvest processes\u0026nbsp;[23, 36]. Among farmers who realized these benefits, MASA was viewed as useful and worth adopting.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Notably, the reviewed studies highlighted the role that external variables play in mediating the usefuless of mobile agricuture technologies. When factors such as education levels, household resources, and access to technology infrastructure\u0026nbsp;[24, 30]\u0026nbsp;were high, they directly influenced the success of farmers in using MASA. These conditions enabled the effective utilization of mobile agricuture technologies, leading to enhanced efficiency and productivity gains, thereby fostering positive perceptions and higher adoption rates. Consistent with results from the reviewed studies, evidence from other geographical contexts corroborates these findings. In Bangladesh, a study [47]that asked rice farmers to rank mobile phone productivity perceptions on a 7-point scale found agricultural information ranked second. Farmers viewed mobile phones as productivity enhancers when they provided actionable information from extension officers, enabled crop disease identification, and facilitated remote training and meetings. Overall, evidence from both our reviewed studies and those in other geographic contexts indicates that when farmers realize tangible efficiency and productivity gains enabled by using MASA in their farming operations, they develop a positive inclination toward the technologies and are more likely to adopt them. Supportive external factors, such as education and resources, further enhance the perceived usefulness of MASA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to Information and Inputs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults from 50% of the reviewed studies (Appendix) demonstrate that access to information and inputs significantly influences MASA\u0026apos;s perceived usefulness among smallholder farmers. Two studies\u0026nbsp;[27, 36]\u0026nbsp;found that smallholder farmers primarily adopted these technologies to address agricultural knowledge gaps, particularly regarding pest management, market prices, and soil conditions. Farmers who successfully used MASA to address their information needs developed a positive association with these technologies, displaying stronger adoption intentions compared to those who had not effectively used the technologies to bridge their knowledge gaps. An empirical study from Nigeria\u0026nbsp;[30]\u0026nbsp;examining adoption patterns for two applications a herbicide calculator and an agronomy application tool found that adoption rates positively correlated with the applications` ability to support timely, informed farm-level decision-making. Specifically, farmers who viewed these applications as reliable channels for accessing decision-critical information showed a greater inclination to adopt the technologies. These findings align with a review study by Aparo et al.\u0026nbsp;[48]\u0026nbsp;on mobile phone adoption patterns, which emphasized that farmers\u0026apos; technology adoption decisions were primarily influenced by their perception of the tools\u0026apos; utility in facilitating timely, informed farming decisions. Regarding input access, two studies\u0026nbsp;[23, 34]\u0026nbsp;found that farmers developed positive utility perceptions of mobile agricultural services when they experienced enhanced supplier communication and procurement efficiency. The technologies\u0026apos; ability to streamline what would otherwise be lengthy and challenging input acquisition processes through traditional channels led farmers to view them as particularly useful. Overall, in cases where mobile agricultural services favorably facilitated timely agricultural knowledge dissemination and improved connections with input suppliers, farmers were able to make timely decisions and secure inputs efficiently. Among these farmers, experiencing this ability to overcome information and input access challenges translated into positive perceptions of the technologies\u0026apos; usefulness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost-effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA synthesis of findings from empirical studies revealed that 40% of the reviewed literature (Appendix) highlighted cost-effectiveness as a key determinant shaping the perceived usefulness of MASA among smallholder farmers in sub-Saharan Africa. Empirical studies conducted in Nigeria and South Africa\u0026nbsp;[30, 33]\u0026nbsp;showed that farmers\u0026apos; MASA adoption decisions were predominantly based on an evaluation of benefits relative to costs. These costs encompassed various expenses, including data usage, subscription fees, and initial purchase requirements. Following this cost-benefit analysis, farmers who identified favorable benefit-to-cost ratios exhibited positive attitudes toward MASA adoption, ultimately integrating these technologies into their operations. Additional research\u0026nbsp;[28]\u0026nbsp;found \u0026nbsp;that cost considerations significantly influenced MASA adoption in low-income agricultural regions. The researchers found that resource-constrained farmers methodically evaluated monetary expenditures against potential benefits within their financial limitations.. The perceived usefulness of MASA and subsequent adoption were primarily observed among farmers who concluded that the potential benefits justified the investment of their limited financial resources. Further evidence from the literature\u0026nbsp;[25, 29]\u0026nbsp;indicated that applications delivering tangible benefits in yield enhancement and labor optimization, while maintaining low operational costs, generated stronger farmer engagement. This pattern consistently demonstrated that positive adoption inclinations were most prevalent among farmers who perceived mobile agricuture technologies\u0026apos; utility to outweigh the associated costs. These observed cost sensitivities and analytical approaches to determining MASA usefulness are consistent with findings by\u0026nbsp;[3], whose work documented that income constraints and dependence on non-cash income are predominant characteristics among sub-Saharan Africa smallholder farmers. These economic conditions predispose farmers to carefully scrutinize technology-related investments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; However, cost-benefit assessments of mobile agricultural technologies do not yield uniform results across all contexts. Contrary to findings from the papers we reviewed, a study in India [49] found that despite farmers having access to mobile phones and internet, traditional methods like peer-to-peer communication and mass media remained more cost-effective and convenient for accessing agricultural information. This highlights that cost-benefit assessments of mobile phone adoption for agricultural information vary significantly across regions. The synthesis of empirical studies reveals that for resource-limited smallholder farmers, MASA adoption decisions are ultimately determined by whether the anticipated agricultural operational benefits justify the required financial investment in the technologies. However, this cost-benefit calculus varies considerably across different geographical and cultural contexts, suggesting that the perceived advantages of mobile agricultural technologies over traditional information channels are not universal but rather context-dependent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey determinants of Perceived Ease of Use (PEOU)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser-Friendliness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUser-friendliness emerged as a key determinant of ease of use, with about 50% of the studies highlighting this aspect as crucial when evaluating the ease of use of mobile agricultural technologies in SSA. Across the reviewed studies, a friendly interface was typically characterized by features that reduce cognitive load and operational complexity for the user. These include intuitive navigation, voice-guided instructions, pictorial interfaces, and support for local languages. Such design elements make the technology more accessible and less intimidating, especially for users with limited formal education or digital experience. Two studies\u0026nbsp;[24, 32]\u0026nbsp;revealed that demographic characteristics, specifically age and education level, influenced farmers\u0026apos; perceptions of the ease of use of mobile agricultural technologies. In these studies, farmers with an average age of 50 years were found to have relatively low levels of formal education and demonstrated strong preferences for technologies requiring minimal training and straightforward interfaces. In contrast, younger farmers, with an average age of 40 years and higher levels of formal education, prioritized functionality over simplicity. These findings suggest that educational attainment plays a significant role in shaping how different age groups of farmers in sub-Saharan Africa assess the usability of technology. Two investigations\u0026nbsp;[30, 31]\u0026nbsp;found that MASA adoption rates increased when these technologies incorporated accessibility features such as voice-guided instructions, pictorial interfaces, and local language support. These design elements enhanced both operational simplicity and practical utility. These findings align with broader research on mobile technology design in developing regions\u0026nbsp;[19], which documented that in areas characterized by lower literacy rates, users demonstrate stronger engagement with technologies that balance simplicity with utility while minimizing user anxiety. The empirical evidence demonstrates that educational attainment and age function as significant behavioral moderators in determining mobile agricultural technology usability perceptions among distinct farmer demographics in sub-Saharan Africa. Furthermore, the analyses reveal that interface simplicity emerges as a central evaluative criterion through which farmers assess technological ease of use, ultimately influencing their behavioral disposition toward MASA adoption across the region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow Effort Requirement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 40% of the empirical studies, we reviewed (Appendix), smallholder farmers in SSA perceived minimal required effort as a key determinant in assessing mobile agricultural technologies as easy to use, which influenced their sustained adoption decisions. We observed in our review, Based on our review, the notion of \u0026ldquo;effort\u0026rdquo; encompasses physical, mental (cognitive), and emotional dimensions, with the dominant type varying across user contexts. In two of the reviewed studies [25, 34], farmers facing high workloads and limited time for acquiring new skills favored solutions that reduced physical labor and cognitive demands. For instance, a South African study[25]\u0026nbsp;evealed that climate-smart digital tools requiring minimal additional physical work or mental processing were particularly beneficial to women farmers. These women, often burdened by domestic labor and constrained by patriarchal norms, expressed a need for technologies that minimize time and energy demands both physical (e.g reduced manual input) and mental (e.g low learning burden).These findings align with broader research [50]\u0026nbsp;on women\u0026apos;s agricultural productivity in SSA. That research shows that women\u0026apos;s productivity is hampered by multiple domestic responsibilities such as childcare, cooking, water collection, and laundry which collectively cause physical fatigue, time scarcity, and emotional stress. These burdens leave little capacity for engaging with complex or time-intensive innovations.\u0026nbsp;When these technologies reduce such burdens, they are perceived as \u0026ldquo;low effort\u0026rdquo; and are more readily adopted. While women\u0026apos;s preference for low-effort technologies is primarily shaped by time poverty and domestic overload, studies like \u0026nbsp;[24] suggest that male farmers also value ease of use, though driven more by practical efficiency seeking tools that save physical effort or streamline workflows. This underscores that while both genders seek low-effort solutions, the types of effort they aim to reduce and the reasons for doing so differ, reflecting broader structural and social dynamics in SSA farming communities.\u003c/p\u003e\n\u003cp\u003eA study [51] examining behavioural intention to use mobile phone-accessible e-textbooks in Iran\u0026apos;s agricultural sector provides additional support for our review findings. It showed that users favoured e-textbooks designed with intuitive navigation and self-explanatory features, which minimised cognitive effort and enabled independent learning. This demonstrates that the relationship between low-effort design and perceived ease of use extends beyond SSA contexts, indicating that users consistently favour technologies that reduce cognitive and operational demands, regardless of geographical or cultural setting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccessibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccessibility, encompassing both infrastructure and affordability, emerged as a significant determinant of ease of use in 40% of the analyzed studies (Figure 1). The evidence reveals two distinct but interconnected pathways through which accessibility influences perceived ease of use. This twofold relationship manifests in several ways. First, device accessibility directly shapes user perceptions. Studies by\u0026nbsp;[30, 32]\u0026nbsp; demonstrated that farmers with access to durable, low-cost mobile devices consistently reported higher perceived ease of use. This established a clear link between device affordability and technology acceptance. This relationship is particularly pronounced among resource-constrained smallholder farmers, where device cost represents a primary barrier to adoption. Second, infrastructure accessibility creates the enabling environment for sustained use. \u0026nbsp;Research by\u0026nbsp;[26, 34]\u0026nbsp;provided compelling evidence that reliable network coverage and robust user-support infrastructure significantly enhance farmers\u0026apos; perceptions of mobile agricultural technologies. These factors make the tools seem more accessible and easier to use. These studies showed that even well designed technologies are perceived as difficult to use when infrastructure support is inadequate. The evidence further indicates that accessibility barriers compound rather than operate independently. Studies by\u0026nbsp;[27, 31]\u0026nbsp;demonstrated that training programs and local support services reduce perceived complexity. They do so by simultaneously addressing multiple accessibility dimensions, such as device familiarity, infrastructure knowledge, and ongoing support. This multidimensional nature of accessibility is reinforced by evidence from other regions. A study [51] on mobile phone-accessible e-textbooks in Iran\u0026apos;s agricultural sector found content quality to be the strongest predictor of adoption. Specifically, update frequency and understandability influenced both behavioural intention and perceived ease of use.\u0026nbsp;The convergence of findings across SSA and non-SSA contexts reveals that accessibility operates through context-specific but theoretically consistent mechanisms. While SSA studies emphasize physical infrastructure and device affordability, other regions prioritize content accessibility and quality. This suggests accessibility functions as an overarching concept that consistently influences ease of use by reducing cognitive and operational effort, regardless of context-specific manifestations.\u003c/p\u003e\n\u003cp\u003eNotably, given that the broader agricultural technology adoption literature identifies social and cultural influence, government support, and access to technical assistance as important drivers [36, 52\u0026ndash;55], we anticipated these institutional and contextual factors would feature prominently in the 14 studies included in our review. Contrary to our expectations, institutional and contextual factors like social influence or government support were either absent or appeared only occasionally in the studies. Social and cultural influence was associated with ease of use in just two studies [26, 35], while technical support was linked to perceived usefulness in only two others [27, 34], suggesting that these institutional and contextual factors are not being adequately captured within current TAM applications to MASA in Sub-Saharan Africa studies. One explanation we offer for this gap aligns with findings by [32]. These suggest that the original TAM does not capture the nuanced institutional contexts of Sub-Saharan Africa that shape farmer perceptions. For example, farmers may not expect digital support, while extension systems under-invest in digital tools. This reciprocal relationship represents an institutional dynamic that TAM\u0026apos;s cognitive focus may overlook. Farmers who have long relied on traditional support may not expect similar structures for mobile tools. At the same time, under-resourced extension systems tend to focus on established methods that reach more people\u0026nbsp;[55]. This could create a mutually reinforcing dynamic where mobile agricultural platforms are viewed as optional add-ons rather than essential tools requiring institutional support. The limited attention to institutional enablers in current MASA studies may therefore reflect that without extension to Sub-Saharan African contexts, TAM captures only individual-level cognitive factors, potentially overlooking how these established institutional determinants manifest in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review aimed to answer two key questions: (1) Which TAM construct perceived usefulness or perceived ease of use exerts greater influence on smallholder farmers\u0026apos; adoption of MASA? And (2) what are the key determinants shaping these perceptions? However, several limitations should be noted. Although the synthesis offers valuable insights across 14 empirical studies, it did not independently analyse the heterogeneity of study contexts. These include differences in timeframes, cultural settings, and farmers\u0026rsquo; exposure to technology. These contextual variables undoubtedly shape farmers\u0026rsquo; perceptions and adoption behaviors. However, they were beyond the scope of this review, which focused on cross-study commonalities over intra-study contrasts.\u003c/p\u003e\n\u003cp\u003eSecond, the temporal range of included studies (2010\u0026ndash;2024) spans a period of significant technological evolution in SSA, during which access to smartphones, mobile data, and digital literacy has changed considerably. While we noted the publication year of each study, we did not conduct a longitudinal or temporal comparison of how determinant importance may have shifted over time.\u003c/p\u003e\n\u003cp\u003eThird, although cultural and infrastructural differences (such as network coverage or local norms) were occasionally discussed within individual studies, our review synthesized findings thematically rather than stratifying them by cultural region or technological maturity level. As such, some context-specific nuances may have been diluted in pursuit of broader conceptual convergence.\u003c/p\u003e\n\u003cp\u003eFuture research could build on our findings by conducting meta-analyses or comparative reviews that explicitly examine temporal and cultural variation, or by integrating studies using alternative conceptual frameworks to capture dimensions not addressed by TAM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we conducted a systematic review of the literature using the Technology Acceptance Model as a conceptual framework to evaluate the adoption decisions of mobile agricultural services and applications among smallholder farmers in sub-Saharan Africa. By analyzing 14 empirical studies published between 2010 and 2024, we contributed to the limited research on the behavioral influences shaping technology uptake in this context. Our analysis revealed that perceived usefulness emerged as the most influential factor guiding farmers\u0026rsquo; decisions. They tend to prioritize tangible benefits, such as increased productivity, better access to agricultural information, and overall cost-effectiveness, when judging whether to adopt these tools. In terms of ease of use, user-friendliness, minimal effort required, and easy access were the key determinants of positive perceptions. Additionally, external variables including education level, income, device type, and the availability of network infrastructure significantly shaped how farmers assessed both usefulness and ease of use. These findings offer practical insights for technology developers and development practitioners, enabling them to design solutions that better align with the needs and preferences of smallholder farmers across the region. Based on our results, we conceptualized an adapted framework tailored to the adoption of mobile agricultural technologies (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which we believe will serve as a valuable tool for understanding user behavior in this setting. During our review, we observed that recent studies on technology uptake among smallholder farmers have increasingly turned to extended acceptance models to capture a wider range of influencing factors. Many of these studies highlight limitations in the original framework\u0026rsquo;s ability to fully explain adoption behavior in agricultural contexts. Despite these challenges, our research focused on synthesizing evidence grounded in the initial version, addressing a gap that had not yet been explored.\u003c/p\u003e\u003cp\u003eFuture studies could empirically test the adapted framework in diverse settings, use mixed methods to explore context-specific factors, and consider integrating elements from extended models. Longitudinal and comparative studies across countries, crops, or technology types could further refine the framework and support the design of more effective, user-centered mobile agricultural tools.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eMASA\u003c/strong\u003e: Mobile Agricultural Service Applications\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTAM\u003c/strong\u003e: Technology Acceptance Model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSSA\u003c/strong\u003e: Sub-Saharan Africa\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUTAUT\u003c/strong\u003e: Unified Theory of Acceptance and Use of Technology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e: Perceived Usefulness\u003c/p\u003e\n\u003cp\u003eP\u003cstrong\u003eEOU\u003c/strong\u003e: Perceived Ease of Use\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICT :\u003c/strong\u003e Information and Communication Technology \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUSSD:\u003c/strong\u003e Unstructured Supplementary Service Data\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVR\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInteractive Voice Response\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor`s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1* was responsible for all aspects of manuscript writing. 2 and 3 contributed by reviewing and revising all aspects of the research. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data has been included in the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent for Publication at Agriculture \u0026amp; Food Security and agree to BMC\u0026rsquo;s conditions of submission, copyright and license agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are affiliated with the Graduate School of Agricultural Science, Tohoku University, Japan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWiggins S, Keats S (2013) LEAPING \u0026amp; LEARNING LINKING SMALLHOLDERS TO MARKETS. \u003c/li\u003e\n\u003cli\u003eQiang CZ, Chew Kuek S, Dymond A, Esselaar S (2012) Mobile Applications for Agriculture and Rural Development. \u003c/li\u003e\n\u003cli\u003eJayne TS, Mather D, Mghenyi E (2010) Principal Challenges Confronting Smallholder Agriculture in Sub-Saharan Africa. 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J Environ Manage. https://doi.org/10.1016/j.jenvman.2025.124140\u003c/li\u003e\n\u003cli\u003eAker JC, Cariolle J (2023) Mobile Phones and Development in Africa. https://doi.org/10.1007/978-3-031-41885-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mobile Agricultural Service Applications (MASA), Technology Acceptance Model (TAM), Sub-Saharan Africa, Smallholder Farmers","lastPublishedDoi":"10.21203/rs.3.rs-5911289/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5911289/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growth in mobile phone use in Sub-Saharan Africa has seen Mobile Agricultural Service Applications (MASA) emerge as a potential solution to address agricultural challenges and enhance the productivity of smallholder farmers in the region. While their potential is undeniable, these technologies often struggle to achieve sustained adoption without external support. In response, researchers have turned to the Technology Acceptance Model (TAM) to better understand the behavioural factors that influence farmers' decisions to adopt such applications in a bid to find solutions and interventions. A key observation is that existing research is scattered and lacks a comprehensive synthesis, making it difficult for stakeholders to grasp the broader behavioural influences on adoption. This study addresses that gap by systematically reviewing empirical studies that apply the Technology Acceptance Model to examine mobile agricultural service adoption among smallholder farmers across Sub-Saharan Africa. Specifically, the study addresses two questions: (1) Which TAM construct perceived usefulness or perceived ease of use exerts a greater influence on smallholder farmers' adoption of mobile agricultural service applications? and (2) What are the key determinants of usefulness and ease of use that shape farmers' adoption decisions of MASA? A total of 14 empirical studies published between 2010 and 2024 were analysed. The findings reveal that perceived usefulness is the more influential factor, with farmers primarily motivated by tangible benefits such as increased productivity, better access to agricultural information, and cost savings. Perceived ease of use is shaped by factors like user-friendliness, simplicity, and access to supportive infrastructure. Additional external influences include education level, income, device type, and network availability. Based on these insights, we propose a contextual framework to guide future design and policy interventions aimed at promoting the sustainable use of mobile agricultural services by smallholder farmers.\u003c/p\u003e","manuscriptTitle":"A Systematic Review of Mobile Agricultural Service Applications for Smallholder Farmers in Sub-Saharan Africa: Perspectives from the Technology Acceptance Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 10:26:33","doi":"10.21203/rs.3.rs-5911289/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a374c15-f9de-464f-9ca1-8384d9b696bf","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:13:08+00:00","versionOfRecord":{"articleIdentity":"rs-5911289","link":"https://doi.org/10.1186/s40066-025-00563-y","journal":{"identity":"agriculture-and-food-security","isVorOnly":false,"title":"Agriculture \u0026 Food Security"},"publishedOn":"2025-12-09 15:57:46","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-07-15 10:26:33","video":"","vorDoi":"10.1186/s40066-025-00563-y","vorDoiUrl":"https://doi.org/10.1186/s40066-025-00563-y","workflowStages":[]},"version":"v1","identity":"rs-5911289","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5911289","identity":"rs-5911289","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}