Evaluating the Impact of a Mobile Decision-Support Tool on Smallholder Crop Management and Productivity in the Coastal Region of Kenya

1. Introduction

Many developing countries still rely heavily on agriculture, with smallholder farmers playing a major role in both rural lives and food production. Over 70% of Kenya's agricultural production comes from smallholder farmers, but their productivity is still low because to a number of issues, such as poor soil management, climate variability, and restricted access to extension services (Food and Agriculture Organization, 2020). Kenya's coastline region faces particular difficulties such unpredictable rainfall, sandy soils, and inadequate infrastructure, all of which worsen productivity problems. Decision-making has been adversely affected by the information gap caused by traditional extension systems' inability to provide farmers with timely and location-specific guidance (Aker, 2011).Opportunities for digital agricultural solutions have been made possible by the quick spread of mobile phones in Sub-Saharan Africa. Farmers can get real-time advice on planting, fertilizing, pest control, and weather forecasts from mobile decision-support technologies (Qiang et al., 2012). Although these techniques have demonstrated potential, little is known about how they actually affect smallholder productivity, especially in particular agro-ecological situations like coastal Kenya.1.1 Problem StatementSmallholder farmers in Kenya's coastal region still face low crop yields and ineffective farm management techniques despite the growing availability of mobile decision-support applications targeted at raising agricultural production. This is mostly because timely, accurate, and context-specific agricultural information is hard to come by. There is a crucial information gap because traditional extension services are frequently insufficient, understaffed, and unable to properly reach all farmers. There is little empirical data on whether mobile technologies increase crop management techniques and production among smallholder farmers in this area, despite the fact that they have the ability to close this gap. The adoption and efficient use of these tools may also be hampered by elements like limited digital literacy, inadequate network infrastructure, and cost concerns.Policymakers, agricultural extension providers, and technology developers are among the stakeholders who lack the evidence needed to justify investment, create successful interventions, and grow digital agricultural solutions in the absence of a clear knowledge of their impact. Thus, the purpose of this study is to assess how well a mobile decision-support application helps smallholder farmers in coastal Kenya manage their crops and increase productivity.1.2 Research ObjectivesTo evaluate the impact of mobile decision-support tools on crop productivityTo assess changes in crop management practices among usersTo identify barriers to adoption of mobile agricultural technologies1.3 Research QuestionsDoes the use of mobile decision-support tools significantly improve crop yields among smallholder farmers in coastal Kenya?How does the use of mobile decision-support tools influence crop management practices?What are the key factors affecting the adoption and effective use of mobile decision-support tools among smallholder farmers?

2. Literature Review

2.1 ICTs and Digital Transformation in Agriculture

Over 60–70% of the population in Sub-Saharan Africa depends on agriculture for both food security and income, making it a vital sector for livelihoods. However, because to poor extension systems, restricted access to contemporary inputs, and insufficient agricultural knowledge distribution, production levels continue to be low. As a result, by giving farmers better access to timely and pertinent information, information and communication technologies (ICTs) have become important forces behind agricultural change [(Aker & Mbiti, 2010)].Mobile phones have emerged as the most widely used and accessible digital tools in rural areas, and the adoption of ICT advancements in agriculture has been extensively documented throughout African nations. A systematic review of ICT innovations in Africa confirms that mobile-based services dominate agricultural advisory systems, while radios and television continue to play supportive roles in information dissemination (Ayim et al., 2022).

2.2 Mobile Phones and Agricultural Advisory Services

Because of their accessibility, affordability, and capacity to provide real-time information, mobile phones have gained widespread recognition as revolutionary instruments in agricultural development. According to Qiang et al. (2012), mobile applications reduce information asymmetry and increase agricultural productivity by giving farmers access to market prices, weather forecasts, and extension services. Mobile-based agricultural services have grown quickly in Kenya and other Sub-Saharan African countries. According to [Baumüller (2016)], mobile phones improve service delivery by removing geographical constraints, allowing farmers to obtain agricultural assistance without the need for in-person extension trips. Similarly, it has been demonstrated that mobile services, like SMS-based advising platforms, enhance decision-making and lower production risks [(Aker & Mbiti, 2010)].

However, problems like digital literacy, infrastructural constraints, and service price continue to limit the efficacy of mobile agriculture services despite their widespread adoption (Ayim et al., 2022).

2.3 Mobile Decision-Support Tools and Productivity Outcomes

Farmers can get data-driven advice on crop management, pest control, and input use from mobile decision-support systems (DSS). These tools help make smarter decisions by combining real-time data with agronomic knowledge. Recent research indicates that by increasing knowledge access and lowering uncertainty, ICT-based agricultural extension services have a major impact on farming practices and productivity [(Mulungu et al., 2025)]. ICT interventions, such as mobile advising tools, have been shown to have a favorable impact on agricultural output, income, and rates of technology adoption [(Fabregas et al., 2019)]. However, contextual elements like farmer education, the availability of infrastructure, and institutional support systems affect the impact's magnitude.

2.4 Adoption of Agricultural Technologies

Both technological and socioeconomic considerations have an impact on the adoption of mobile agriculture technologies. The Technology Acceptance Model (TAM), which highlights perceived utility and usability as important factors, is often used to explain user adoption behavior [(Davis, 1989)]. Farmers are more willing to use digital tools in agricultural settings if they see definite advantages in terms of income and production. According to a comprehensive analysis of mobile agricultural service applications in Sub-Saharan Africa, perceived utility, usability, and external enabling factors like infrastructure assistance and training have a major impact on uptake [(Muromba et al., 2025)]. In a similar vein, research on ICT adoption in Africa reveals enduring obstacles such as low digital literacy, insufficient ICT infrastructure, and expensive service fees (Ayim et al., 2022).

2.5 Empirical Evidence from Africa and Kenya

Kenya's robust ICT environment and extensive mobile penetration make it a leader in mobile agricultural innovation. According to [Baumüller (2016)], Kenya has been in the forefront of agricultural mobile service innovation, including platforms that offer weather forecasts, input supply information, and market prices. Nevertheless, research indicates that usage is still unequal and on a small scale despite these advancements. Due to inadequate training and inadequate extension support networks, many farmers still do not have access to digital technologies or do not use them efficiently [(Mulungu et al., 2025)]. Additionally, the results of ICT-based agricultural interventions are frequently varied, with bigger effects shown among farmers with greater resources and education [(Fabregas et al., 2019)].

2.6 Research Gap

There are still a number of gaps in the growing body of research on ICT-enabled agriculture in Sub-Saharan Africa. First, rather than concentrating on particular decision-support technologies, the majority of research concentrate on ICT adoption in general. Second, the usefulness of mobile decision-support systems in coastal Kenya, an area with distinct agro-ecological and socio-economic characteristics, is not well supported by empirical data. Third, rigorous quasi-experimental assessments that separate the causal influence of mobile technologies on productivity results are frequently absent from current research. Thus, by offering actual data on the effects of a mobile decision-support tool on smallholder crop management and productivity in coastal Kenya, this study adds to the body of literature.

3. Methodology

3.1 Research Design

This study assessed the effect of a mobile decision-support application on smallholder agricultural productivity using a quasi-experimental research approach. Since it was not possible to randomly assign farmers to treatment and control groups under actual farming conditions, the quasi-experimental technique was appropriate. Farmers who utilized the mobile decision-support application (the treatment group) and those who did not (the control group) were compared in the study. This approach made it possible to evaluate how tool usage affected variations in crop productivity and farm management techniques.

3.2 Study Sample and Sampling Procedure

A total of 200 smallholder farmers from Kenya's coastal region participated in the study; 100 of them used the mobile decision-support application, and the remaining 100 did not. To guarantee representation from a variety of farming communities, farm sizes, and socioeconomic backgrounds, respondents were chosen using a stratified sampling technique.

3.3 Data Collection Methods

A combination of field observations, farm production records, and structured questionnaires were used to gather data. The survey recorded farming methods, mobile tool usage trends, demographic traits, and the technology's perceived utility. Farmers' records and field estimations were the direct sources of crop yield statistics.

3.4 Data Analysis

Both descriptive and inferential statistical methods were used to analyze the data. Adoption trends and farmer characteristics were compiled using descriptive statistics. Multiple regression analysis was used to ascertain the impact of mobile tool usage on crop productivity while controlling for other explanatory variables, and independent samples t-tests were used to evaluate mean differences in crop yields between users and non-users. The following was the specification of the regression model:

Yield = β₀ + β₁(Tool Use) + β₂(Farm Size) + β₃(Education) + β₄(Experience) + ε

where:

• Yield represents crop productivity,

• Tool Use is a binary variable (1 = user, 0 = non-user),

• Farm Size, Education, and Experience are control variables,

• ε is the error term.

4. Conceptual Framework

The study's conceptual framework demonstrates the proposed connections between crop yield, farm management techniques, and the use of mobile decision-support tools by smallholder farmers in Kenya's coastal region. According to the framework, agricultural productivity is indirectly impacted by the deployment of mobile decision-support technologies through enhanced farm management techniques. In this relationship, tool usage and productivity outcomes are mediated by farm management methods. In particular, farmers that utilize mobile decision-support technologies are anticipated to have access to timely, pertinent, and location-specific agricultural information that improves decision-making in crucial farming tasks including planting, applying fertilizer, controlling pests, and harvesting. It is anticipated that better decision-making would result in more effective farm management techniques, which will eventually raise agricultural yields and farm revenue.

Additionally, it is expected that farmer-specific factors such as education level, farm size, and farming experience influence the association between mobile tool use and crop productivity. It is anticipated that education will improve farmers' capacity to successfully analyze and use digital agricultural information. Farm experience may have an impact on how digital information is linked with indigenous knowledge systems, and farm size may have an impact on how well farmers can use recommended methods.

Regression Results

Table 1 displays the multiple regression analysis's findings. The results show that crop productivity is positively and statistically significantly impacted by the use of mobile decision-support tools. In particular, farmers who utilized the tool outperformed those who did not.

There is a large and statistically significant positive correlation between crop productivity and mobile tool use (β = 0.85, p = 0.001). This suggests that, when all other variables are held equal, farmers who employ mobile decision-support tools produce significantly higher yields than those who do not. A positive and statistically significant influence on productivity is also shown by farm size (β = 0.30, p = 0.020), indicating that farmers with bigger land holdings typically attain higher output levels, probably as a result of economies of scale and better resource availability.

Education level (β = 0.15, p = 0.045) is similarly positive and statistically significant, suggesting that farmers with higher levels of education are better able to comprehend and use agricultural knowledge, which results in higher output. Despite having a positive correlation with productivity, farming experience (β = 0.10, p = 0.080) is not statistically significant at the 5% level. This implies that although experience could help farmers make better decisions, it has less of an impact than formal education and the use of digital tools. Overall, the regression model shows that among smallholder farmers in the research area, mobile decision-support technologies are a significant factor in determining agricultural productivity.

5. Discussion

The results verify that smallholder farmers' agricultural productivity is greatly increased by mobile decision-support technologies. These findings are in line with earlier research that emphasizes how ICTs might enhance access to agricultural information [(Fabregas et al., 2019)]. However, the full potential of these technologies is limited by adoption constraints including inadequate infrastructure and insufficient digital literacy.

6. Conclusion

The purpose of this study was to assess how a mobile decision-support application affected smallholder crop management techniques and productivity in Kenya's coastal region. The results offer solid empirical proof that crop productivity is significantly increased when mobile-based agricultural decision-support systems are used. Compared to non-users, farmers who used the mobile tool reported greater yields, suggesting that digital agricultural interventions can successfully close information gaps that have historically limited smallholder farming systems.

The findings also show that mobile decision-support technologies are useful through better farm management techniques in addition to being direct. Farmers were able to boost agricultural output by making better decisions about planting, fertilizer application, and pest management thanks to timely and context-specific information. This demonstrates how farm management techniques can improve productivity results by acting as a mediator. The study found that while farming expertise had a smaller impact, socioeconomic factors like farm size and education level had a considerable impact on production outcomes. These results imply that although digital tools are helpful for all types of farmers, their effects can be amplified when farmers have more information and sufficient resources to carry out suggested practices.

The study's overall findings indicate that mobile decision-support tools are a practical and significant innovation for raising smallholder farmers' agricultural output in coastal Kenya. But only until issues like disparities in access to technology, infrastructure constraints, and gaps in digital literacy are resolved can they reach their full potential. Increased funding for rural digital infrastructure, focused farmer education initiatives, and the creation of user-friendly, regional agricultural applications are all suggested by the study. In order to scale up digital agriculture solutions and guarantee sustainable agricultural development in the area, government agencies, extension services, and technology suppliers must work together more closely.