CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Farmers make decisions throughout the season: what to plant, which variety to use, when to plant, what inputs to apply, how to control pests, and when and where to sell. The quality of these decisions depends on the information available to the farmer. Traditionally, this information has come from extension agents, other farmers and radio, but the reach of extension is limited. Ghana has improved the ratio of extension agents to farmers from 1:1,906 in 2016 to 1:709 in 2020, yet access to extension remains limited (Ninson & Ninson, 2026).

Digital tools such as mobile phones, SMS, voice messages and platforms such as Esoko offer another channel. Aker (2011) reviewed the use of information and communication technologies (ICT) for agricultural extension in developing countries, grouping them by mechanism, and argued that the spread of mobile phone coverage offers an opportunity to help farmers adopt new technologies. Aker et al. (2016) discuss both the promise and the pitfalls of ICT for agriculture initiatives. The evidence on effects is mixed. In a randomised experiment in India, Fafchamps and Minten (2012) found that farmers who received market and weather information by SMS linked it to some of their decisions, but the information had no significant average effect on the prices farmers received or on whether they changed crop varieties and cultivation practices. Cole and Fernando (2021) studied how mobile phone-based agricultural advice affects technology adoption, diffusion and sustainability. Together, these studies suggest that digital information does not automatically change what farmers do; its influence depends on the content, the context and the farmer's ability to act on it.

Recent Ghanaian studies give a more detailed picture. Asante et al. (2024b) used data from 3,197 maize-producing households and found that the use of digital advisory services significantly increased the probability of adopting row planting, zero tillage and drought-tolerant seed, by 12.4, 4.2 and 4.6 per cent respectively, with a larger effect on row planting among women than among men. Miine et al. (2023) surveyed 1,199 smallholder farmers in the Bono East Region and found that membership of farmer-based organisations, access to credit and participation in agronomic training increased the likelihood and intensity of adopting digital agricultural solutions, while receiving visits from extension officers reduced them. In a master's study of the Esoko platform in Nkoranza, Larten (2025) found that farmers perceived the platform as useful, reliable and supportive of decisions on planting, input application and marketing, although they reported problems with network connectivity, message timing and language. Azumah et al. (2018) found low patronage of ICT methods such as mobile phones and video among rice farmers in Northern Ghana, and Dzanku et al. (2021) found in a randomised experiment in northern Ghana that video was effective in inducing technology uptake.

Most of these studies concern rural farmers in the middle and northern belts. Kpone-Katamanso Municipality, in the eastern part of the Greater Accra Region, is different. It lies within the Tema industrial and port enclave, had a population of about 412,828 at the 2021 Population and Housing Census (Kpone Katamanso Municipal Assembly, 2026). The Municipal Director of Agriculture has said that the expansion of settlements and housing has depleted much of the land that could have been used for crops and livestock, and encouraged farmers to consider non-traditional agriculture such as snail and grasscutter rearing, mushrooms and home gardening (Ghanaian Times, n.d.). Farmers in Kpone, Santeo and Apollonia therefore operate in a peri-urban setting with limited land, close to large urban markets, which may make timely information on inputs and prices particularly relevant to their decisions. The influence of such information on farmers in these communities has not been documented, and this study addresses that gap.

1.2 Problem Statement

Digital agricultural services have expanded in Ghana, and policy makers and development partners promote them as a way of reaching farmers who extension agents cannot reach. The evidence on whether they change farmers' decisions and technology adoption is mixed internationally (Fafchamps & Minten, 2012) and promising but incomplete in Ghana (Asante et al., 2024b; Miine et al., 2023). Barriers such as poor connectivity, the timing of messages and language have been reported (Larten, 2025), and farmers do not use all channels equally (Azumah et al., 2018).

Ghanaian studies of digital agriculture have concentrated on rural and savannah areas. Little published evidence was found on how farmers in peri-urban Greater Accra, where land is limited because of urban expansion (Ghanaian Times, n.d.), use digital agricultural information, which decisions it influences, and whether it leads them to adopt new technologies. This study was therefore designed to examine the influence of digital agricultural information on farmers' decision-making and technology adoption in Kpone, Santeo and Apollonia communities of the Kpone-Katamanso Municipality.

1.3 Purpose and Objectives of the Study

The purpose of the study is to examine the influence of digital agricultural information on farmers' decision-making and technology adoption in Kpone, Santeo and Apollonia communities in the Kpone-Katamanso Municipality, Greater Accra Region. The specific objectives are to:

(i) describe the socio-economic characteristics of the farmers in the three communities;

(ii) identify the types and sources of digital agricultural information that the farmers use;

(iii) determine the production and marketing decisions that the farmers make with the help of digital agricultural information;

(iv) determine the influence of digital agricultural information on the adoption of improved farming technologies; and

(v) identify the constraints to the use of digital agricultural information.

1.4 Research Questions

The study seeks to answer the following questions:

1. What are the socio-economic characteristics of the farmers in Kpone, Santeo and Apollonia?

2. What types and sources of digital agricultural information do the farmers use?

3. Which production and marketing decisions are made with the help of digital agricultural information?

4. What is the influence of digital agricultural information on the adoption of improved farming technologies?

5. What constraints limit the use of digital agricultural information?

1.5 Research Hypotheses

The following hypotheses will be tested at the 5% level of significance:

H0₁: There is no significant relationship between the use of digital agricultural information and the adoption of improved farming technologies.

H1₁: There is a significant relationship between the use of digital agricultural information and the adoption of improved farming technologies.

H0₂: The socio-economic characteristics of farmers (age, sex, education, farm size, farming experience and income) have no significant influence on their use of digital agricultural information.

H1₂: The socio-economic characteristics of farmers (age, sex, education, farm size, farming experience and income) have a significant influence on their use of digital agricultural information.

H0₃: There is no significant difference in the adoption of improved farming technologies between farmers who use digital agricultural information and those who do not.

H1₃: There is a significant difference in the adoption of improved farming technologies between farmers who use digital agricultural information and those who do not.

1.6 Significance of the Study

The findings will help farmers in the three communities to see how digital information can support their decisions and which services are useful. They will help the Kpone-Katamanso Municipal Department of Agriculture, MoFA and providers of digital agricultural services, such as mobile advisory platforms, to improve the content, timing and language of their messages and to decide how digital services can complement face-to-face extension.

Policy makers and telecommunication and digital agriculture stakeholders will obtain evidence from a peri-urban setting, which is under-studied, to inform digital agriculture strategies. The study will also add to the literature on ICT, extension and technology adoption in Ghana and will serve as a reference for students and researchers.

1.7 Scope and Delimitation of the Study

The study is limited to farmers in Kpone, Santeo and Apollonia communities in the Kpone-Katamanso Municipality of the Greater Accra Region. It covers digital agricultural information received or sought through mobile phones (voice calls, SMS and messaging or social media applications) and dedicated agricultural platforms, and its relationship with production and marketing decisions and the adoption of improved technologies. It does not cover farmers outside the three communities, or the use of digital tools for financial services alone. The data relate to the most recent farming season.

1.8 Limitations of the Study

The study was limited by time and finance, which restricted the sample size. Because farming in the municipality is under pressure from urban development, the number of active farmers may not be well documented, which may make it difficult to draw a complete sampling frame. Farmers' accounts of which decisions were influenced by digital information depend on recall and judgement, and it can be hard to separate the influence of a phone message from that of an extension agent, a neighbour or radio. The study can therefore show association but cannot fully prove that digital information caused a decision. The findings from three peri-urban communities should be generalised with care. The researcher tried to reduce these limitations by building the sampling frame with the help of the Municipal Department of Agriculture and farmer groups, by using a structured questionnaire, and by recording other information sources as control variables.

1.9 Definition of Terms

Digital agricultural information: Information on farming, such as weather, market prices, inputs, planting practices and pest control, that a farmer receives or seeks through digital channels such as mobile phone calls, SMS, messaging applications and agricultural advisory platforms.

Decision-making: The choices a farmer makes about production and marketing, such as which crop or variety to plant, when to plant, what inputs to use, how to control pests and diseases, and when and where to sell.

Technology adoption: The regular use by a farmer of an improved technology, such as improved seed, row planting, recommended fertiliser use or integrated pest management, after learning about it and trying it (Rogers, 2003).

Digital advisory services: Tools and platforms, including mobile applications, voice and text messages and radio programmes, that give farmers information and advice for decision-making.

Peri-urban farming: Farming carried out in and around towns and cities, where land is limited and markets and services are close.

1.10 Organisation of the Study

The thesis has five chapters. Chapter One introduces the study. Chapter Two reviews the relevant theoretical and empirical literature and presents the conceptual framework. Chapter Three describes the study area and the research methodology. Chapter Four presents and discusses the results. Chapter Five summarises the findings, draws conclusions and makes recommendations.

REFERENCES

Aker, J. C. (2011). Dial "A" for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647.

Aker, J. C., Ghosh, I., & Burrell, J. (2016). The promise (and pitfalls) of ICT for agriculture initiatives. Agricultural Economics, 47(S1), 35–48.

Asante, B. O., Ma, W., Prah, S., & Temoso, O. (2024b). Promoting the adoption of climate-smart agricultural technologies among maize farmers in Ghana: Using digital advisory services. Mitigation and Adaptation Strategies for Global Change, 29(3), Article 19. https://doi.org/10.1007/s11027-024-10116-6

Azumah, S. B., Donkoh, S. A., & Awuni, J. A. (2018). The perceived effectiveness of agricultural technology transfer methods: Evidence from rice farmers in Northern Ghana. Cogent Food & Agriculture, 4(1), Article 1503798. https://doi.org/10.1080/23311932.2018.1503798

Cole, S. A., & Fernando, A. N. (2021). "Mobile'izing" agricultural advice: Technology adoption, diffusion and sustainability. The Economic Journal, 131(633), 192–219.

Dzanku, F. M., Osei, R. D., Nkegbe, P. K., & Osei-Akoto, I. (2021). Information delivery channels and agricultural technology uptake: Experimental evidence from Ghana. European Review of Agricultural Economics. Advance online publication. https://doi.org/10.1093/erae/jbaa032

Fafchamps, M., & Minten, B. (2012). Impact of SMS-based agricultural information on Indian farmers. The World Bank Economic Review, 26(3), 383–414.

Ghanaian Times. (n.d.). Lack of arable land hampers agric in Kpone-Katamanso. https://ghanaiantimes.com.gh/lack-of-arable-land-hampers-agric-in-kpone-katamanso

Kpone Katamanso Municipal Assembly. (2026). Composite budget: Kpone Katamanso Municipal Assembly. Ministry of Finance, Ghana. https://www.mofep.gov.gh/sites/default/files/composite-budget/2026/GR/Kpone_Katamanso.pdf

Larten, H. (2025). Adoption of digital agricultural extension platforms and their contribution to crop yields among rural farmers in Nkoranza, the Bono East Region: A study of Esoko [Master's dissertation, University of Media, Arts and Communication]. UniMAC Repository. https://repository.unimac.edu.gh/handle/123456789/927

Miine, L. K., Akorsu, A. D., Boampong, O., & Bukari, S. (2023). Drivers and intensity of adoption of digital agricultural services by smallholder farmers in Ghana. Heliyon, 9(12).

Ninson, J., & Ninson, D. (2026). Are agricultural extension services accessible to Ghanaian farmers? Probabilities and expectations from corner solution responses. Applied Studies in Agribusiness and Commerce, 20(1). https://doi.org/10.19041/APSTRACT/2026/1/10

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.