Tropical Research Reference Platform

By Ifeanyi Charles Okoli

Published date: 19th March 2023


Countless digital solutions have been developed in recent years are enabling the 500 million farmers in the world to be more efficient, productive, and connected. These digital solutions generally address the challenges encountered in crop and livestock production, agricultural advisory services, farm management decisions, input supply, finance, insurance, market access, and linkages. Digital technologies such as Short Message Services (SMS), peer-to-peer video services (PVS), Interactive Voice Response (IVR), and Unstructured Supplementary Service Data (USSD) have been shown to empower farmers with better information and access to services. These have also been shown to be the major drivers of digital agriculture and the adoption of smart farming among smallholder farmers in many countries. The rapid penetration of mobile and smartphones into virtually every society in the world has particularly engendered the belief that digital tools could solve many of the challenges encountered in agricultural development, which would ultimately result in poverty alleviation, improve food and nutrition security, and reduce environmental footprints.

According to Tsan and colleagues in their CTA-Dalberg study, in sub-Saharan Africa where only 13 percent of farmers are registered for any form of digital service and only 3 percent are active users, barriers to the digitalization of agriculture, such as limited education and digital literacy, relatively high cost of digital devices and services, gender gaps and growing digital divide have been identified. Since the industry is majorly rural and subsistence in nature, the common communication tools are radio, television, rural telecenters, call centers, multimedia, newspapers, posters, and pamphlets. There has however been a significant movement of rural farmers from subsistence to pre-commercial and then small commercial farming in recent years resulting in the proliferation of digital agricultural platforms and social networking sites which among other roles are serving to drive adoption, scale, and bundling of services. This has been made possible by the availability of cheaper and easier-to-use mobile handsets as well as increasingly affordable and available technologies such as sensors, satellites, drones, switches, and handsets.

Digitalisation of Agriculture in Low-Income Countries

The concept of digitization has been described as the pure analog-to-digital conversion of existing data as seen in the scanning of photographs or converting a word document to a PDF. According to Gartner Glossary, digitalization is the use of digital technologies to change a business model and provide new revenues and value-producing opportunities. Ceccarelli and colleagues defined digitalization in agriculture as the use of data-generating digital tools such as sensors, machines, drones, and satellites to monitor animals, soil, water, plants, and humans to support agricultural tasks. According to Tsan and colleagues, it is the use of digital technologies, digital solutions, and data to transform business models and practices across the agricultural value chain and addresses constraints in productivity, post-harvest handling, market access, finance, and supply chain management. Digitalization is currently transforming all stages of the agricultural value chains—from production to processing, distribution, and consumption, with the data generated often transcending the boundaries between these stages. For example, farm-level data are being leveraged by supply-chain actors other than farmers such as input suppliers, credit and insurance providers, and retailers to improve their services, while also informing policy on various agricultural issues, including threats from climate change and pests, farmer livelihoods, food security, and food safety.

Digitalization equally includes digital tools or devices that are incorporated in agricultural machinery and equipment such as precision agriculture tools and disembodied devices like smartphones, tablets, and software tools, which may include advisory applications, farm management software, and online platforms. Presently at the smallholder level, most of the available tools are simple and smart digital tools that employ manual data, and others that only require smartphone ownership. Embodied smart digital tools that use sensors may also be relevant for small-scale producers with appropriate organizational models, thereby providing several options for addressing development constraints. In more advanced systems,  like precision agriculture, digitalization, and automation form the key enablers and are supported by technologies such as data and information management and analytics, artificial intelligence (AI), machine learning, deep learning, sensors, and data fusion. On this basis, precision agriculture encompasses elements of both digitalization and automation and was defined by ISPA (2022) as a management strategy that gathers, processes, and analyses temporal, spatial, and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.

Embodied solutions: According to Ceccarelli and colleagues, when digital tools and devices are combined with machinery, they are referred to as embodied solutions. These are essentially digital solutions that interact with the environment through a physical element such as agricultural machinery and equipment, which involves not just observations and decision support, but also direct action. Such tools may include the deployment of global navigation satellite systems (GNSS) and Internet of Things (IoT)-enabled devices in the hiring of agricultural machinery, with the GNSS being used for guidance, controlled traffic farming (CTF), advanced machine control, and precise geographic positioning of field-level data. Another example is the use of variable rate technologies (VRT) in combination with precision mapping services (application maps) and IoT-enabled sensors for precise pesticide application, weed control, fertilization, sowing, etc.

Disembodied solutions: These are primarily software-based solutions that require limited hardware resources. They may be in the form of a smartphone, tablet, or software tools, such as advisory applications, farm management software, and online platforms, and may also include remote sensing and/or unarmed aerial systems (UAS) which are limited to data for decision support and scouting. Disembodied solutions encompass mostly the use of SMS, mobile phones, and web-based platforms to deliver digital services for agricultural monitoring and advisory services, such as weather forecasts, market prices, linkages, input procurement, credit scoring, etc. Some of the services may also involve the use of remote sensing information or mobile tagging for production tracking. When disembodied solutions are combined with remote sensing and IoT-enabled devices for data collection, or machine vision (MV), and dedicated models they may be applied in yield monitoring and prediction, precision irrigation and fertilization, and detection of pests and diseases. Other examples include crop identification, status detection, quality grading, etc which could be achieved through geotagged images captured with a smartphone and automatically processed with AI.

Several studies such as those by Tsan and colleagues on the digitalization of African agriculture and the FAO-ITU (2022) report show that disembodied solutions are rapidly penetrating many low- and middle-income countries, especially the use of simple mobile tagging devices as part of product tracing solutions. Digital solutions involving UAS are also penetrating low- and middle-income countries, where they are mostly used for data collection, scouting, and decision support.  Elsewhere, they are being used by large-scale farms in high-value crop production because the direct operation of unmanned aerial vehicles (UAVs), data collection and processing, or outsourcing of the services are still relatively expensive.

Mechanization: The deployment of global positioning systems (GPS) and GNSS is the major driver of the digitalization of agriculture in high-income countries, since these technologies when combined with mechanization enable precision in the positioning of farm operations such as leveling, sowing, spraying, and fertilization. In low- and middle-income countries digital tools and services are only being used to facilitate access to the mechanization of open-field crop production. Ross, however, reported that in high-income countries and some middle-income Asian countries, the adoption rates for GNSS are relatively high, with the positioning, spatial mapping, and linkage of observations and measurements for the deployment of VRT being the major trends. In a recent study by McCampbell on digitalization in low- and middle-income countries, cooperative mechanization hiring services based on digital platforms and similar to the Uber car-hailing business model were shown to be emerging and rely on a commission earned by the intermediary. Examples include the TROTRO Tractor operating in Benin, Ghana, Nigeria, Togo, Zambia, and Zimbabwe in Africa, and the Tun Yat which operates in Myanmar, Asia. The beneficiaries of these concepts are small- and medium-scale farmers as well as factories and companies involved in contract farming which implies a progressive adoption of precision agriculture through VRT in these countries.

Automation and robotics: Ceccarelli and colleagues have rightly observed that in most low- and middle-income countries, smallholder farms are traditionally based on manual labor. Due to their industrial designs, however, farms in the high-income country are adapted to robotic technology, which is usually inappropriate technology for the traditional farming approaches in sub-Saharan Africa and other developing regions. In addition to this, lack of investment, and difficulty in retaining talent in the organizations developing the solutions, also affect middle-income countries. Several socioeconomic factors such as better access to education, migration to cities, social stigma, and government policies to support the jobless have however, in recent times led to diminishing interest in manual labor in some middle-income countries and may eventually favor the emergence of some level of automation and robotics applications.

The use of robots in livestock farming, especially milking robots, has reached an advanced level, in most high-income countries including North America, Europe, Asia, Latin America, and the Caribbean, and is employed mostly on medium- to large-scale farms. Other robots dedicated to manure removal, feeding, and automating barn management, as well as sensors for monitoring health, fertility, well-being, and gas emissions, have also been introduced in some of these countries.

Unmanned aerial vehicles (UAV): The commercial use of UAVs for spraying crops, especially for plant protection has been a common practice in the high-income countries of Asia for more than 30 years, while in Europe, it is considered risky and is therefore not allowed. According to OECD (2021), UAVs are used for high volume and low drift nozzles applications in high-income countries like Australia and the United States of America. Farmers in sub-Saharan countries including Benin, Ghana, Mali, Togo, and Zimbabwe have shown interest in the service, with AcquaMeyer Drone Tech spraying a total of 8 700 hectares of maize, rice, cowpea, pineapple, mango, and papaya belonging mostly to commercial farmers against pest, weed control, and micro-nutrient applications in 2021 alone. Table 1 highlights some cases of agricultural digitalization in developing countries.

Connectivity in Agriculture

Farmers are increasingly consulting and making use of essential data on different agricultural variables such as soil, crop, livestock, and weather. Connected technologies allow for targeted and resource-efficient responses at the different stages of the farming process. In high-income countries, automated farm systems with diverse wireless sensor devices and mechanisms, can monitor the environmental conditions and control the deployed devices according to the collected data through wire-line and wireless access networks. According to Bio Network (2020), precision farming tools include connecting to weather stations that provide critical climate data, Geo-referenced mapping of plots, data collection in the field with the aid of a mobile app, satellite indices for crop monitoring, forecasting models for irrigation, phenology and plant diseases among others.

Table 1: Cases of Agricultural digitalization in developing countries

(Source: Ceccarelli et al., 2022)

It was been estimated by Goedde and colleagues that enhanced connectivity in agriculture could add more than $500 billion to the global gross domestic product, representing a critical productivity improvement of 7 to 9 percent for the industry by the end of this decade. This will however require a lot of investments in connectivity such as low-power wide-area networks(LPWAN), cloud computing, and cheaper, and better sensors requiring minimal hardware. For example, enhanced connectivity is already being deployed in the areas of crop and livestock monitoring, building and equipment management, drone farming, and autonomous farming machinery to deliver higher yields, lower costs, and improved resilience and sustainability as shown in figure 1. Goedde and colleagues have also described the agricultural connectivity use cases that have the potential to radically transform agriculture to include smart-crop monitoring, drone farming, smart-livestock monitoring, autonomous farming machinery, and smart building and -equipment management. Large farms having more investment power and better incentives to digitize their operations will readily optimize the benefits of these technologies. Most farmers in low-income countries however have limited access to the advanced digital tools that would help them to participate in these innovations. For example, although several online platforms that deliver valuable data, services, and support to farmers are available, connectivity remains a prerequisite to accessing them. Even in high-income countries, very few farms currently use connected equipment or devices to access data.

Fig. 1: Existing and emerging connectivity technologies for agriculture (Source: Goedde et al., 2020)

In most cases, the networks accessible to farmers can only support a limited number of devices that lack the capacity for real-time data transfer, which is essential to unlocking the value of more advanced and complex use cases. However, as new and better technologies become more accessible to farmers it is expected that the use of digital applications and analytics, will increase in the industry and will require more efficient connectivity such as LPWAN, 5G, and LEO satellites as shown in figure 1. Improvements in connectivity for low-income countries and rural areas will require the implementation of complex sets of policies, investment, innovation capacity-building of all stakeholders, and deployment of locally appropriate, affordable, and sustainable ICT infrastructure, tools, applications, and services for the rural economy.

According to Mcnamara and colleagues, the connectivity trends identified as the key drivers of the use of ICT in agriculture, among poor producers include low-cost and pervasive connectivity, adaptable and affordable tools, advances in data storage and exchange, and the democratization of information, especially the open access movement and social media. The pervasiveness of connectivity to mobile phones, the internet, and other wireless devices has specifically been driven by factors such as decreases in costs, increases in competition, and expansion of last-mile infrastructure. Internet World Statistics (2011) estimates show that mobile phone penetration in the developing world especially in Asia and Africa was complemented by the expansion in telecommunications infrastructure, with most countries having more than 90 percent of their population served by a cell phone signal as shown in figure 2. This rapid expansion has been enabled by regulations that ensure competition in the telecommunications sector, high demand for mobile phone subscriptions, and affordability of broadband Internet connection in developing regions.

Fig. 2: Percentage of the world’s population covered by a mobile cellular signal (Source: International Telecommunications Union, 2010)

Challenges to Agricultural Digitalisation and Connectivity

The key challenges and enablers of affordable and availability available ICTs in developing countries has been linked to the kind of partnerships, regulations, and policies needs. For example partnerships among organizations with different specialties, capacities, and profit motives are usually needed to deliver affordable ICTs to rural areas but may be difficult to forge. According to Barrett and Slavova, partnerships serving as critical mechanisms for improving rural ICT access could be between the public sector, negotiated public-private partnerships, private agreements among stakeholders in the telecommunications sector, or informal understandings between service providers and stakeholders at the community level. Effective regulation is also necessary for creating the enabling environment needed to expand service provision and minimize the negative effects of competition on consumers. Barrett and Slavova reported that barriers such as a monopoly operators, and excessive licensing regimes may negatively affect business potential, while supportive fiscal and financial policies can enable and enhance ICT services. Some of the key regulatory issues in the telecommunications sector include taxes, licensing, liberalization, and competition policies.

According to the CTA Report (2019) and GSMA Report (2020), the significant barriers to the digitalization of agriculture in low-income countries could be divided into barriers at the farm level and the ecosystem and macro level as shown in figure 3.

Fig. 3: Barriers to the adoption of digital farm service (Source: CTA Digitization of African Agriculture Report 2019)


Several digital solutions have been adopted by farmers in recent years to improve their farming efficiency, productivity, and connectivity. These digital solutions are being used to transform the production, processing, distribution, and consumption stages of the agricultural value chain. The incorporation of digital tools or devices such as smartphones, tablets, software tools, advisory applications, farm management software, and online platforms into agricultural machinery and equipment has become common practice in many high-and middle-income countries. Poor connectivity, limited education, and digital literacy, relatively high cost of digital devices and services, gender gaps, and growing digital divide have been identified as major barriers to the digitalization of agriculture in low-income countries. Improvements in access to digital tools and connectivity are therefore critical to the digitalization of agriculture in these countries and will require appropriate policy, investment, innovative capacity-building of all stakeholders, and deployment of locally appropriate, affordable, and sustainable ICT infrastructure, tools, applications, and services.

Bibliographic References

Barrett,  M. and Slavova, M. (2011). Making ICT infrastructure, appliances, and services more accessible and affordable in rural areas. In: ICT in agriculture: Connecting smallholders to knowledge, networks, and institutions. The International Bank for Reconstruction and Development /The World Bank, Washington DC.

BMG/UKaid Foundations (Undated). Smart farming innovations for small-scale producers are a grand challenge request for proposal. Bill and Melinda Gates Foundation, UK.

Ceccarelli, T., Chauhan, A., Rambaldi, G., Kumar, I., Cappello, C., Janssen, S. and McCampbell, M. (2022). Leveraging automation and digitalization for precision agriculture: Evidence from the case studies. Background paper for The State of Food and Agriculture 2022. FAO Agricultural Development Economics Technical Study, No. 24. Rome, FAO.

Bio Network  (2020). Connectivity for sustainable agriculture. European Commission, -sustainable-agriculture

FAO & ITU (2022). Status of digital agriculture in 47 sub-Saharan African countries. Food and Agricultural Organization, Rome.

GSMA (2020). The mobile gender gap report. Retrieved from

International Telecommunications Union (2010). International telecommunications union’s world telecommunication/ICT indicators database.

Internet World Statistics (2011). Facebook users in the world. June 2011,

ISPA (2022). Precision Ag definition. International Society for Precision Agriculture.

McCampbell, M. (2022). Agricultural digitalization and automation in low- and middle-income countries: Evidence from ten case studies. Background paper for the state of food and agriculture 2022. FAO Agricultural Development Economics Technical Study, No. 25. Food and Agricultural Organization, Rome.

Mcnamara, K.,  Belden, C., Kelly, T.,  Pehu, E. and Donovan, K. (2011). Introduction: ICT in agricultural development. In: ICT in agriculture: Connecting smallholders to knowledge, networks, and institutions. The International Bank for Reconstruction and Development /The World Bank, Washington DC.

Pehu, E., Belden, C.,   Majumdar, S. and  Jantunen, T. (2011). Increasing crop, livestock, and fishery productivity through ICT. In: ICT in agriculture: Connecting smallholders to knowledge, networks, and institutions. The International Bank for Reconstruction and Development /The World Bank, Washington DC.

Rose, D. (2022). Agricultural automation: the past, present and future of adoption. The State of Food and Agriculture 2022, background paper. Internal document.

Tsan, M., Totapally, S., Hailu, M. and Addom, B.K. (2019). The digitalization of African agriculture report 2018-2019. Report. CTA and Dalberg Advisors. https://cgspace.cgiar. org/handle/10568/101498

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