Emerging ICT Technologies for Agriculture, Training, and Capacity Building for Farmers in Developing Countries: A Case Study in Zimbabwe

Emerging ICT Technologies for Agriculture, Training, and Capacity Building for Farmers in Developing Countries: A Case Study in Zimbabwe

Samuel Musungwini, Petros Venganai Gavai, Briget Munyoro, Anyway Chare
DOI: 10.4018/978-1-6684-6413-7.ch002
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Abstract

This chapter explores the use of emerging ICT technologies for agriculture in Zimbabwe, a developing country in sub-Saharan Africa. It investigates the concept of ICT technologies and their potential impact on agriculture. The chapter then brings the background and context of the study. The chapter is premised on a qualitative approach which fuses a literature review to situate the chapter and eight (8) in-depth interviews and a ten (10) member focus group discussion to gather empirical data. The findings reveal that there are emerging ICT technologies in Zimbabwe which include artificial intelligence, drone technology, IoT, robots, and big data. However, the application and use of these technologies are still very localized and limited to big and highly capacitated commercial farmers, with very few smallholder farmers using the technology because of challenges that impede the adoption and use of these emerging ICT technologies for agriculture by the bulk of farmers. The chapter submitted measures that could be implemented to try and increase the adoption and increased use of these technologies.
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Introduction

The advent of Information and communication technology (ICT) has transformed the general operational landscape and it is now business unusual. This transformation has been felt across different facets from government operations (Twinomurinzi et al., 2017), health (Simbarashe & Coleman, 2018), education (Gocha, 2014), and production areas like agriculture (Batani et al., 2019) changing at a rapid pace. This has resulted in the coining of such terms as ICT for development, Mobile for development (M4D), and sustainable development goals (SDGs) (Msendema & Nyirenda, 2019). These digital technologies enable people from all lifestyles to connect from their remote locations to any part of the world across the globe at high speeds at any time (Musungwini & Zyl, 2017). These ICTs are continuously evolving since the advent of the computer and the SDGs framework is premised on harnessing ICTs to attain the world we want. In the last decade, literature is awash with research articles, dissertations, and policy documents on the adoption, application, and use of ICTs in various areas including agriculture (Ayim et al., 2020; Musungwini et al., 2022). Historically, the agriculture sector has been the mainstay of the Zimbabwean economy, which resulted in the country being acclaimed as the breadbasket of Africa.

However, in the last three decades from 1990 to 2020, the sector has undergone a rapid decline because of the crumbling economy. Technology is continuously evolving and its tilting towards an Artificial Intelligence (AI)-enabled smart precision agriculture as posited by Loures et al., (2020). The use of digital smart connected farm culture in which technology is the heart and soul, which dictates and empowers a farmer in a truly rewarding way, is now the order of the day (Linaza et al., 2021). In recent years, agriculture practitioners and farmers alike have been increasingly fighting all sorts of external and internal factors such as unpredictable hostile weather, pest problems, water shortage, and even drought. Advanced applications of ICTs like AI, Sensors, Drones, IoT, and Big data analytics, have emerged as a window of opportunity for the farming community (Psirofonia et al., 2017). This smart technology can ensure that every plant is scanned for health and growth tracking such that any slight change in the condition of the plant is identified and notified to the farmer in real-time (Ben Ayed & Hanana, 2021). The government has acquired the High performance-computing infrastructure, which can be used to process large amounts of data in a short time frame and to make sense of large quantities of structured and unstructured data.

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