Keywords

1 Introduction

Information and communication technology (ICT) includes services, devices, applications and networks. ICT is defined by the World Bank (2011) as “… any device, tool, or application that permits the exchange or collection of data through interaction or transmission” (p. 3) and it adds that ICT “includes anything ranging from radio to satellite imagery to mobile phones or electronic money transfers” (p. 3). Indeed, ICTs range from traditional communication aids (e.g. telephones, televisions, radios), internet and mobile applications, to Cloud Computing, Big Data analytics and information systems, Internet of Things (IoT), remote sensing and drones using geographic information systems (GIS), blockchain and artificial intelligence (FAO and ITU 2019; World Bank 2011). The importance of ICT for addressing sustainability challenges was recognized in the 8th Millennium Development Goal “Global partnership for development”, with the target to “…make available the benefits of new technologies, especially information and communications technologies (ICTs)” (United Nations 2015a) in the fight against poverty. It is also highlighted in the 2030 Agenda for Sustainable Development (United Nations 2015b) and the Sustainable Development Goals (SDGs) where ICT is considered in relation to SDG 4 (Quality education), 5 (Gender equality), 9 (Industry, innovation and infrastructure) and 17 (Partnerships for the goals).

Agriculture is strongly related to many sustainability challenges such as biodiversity loss, climate change, water scarcity, rural poverty, and malnutrition and food insecurity (Bruinsma 2011; FAO 2014, 2016; Foley et al. 2011; IAASTD 2009; Postel 2000). For that, and in order to address these pressing challenges, there have been many calls for sustainability transitions in agro-food systems (El Bilali 2018, 2019; FAO 2017; UNEP 2018). ICT is one of the instruments contemplated to make agriculture smarter and more sustainable (Bello and Aderbigbe 2014; El Bilali and Allahyari 2018; FAO et al. 2017; FAO and ITU 2019; Singh et al. 2014; Sørensen et al. 2019; Svenfelt and Zapico 2016). Thanks to their disruptive potential, ICTs hold the potential to contribute to transition towards sustainability in agriculture (Berti and Mulligan 2015; FAO et al. 2017; Nakasone et al.2014; Poppe et al. 2013; World Bank 2011). Disruptive ICT trends include Big Data (web of data, linked open data), mobile/cloud computing, location-based monitoring (remote sensing, geo information, drones, etc.), IoT (Poppe 2016; Wolfert 2015). Therefore, agriculture digitization is high on the political agenda, especially in industrialized and in transition economies (G20 2017), and ICT application in agricultural development is highlighted as a priority (Kolshus et al. 2015; UN-ESCAP 2008). ICTs are increasingly used in modern agriculture (Lehmann et al. 2012). The global smart farming market is expected to have an annual growth rate of 19.3% from 2017 to 2022 thus reaching $23.14 billion by 2022 thanks to the growing penetration of ICT in farming – in developed and developing countries alike – and the increasing attention to climate-smart agriculture (BIS Research 2018). Apart from addressing the issue of climate change, ICTs have also been put forward as a means to achieve food security and to enhance agri-food systems sustainability (Svenfelt and Zapico 2016).

This review paper analyses the potential of ICT in agricultural sector. In particular, it explores the benefits of ICT in terms of agriculture smartness and sustainability, provides an overview on the main technologies used, delineates the contours of the smart farming market and landscape, and analyses the potential drawbacks of such a ‘digital revolution’ in agriculture.

2 ICTs in Agriculture: E-Agriculture, Smart Agriculture and Precision Agriculture

Different terms have been used to refer to the application of ICTs in agriculture e.g. e-agriculture, digital agriculture, smart agriculture, precision agriculture.

FAO uses the term e-agriculture to refer to the application of ICTs in agriculture and rural development (FAO et al. 2017; Kolshus et al. 2015). E-agriculture involves the conceptualization, development and application of ICTs solutions in agriculture and rural areas (FAO 2019).

Precision agriculture (PA) is a widely cited example of the application of ICT in agriculture (Allahyari et al. 2016; Balafoutis et al. 2017; Ess and Morgan 2003; McBratney et al. 2005). It is a modern farming model that consists in the use of sensors to optimize the utilization of inputs (e.g. fertilizers, pesticides, irrigation water) (Mintert et al. 2016). It came into use in the 1980s as GPS technologies became accessible by farmers, especially in developed countries. Modern PA relies on a combination of satellite navigation and GPS technology, sensors and IoT (Schrijver et al. 2016). McKinsey & Company (Magnin 2016) define precision agriculture (i.e. precision farming) as “a technology-enabled approach to farming management that observes, measures, and analyzes the needs of individual fields and crops” (p. 4) and argue that it is shaped by two trends “big-data and advanced-analytics capabilities on the one hand, and robotics—aerial imagery, sensors, sophisticated local weather forecasts—on the other” (p. 4). The European Parliamentary Research Service (Schrijver et al. 2016) defines precision agriculture (PA) as “a modern farming management concept using digital techniques to monitor and optimise agricultural production processes” (p. 6) and highlights that the key point in PA is optimisation of the use of resources and inputs (e.g. fertilizers). In many countries (e.g. China, Japan), wide-scale deployment of smartphones and IoT systems led to a rapid adoption of precision farming solutions. Precision agriculture technologies (PATs) include variable rate technologies (planting/seeding, irrigation, nutrient application, pesticide application), machine guidance (auto-guidance or driver assistance), precision physical weeding technology, controlled traffic farming (a system confining all load of machinery to permanent traffic lanes) (Balafoutis et al. 2017). In variable rate technologies, data from sensors allow tailoring the amounts of inputs to current crops needs and different fields/plots, instead of applying the same amount of inputs to the whole area. PATs are nowadays present in all stages of crop production and increasingly used in livestock production (Banhazi et al. 2012; FAO et al. 2017). For instance, virtual fence technology allows managing cattle herd based on sensors and remote-sensing signals, or actuators attached to cattle (Umstatter 2011). Companies offer a variety of applications for precision crop farming such as yield monitoring and forecasting, precision irrigation, crop scouting, variable rate application, and recordkeeping. In this regard, a large market share of precision crop farming is held by precision irrigation products. As for the livestock sector, the introduction of autonomous milking robots is expected to increase the growth of smart farming solutions (BIS Research 2018).

Smart farming (cf. smart agriculture) represents the application of modern ICTs for optimising complex farming systems (AgroCares 2019; Smart AKIS 2016). This application is leading to what can be called a ‘Third Green Revolution’ (following the plant breeding and genetics revolutions) (Smart AKIS 2016) or ‘fourth revolution’ (after domestication of animals and plants, systematic use of crop rotations, and ‘Green revolution’) (Walter et al. 2017). Unlike with precision agriculture, smart farming (SF) focuses on access to data and their use in a smart way rather than precise measurement and determination of differences between farm plots or animals. Smart agriculture involves the use of a wide range of ICTs and mobile devices (e.g. smart phones and tablets) to access real-time data about soil, plants, climate, weather, etc. so that farmers have the information needed to make informed decisions (AgroCares 2019). SF is based upon the combined application of ICT solutions such as the IoT, precision equipment, sensors and actuators, Big Data, GPS, robotics, unmanned aerial vehicles (e.g. drones), etc. It is, hence, strongly related to three interconnected technology fields; management information systems, precision agriculture, agricultural automation and robotics (Smart AKIS 2016). The IoT support is essential for sensors and machines to interface with the farmer. Autonomous robotic tractors, machines (e.g. seeding machines), drones are self-sufficient, along with the inclusion of GPS, cameras and the IoT connectivity to enable remote operation and monitoring. Drones enable farmers to collect more information about crops (e.g. crop health) and can execute different operations such as imaging, monitoring, spraying. Sensors collect data on soil as well as air quality, weather, light conditions, irrigation. Pulling together sensors and drones is what forms the Internet of Things (IoT), a network of physical devices that are carefully outfitted with electrical connectivity and that enables data exchange, processing and aggregation. The use of the IoT enables a more immediate integration of the physical world into electronically-based systems, resulting in economic benefits, efficiency, and reduced human labour (Smart AKIS 2016).

Digital farming integrates both precision farming and smart farming concepts. Its essence lies in creating actionable intelligence and meaningful added value from data thus going beyond the mere availability of data (CEMA-Agri 2017). In a paper by the German Agricultural Society (DLG 2018), digital agriculture is understood to mean consistent application of precision agriculture and smart farming methods, use of web-based data platforms together with Big Data, and internal and external networking of the farm.

3 Benefits of ICTs: Smartness and Sustainability of Agriculture

Svenfelt and Zapico (2016) argue that ICT solutions can improve the sustainability of agriculture by increasing efficiency, networking agro-food chain actors, and enhancing traceability and transparency. Likewise, El Bilali and Allahyari (2018) put that “ICTs can contribute to agro-food sustainability transition by increasing resource productivity, reducing inefficiencies, decreasing management costs, and improving food chain coordination” (p. 456). According to FAO and the International Telecommunication Union (FAO and ITU 2016), benefits of e-agriculture include increasing markets efficiency, improving vertical and horizontal linkages, facilitating information sharing and networking, developing value-added services, reducing individual and institutional risk, increasing food and nutrition security and safety. Verdouw et al. (2016) and Mat et al. (2018) argue that agriculture productivity and sustainability can be dramatically improved through the revolutionary potential of IoT perspective. Schrijver et al. (2016) highlight that precision agriculture can contribute to food security and support environmental sustainability of farming. Likewise, Walter et al. (2017) put that “ICT and data management can provide novel ways into a profitable, socially accepted agriculture that benefits the environment (e.g., soil, water, climate), species diversity, and farmers in developing and developed countries” (p. 6150). Poppe et al. (2013) add that ICT development has a large potential impact on sustainability, resource efficiency, food safety, and waste generation and management. Meanwhile, FAO et al. (2017) enumerate as benefits of ICT improving market access, enhancing agricultural extension and advisory services, improving preparedness of farmer and reducing climate change-related risks, increasing food safety and traceability and facilitating certification, fostering financial inclusion of rural communities, improving access by smallholders to insurance and risk management products (e.g. market-based index-insurance products). Focusing on the specific benefits of PA, authors (FAO et al. 2017) put that “While the main incentive to adopt Precision Agriculture (PA) methods is to maximise profitability, it can also tackle health and safety issues as well as reduce environmental impacts of farming practices” (p. 9).

The role of ICTs in improving system efficiency is central in literature on ICT for sustainability (Caputo et al. 2018a; Dao et al. 2011; GeSI 2008; van Marrewijk and Hardjono 2003). ICTs have been for a long time also used to increase resource efficiency and productivity in agro-food systems (Berti and Mulligan 2015; Svenfelt and Zapico 2016; Thöni and Tjoa 2017). Indeed, ICT can help reducing the use of agricultural resources and inputs (pesticides, fertilizers, energy, water) as well as decreasing environmental externalities (Lehmann et al. 2012). Smart farming is considered to have a real potential to deliver more productivity and sustainability in agriculture by fostering a more precise and resource-efficient approach especially in the use of inputs and resources (e.g. water, energy) (Smart AKIS 2016).

The reduction of input use thanks to variable rate technologies, that are an integral component of precision farming, has positive impacts, not only environmental (Balafoutis et al. 2017; Bora et al. 2012; European Union 2017; Hedley 2015; Mutchek and Williams 2010; Saidi 2013; Schrijver et al. 2016) but also economic (Balafoutis et al. 2017; Batte and Ehsani 2006; Bergtold et al. 2009; European Union 2017; Hedley 2015; Jensen et al. 2012; Koch et al. 2004; Lambert and Lowenberg-De Boer 2000; Plant et al. 2000; Shockley et al. 2015; Swinton and Lowenberg-DeBoer 1998; Tekin 2010; Timmermann et al. 2003). ICT decision-support systems help producers to minimize production costs while maximizing production efficiency (Hedley 2015; Walter et al. 2017) and reducing environmental footprints of farm operations (Hedley 2015). Moreover, ICT-based smart irrigation systems can reduce not only water consumption (Evans and King 2010; HydroSence 2013; Mutchek and Williams 2010) but also greenhouse gas (GHG) emissions and carbon footprint (Mutchek and Williams 2010) thanks to reduced energy use. In addition, precision agriculture practices and technologies result in lower GHG emissions and enhance carbon sequestration ability of soils thus mitigating climate change (European Union 2014). This makes essential strategies to scale up climate-smart agriculture (CSA) (Westermann et al. 2018).

From the social point of view, it is argued that smart farming reduces the heavy workload of the farmers and farm workers, hence improving their wellbeing and quality of life (Smart AKIS 2016). ICTs can also help alleviating the big mismatch between demand and supply in the agricultural and rural labour market regarding skills and location (Poppe et al. 2013). Moreover, some scholars (Caputo et al. 2018b; Del Giudice et al. 2017; Walter et al. 2017; Wognum and Bremmers 2009; Wognum et al. 2011) argue that ICTs can increase food chain transparency. Indeed, ICT and information systems have been used to improve agro-food products traceability and food chains transparency (Kaloxylos et al. 2013).

A growing body of evidence shows that benefits can be increased by exploiting the synergies between different ICTs such as IoT, mobile and cloud-based Big Data analytics (Rajeswari et al. 2017), IoT and Blockchain (Lin et al. 2018; Shyamala Devi et al. 2019), IoT, artificial intelligence and Cloud computing (Bu and Wang 2019).

4 Landscape of Smart Farming: Market Perspectives and Main Actors

The market of smart farming is growing quickly. Precision farming technologies are taking up with an expected annual growth of 12% till 2020. The European Union (European Union 2017; Sarantis 2015; Schrijver et al. 2016; Zarco-Tejada et al. 2014) and the US are the most promising markets. Regional analysis of smart farming market performed by BIS Research (2018) shows that North America is at the forefront of the global market, with high market penetration in the United States. However, it is in the Asia-Pacific region that the fastest market growth is projected from 2017 to 2022 mainly due to the presence of economically advancing countries such as India and China (BIS Research 2018). Pivoto et al. (2018) show that China is one of the countries where most research on smart agriculture is being carried out, together with US, South Korea, Germany and Japan. There is still a divide in terms of the use of smart agriculture technologies and solutions not only between developing countries and the developed world, but also among the industrialised countries. In fact, Smart AKIS (2016) argues that while in the USA possibly up to 80% of farmers use some kind of smart farming technologies (SFT), in Europe it is no more than 24%, despite an increasing attention to precision agriculture (Schrijver et al. 2016). Moreover, smart farming technologies are still not affordable for the large majority of farmers (especially smallholders) in developing countries. FAO et al. (2017) put that “The digital divide between developing and developed countries is nowhere more evident than in agriculture” (p. 9).

In a review on smart farming, with a particular focus on Big Data, Wolfert et al. (2017) put that “The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data…” (p. 69). Indeed, different stakeholders (from public, civil society and private sectors) shape the smart farming arena and the market is becoming more competitive. BIS Research (2018) profiles 24 leading companies engaged in precision agriculture: AGCO Corporation, Boumatic LLC, Ag Leader Technology, Dairymaster, CNH Industrial, DeLaval, Deere & Company, Fullwood Ltd., DICKEY-john, Philips Lighting, Raven Industries, Osram Licht AG, SST Development Group, Cree Inc., The Climate Corporation, General Hydroponics, Topcon Positioning Systems, AKVA Group, Trimble Inc., XpertSea, Afimilk Ltd., Eruvaka Technologies, Allflex USA Inc., DJI Innovations. Moreover, different international organisations are actively involved in the promotion of e-agriculture and smart agriculture through, among others, the development of resources. These include FAO (FAO 2013, 2015, 2019) and the World Bank (World Bank 2012). Different platforms and communities of practice facilitate the exchange of experiences and information on ICT use in agriculture e.g. e-Agriculture Community of Practice (run by FAO)Footnote 1, ICT Observatory and ICTUpdate (of the Technical Centre for Agricultural and Rural Cooperation)Footnote 2, InfoDev (a project of the World Bank)Footnote 3, ICT in Agriculture Sourcebook (provided by the World Bank)Footnote 4, FAO-ITU e-Agriculture Strategy Guide (FAO et al. 2017).

5 ICTs in Agriculture: Potential Negative Impacts and Challenges

ICTs come not only with opportunities to farmers and farming communities but also certain challenges. Davies (2014) highlight that ICT solutions, when developed in isolation from practices and realities of producers, run the risk of hampering transition towards sustainability in agriculture. Walter et al. (2017) put that “Only by considering new technologies in conjunction with a diversity of crop and livestock systems, as well as the relevant markets and policies, can farming in the digital era become smart farming” (p. 6149). Data ownership is a key concern with digital technologies use (Berti and Mulligan 2015; Walter et al. 2017) as well as cyber security (Barreto and Amaral 2018) and privacy (especially regarding Big Data and IoT) (FAO et al. 2017). The potential data misuse creates additional ethical and legal challenges for smart agriculture monitoring and regulation (Charo 2015). Moreover, as there must be accountability for mismanagement, challenges of accountability and responsibility will intensify with emerging smart agriculture technologies. Furthermore, agriculture digitalization might affect employment opportunities and job profiles in farming even further (Walter et al. 2017) with far-reaching implications in rural areas. There are also some concerns about high energy consumption and carbon footprint that imply the use of modern ICTs such as the IoT (Popli et al. 2019).

A host of challenges should be addressed to increase the uptake of ICTs in agriculture (Sylvester 2013, 2015). Profitability is a key factor hampering widespread adoption of ICTs in agriculture as it is not always easy to demonstrate that ICTs uptake will improve farm profitability (Mintert et al. 2016). High adoption costs for individual farms and limited skills and knowledge can represent important adoption hurdles (Baumgart-Getz et al. 2012; Kutter et al. 2011; Pivoto et al. 2018), especially for small-scale farmers in developing countries. Therefore, the benefits of ICT and smart agriculture technologies might be limited to industrialized countries and widely grown crops (e.g. wheat, maize, rice) (Walter et al. 2017), which may widen the North-South technology divide.

Wolfert et al. (2014) enumerated several bottlenecks of ICT development in agriculture and, consequently, of the spread of smart agriculture, such as regional focus and cultural differences, small-scaled and isolated software development, difficult or impossible interoperability between various systems, complicated handling and integration of large data amounts. Schrijver et al. (2016) and Pivoto et al. (2018) highlight that the adoption of precision farming technologies can be a challenge for farmers as they may need to acquire new knowledge and skills. Besides capacity (cf. farmers’ skills and human capital), several factors hinder the integration of ICT in agriculture in the Global South, such as connectivity, content (e.g. language, cultural appropriateness) and cost (FAO et al. 2017; IDEV 2016). Therefore, ICTs, and smart agriculture technologies, need to be tailored to use contexts and be developed in collaboration with beneficiaries and end-users to make sure that they are accessible, adequate and relevant (FAO et al. 2017; IDEV 2016; World Bank 2017). FAO (Kolshus et al. 2015) identified several challenges in making ICTs accessible for farmers such as capacity development (ability to effectively use technologies); content (adaptation of content to farmers’ needs); technologies (challenge of identifying suitable technologies). In general, women, older and poor farmers, and people living in remote rural areas have difficult and limited access to ICTs (FAO et al. 2017; Kolshus et al. 2015). The E-agriculture Strategy Guide (FAO and ITU 2016) provides a framework to address the ICT opportunities and challenges for agriculture in a more efficient way. Challenges include ICT access challenges (cf. coverage, quality, costs) posing hindrances to their adoption and use, cybersecurity, e-waste. Likewise, the Overseas Development Institute (ODI), the UK Department for International Development (DFID) and FAO provided policy recommendations to foster the role of ICT in supporting agricultural development and rural livelihoods (Chapman et al. 2003): building on existing systems, while encouraging integration of different technologies; determining who should pay; ensuring equitable access to marginalised groups and rural population; promoting localised content; building capacity; using realistic, suitable technologies; building knowledge partnerships to ensure that knowledge gaps are filled.

6 Conclusions

ICTs can play a central role in transition towards smart and sustainable agriculture, which is deemed necessary to address the challenge of growing food demand in the ages of changing climate and increasing resources scarcity. New digital technologies and services help farmers to reduce inefficiencies and deliver greater efficiency in resource use. However, some precautions should be taken to make the ongoing ‘digital revolution’ in agriculture inclusive for disadvantaged categories such as small-scale farmers and women, especially in developing countries. In order to maximize the benefits of ICT-based smart agriculture, also in developing countries, it is necessary to develop services and applications that are relevant, locally appropriate, user-friendly, and affordable. Also, the interoperability of these technologies should be addressed to optimize their synergistic effects. Moreover, investment (public, private, and public-private) is needed to move the technologies from ‘proof-of-concept’ to ‘proof-of-work’ stage. Aside from the benefits of digital and smart technologies, there are also threats and challenges that need to be appropriately addressed. For that, there is a need to develop policies supporting the necessary market and legal architecture for ICT and smart farming, with due consideration of emerging ethical issues. Governments can enhance widespread uptake of ICTs in agriculture and rural areas by facilitating access to soft and hard infrastructure. Meanwhile, opponents and advocates of ICT – across policy, practice and science – need to engage in an open, sincere dialogue on the future of farming in the digital era.