1. Introduction
Digitalization redefines people’s routines and can produce positive effects from both economic and environmental perspectives. However, as the literature stresses [1,2,3], the digital transformation could also cause social and ethical issues. The impacts of digitalization are investigated in the scientific literature to identify potential and controversial aspects to govern its settings or to adapt policies and practices [4]. A wide range of the literature contributes to unfold the impacts focusing on use cases based on the use of specific technologies (e.g., blockchain, artificial intelligence, and Internet of Things) and applied to specific scenarios (e.g., agriculture, energy, and commerce). What seems to be missing is a more comprehensive analysis that proposes a taxonomy of the impacts due to the introduction and use of digital technologies. There are indeed a few and recent attempts in the literature aiming at shedding some light on the impacts of digital technologies [4,5,6,7,8,9].
This paper aims at proposing some considerations on the digital, socioeconomic, and ecological impacts of digitalization in agriculture and rural areas. The main research question is unfolded in three levels: What are the main areas of impacts identified by scholars in agriculture and rural areas? What are the outcomes? What are the connections between digital solutions and impacts? Thanks to a thorough literature review, this paper identifies the main areas of impacts and proposes a summary of those. Thus, the main purpose of this work is not to provide a theoretical analysis of technological innovation per se built on existing theories but rather to identify, classify, and describe the potential impacts of digital technologies in rural areas and agriculture reported in the scientific literature. Through this analysis, we propose tools (the taxonomy in Section 5 and a grid in Section 6) useful to policymakers and other users to reflect beforehand on the potential impacts due to digital technologies. The rest of this work follows this structure.
Firstly, we propose a brief reflection on technological innovation theory and present the analytical framework carried out in the H2020 DESIRA (Digitalization: Economic and Social Impacts in Rural Areas—
2. The Digital Innovation
The literature on technological innovation is quite vast, and it includes different disciplines, such as economics, sociology, and psychology. These studies highlight diversely the social role of technology, how and why it emerges, how it can (or not) spread and be adopted by agents, what type of impacts can be forecasted, and other aspects. Theories on the process of technological innovation evolved from linear models—which prioritizes the scientific research in the innovation process, underestimating the role of later players in both diffusion and adoption steps—to systemic perspectives [10,11]. The latter, considering some theories rather different in both ontological and epistemological sense (e.g., the multi-level perspective [12]; the actor-network theory [13]; or the social practice theory [14]), emphasizes the knowledge flows among actors in the technology adoption process, their expectations, and the culture of technology. Market developments are considered, but also policies and the institutional structures as factors that promote or limit technological development, its diffusion, and its adoption. In short, these theoretical perspectives tend to emphasize the role of multiple agencies and distributed learning mechanisms in technological changes focusing on inter-organizational networks in which the innovation develops [15,16]. In short, technological change is not considered just in terms of “physical” inventions or developments, but as a process interacting with changes in people’s behavior and the institutional and socioeconomic structures (the market, firm organizational forms, consumer preferences, policy goals, actors’ skills, and knowledge).
The systemic perspectives shed some light on the activities, connections, and contextual conditions that foster or hamper innovations to manage the innovation processes themselves [17]. They also allow the reader to observe how innovation affects multiple socioeconomic spheres. Since the classic contributions of Schumpeter’s and Kuznets’ models [18] or even Marx’s perspective [19], it was reported how innovation can be disruptive for socio-economic systems, particularly novel technologies [20]. Precisely, some technological innovations can be defined as game changers [21], because their adoption deeply transforms the wide context in which both routines and interactions take place due to their connection with multiple elements of the socioeconomic system. As it was stressed [22,23], due to their multiplicity of intersections, game changers may trigger the unpredicted emergence of novel social phenomena or cascading effects because of the reorganization of processes in socioeconomic systems that react or adapt to the game changers.
Following the previous considerations, a systemic perspective was adopted in DESIRA, of which this work is a first outcomes. The aim is to analyze the ongoing digitalization process in rural areas and agriculture to strengthen the capacity of society and political bodies in responding to the digital challenges. It is important to note that there is a distinction between the terms “digitization” and “digitalization” due to their substantial different impacts (on this: DESIRA Digital Transformation:
The complexity generated by digitalization and its present and future challenges in literature was also analyzed applying the concept of the social–cyber–physical system (SCPS) (on this: DESIRA Socio-Cyber-Physical System:
3. The Promises of Digitalization in Agriculture and Rural Areas
UN forecasts that in 2050 the food system will need to nourish more than 9 billion people all over the world [31]. To feed everyone without compromising the entire ecosystem, it is urgent to intervene by redesigning an efficient and sustainable food production system. At the same time, rural communities suffer from several problems (difficulty in reaching markets, ageing, depopulation, lack of public and health services, etc.) that can also negatively affect sustainable food production. Considering these concerns, reports [6,32,33] suggest that the digitalization process can contribute to both agriculture (e.g., contributing to efficient use of resources) and rural communities (e.g., defining new and enriched services) sectors. At the same time, it is also stated that digitalization can contribute to achieving the UN Sustainable Development Goals (SDGs) in rural areas, the 17 interlinked goals such as “no poverty”, “zero hunger”, and “climate action” [31,34]. Despite the positive picture, some scholars suggest that to promote digital transformation, international institutions and policies underestimate the social complexity of these technologies and the possible undesirable effects. Lajoie-O’Malley et al. [35] observe that the way digital technologies can transform socioeconomic context is affected by hopes, imaginations, and visions regarding the role that these tools might play for social actors particularly according to the main international agencies (e.g., FAO, World Bank). The dominant narrative of these organizations seems to support the status quo of global industrial agri-food systems. Academics argue that international agencies assume a neo-Malthusian narrative to agricultural issues and a technological optimism as the solution. In short, there is a mismatch between population growth and food availability, which they assume can be solved by technological innovation, optimistically leading not only to technical, but also to social, political, and even moral progress and environmental protection.
The growing literature reports that digitalization could produce unexpected and negative outcomes, and a too simplistic picture cannot be used to govern such a process. Salemink et al. [36] observe that initiatives to promote digitization in rural contexts are mainly based on a free-market rationale without considering contextual specificities (e.g., private investments, digital skill levels, and trust in technology). For these reasons, policies can contribute to an increase in social exclusion for fragile actors, such as elders or low-educated people, or forms of dependency by digital providers that control both technologies and collected data. In this perspective, Rotz et al. [37] report that automatized agriculture significantly improves the lives of farmers and workers who can utilize digital technologies, creating new job opportunities, but also a radically bifurcated labor market increasing social asymmetries. Therefore, on the one side, there are highly skilled, highly trained digital workers that increase productivity and efficiency and, on the other side, the lower-skilled workers in the fields, greenhouses, and warehouses, which are subjected to increased scrutiny and surveillance, further rationalization of their workplaces, and ever-escalating expectations of productivity. These low-skilled workers are at risk to be replaced by robots and automatized solutions. Moreover, digital tools could not contribute to reach the SDGs on climate and environment according to [38]. They state that the massive use of digital solutions could increase the world electricity demand up to 20% by 2030, and that without changes in the energy sector (increasing renewables and energy efficiencies) the ecological footprint of human activities will grow considerably.
These considerations suggest that the relevance of reflections on ethical and normative aspects of the digital transformation in agriculture and rural areas have not yet reached satisfying conclusions [8,39]. How and which data should be collected and shared, what are the benefits and for whom, and other questions could be answered by engaging relevant stakeholders in a participatory process anticipating future farming trajectories to define guidelines and norms to implement digital tools. For this purpose, studies on the impacts of digital agriculture are pivotal. They figure out possible impacts supporting the elaboration of adequate solutions. The literature on this issue is quite recent. Works can be roughly divided along a continuum wherein they analyze digital impacts based on the specificity of the considered technologies and the considered scenarios. On the one hand, some studies examine the effects of digitalization as a factor for the optimization of activities (e.g., seeding and milking) and resources (e.g., water, energy, land, and fertilizers) to achieve economic or environmental benefits. In these cases, scholars take into consideration a specific set of digital tools, such as sensor nodes [40], WebGIS [41] or Internet of Things–based solutions [42]. On the other hand, some works report changes in distinct wider contexts, such as farming or rural communities, considering a small set of technologies, such as robots [43] or digital platforms [44]. Here, scholars take into consideration not only the effects on production processes, but also on sociotechnical dimensions, such as privacy, data ownership, or lack of digital skills. In other cases, scholars propose general considerations on digital transformation proposing an analysis in specific application scenarios—an application scenario can be defined as the way users interact with a digital system in each context—such as farming [45], agriculture knowledge [46], or even wider contexts. For instance, by considering the consequences as anticipated by media, experts, and farmers for the digitalization process [9] or the perceptions of digital risks by key governance actors [47]. These studies highlight the complexity of the interrelated socioeconomic and environmental impacts, as well as ethical dimensions in digitalization.
Said analyses make it difficult to identify a comprehensive and detailed overview of the impacts of digital technologies. This consideration is based on the fact that, on the one hand, there is a general attitude to focus on very specific technologies, sectors and dimensions (e.g., sensors for reducing costs in milking), which are analyzed by using a quasi-experimental method or interviewing experts and stakeholders; on the other hand, other analyses (e.g., on agriculture and on digital tools) are based on a literature review or on illustrative examples. In both cases, they offer useful information about digitalization, but they lack the design of a summary of impacts to anticipate the possible effects of the digitization process. As Klerkx et al. [7] observe, the intricate relationship among different entities involved and interconnected in the digitalization process (plants, technological tools, biological processes, cultural aspects, etc.) Klerkx et al. [7] propose the notion of social–cyber–physical–ecological systems to grasp a new social ontology generated by the intricate relationship of entities involved and interconnected in the digitalization process) needs a more comprehensive picture to address questions still under inquiry (such as the complex socioeconomic and environmental impacts as well as ethics and policy issues).
Following these considerations, we propose in what follows a taxonomy of the impacts to unravel the complexity of digitalization. Our proposal consists of a classification of the impacts based on a systematic literature review, scrutinizing the outcomes of digital tools in the main areas of impacts for agriculture and rural communities. The aim is to provide a tool in the form of a grid, which can give practitioners (e.g., farms, rural communities, rural workers, and officials) the possibility to identify potential impacts (both positive and negative, depending on the specific context) and connections between digital solutions and their possible uses in specific application scenarios. In this sense, our tool can stimulate a preliminary reflection on possible outcomes, and also support the design of policies to address side effects.
4. Methodology
Among the plethora of different analyzed methodologies to conduct a literature review, the standalone one [48,49] is used in this work. Such an approach enhances the analysis of the existing literature upon a specific and identified subject while aggregating or interpreting expressed concepts [48]. In fact, the present work aims at answering the research questions anticipated in the introduction by reviewing the most relevant literature. The aim is in increasing the understanding of the actual and forecasted outcomes of digitalization in agriculture and rural areas. To do so, an inductive thematic analysis [50] was conducted to answer the research question and achieve a comprehensive, not yet exhaustive, overview of the impacts.
The identification of relevant literature was performed by selecting both grey and white literature published between 2015 and 2021 in English only. Following literature indications [51], we used a search query using specific keywords and their combination on Scopus, Google Scholar and in the University of Pisa library system. The used keywords are “digitalization” (or “digitization”) AND “impact” OR “effect” OR “outcome” AND “agriculture” OR “rural area”. The papers were selected based on their pertinence with respect to the research questions by taking into consideration titles, abstracts, and keyword. In total, 130 works were preliminarily selected and then reduced to 35, because they provide a description of digital solutions associated with socioeconomic and environmental impacts, as well as application scenarios and contexts. The impacts were identified by conducting a qualitative inductive thematic analysis [50] and then clustered. The clusterization were performed to homogenize the language. The relation among the documents and the identified impacts (which were possibly renamed during the clustering process) is carefully maintained, and each word reports the references from which it was extracted before being clustered. The collected literature works were organized (see Supplementary Materials Table S1) by indicating the “issue analyzed”, meaning the main object of the document; the “digital tool”, meaning the technological instruments application and utilization (Digital tools are varied. To reduce this complexity, a synthetic classification is used in DESIRA:
Finally, to build the overview of the impacts, which we refer to as the taxonomy hereinafter, aggregation was performed through a conceptual analysis of the out-comes and of the effects described in the literature. Three different layers were identified: 4 “domains” (layer 1), 14 “areas of impact” (layer 2), and 61 “outcomes field” (layer 3) (see Figure 1 and Supplementary Materials Table S2).
The “domains” represent macro-dimensions where the digitalization take place; the “areas of impact” refer to sub-dimensions within each domain, identifying several areas where the clustered effects are allocated. For both clarity and comprehensiveness purposes, the terms were labeled (see Supplementary Materials Glossary S3), using the definitions found in the literature and therein cited.
Through the elaboration, an exercise to connect the elements of the taxonomy to the digital technologies is proposed as well, as reported in the grid (see Table 1). This grid may help to stimulate reflections on possible outcomes in specific situations and contexts. In the grid, the taxonomy is reported together with some digital technologies to show plausible connections. To suggest how the grid could be utilized by academics and practitioners when analyzing a theme (e.g., labor opportunities, ecological footprint) in the field of digitalization of agriculture and rural areas, two examples are provided in Section 6. The two topics were chosen and analyzed based on the literature review, leading to the description of the impacts that a specific technology could generate in different domains. In the conducted analysis, ethical issues are not explicitly reported, even though any change by digital tools implies ethical issues to be considered [5,8,43,52]. On this, some insights are discussed.
5. A Taxonomy of Digitalization Impacts
As previously mentioned, the literature review aims at answering the identified research questions to provide a summary of the digital impacts. As anticipated, three different layers are identified: “domains”, “areas of impact”, and “outcomes field” (Figure 1). The study outcome is reported by following the structure of the research question. The starting point is as follows: What are the main domains and related areas of impacts of digitalization as identified in the literature in agriculture and rural areas? What are the involved outcomes?
Based on the literature review on digitalization impacts (e.g., [7,43]), it is possible to identify macro-dimensions, as to say the coherent summary of the socioeconomic and environmental spheres modified by the digitalization process. It helped us to specify more analytically the set of the digitalization impacts and outcomes. Through it, four macro-dimensions are identified—the “domains”—opening to an analytical description of the digitalization impacts and outcomes. The “domains” are “economic”, “environment”, “governance”, and “social”. In the literature, the “governance” dimension is considered as part of the “social” one, but we separated them to emphasize the former. Moreover, for each domain, different “areas of impact” were specified. These areas referred to sub-dimensions to offer a more detailed level of analysis. They should not be considered as exhaustive, anyway. Supplementary Materials Glossary S3 contains a brief definition of each term and some examples about what the areas refer to, along with references to the considered literature. Similarly, for each area of impact, several possible outcomes are indicated starting from the results emerged from the literature review and the approach proposed in this work. Furthermore, a brief description of the taxonomy and of the outcomes is provided through some explanations.
The first and largely mentioned dimension in the literature is the “economic” one. It refers to all the activities and resources used for the production and provision of a good or service. It also includes the income and value-chain, involving the working conditions and decision-making processes. Under the economic label, three areas of impacts are identified and defined as follows:
-
Organization, the working flow and management activity related to the production process;
-
Value chain, the “sequence of activities that a firm undertakes to create value, including the various steps within the supply chain but also additional activities, such as marketing, sales, and service” (Deardorff’s Glossary of International Economics in
https://iate.europa.eu/search/standard , accessed on 8 March 2021); -
Markets, places where parties gather to exchange goods/services defining prices.
Digitalization can produce relevant outcomes in this dimension and related impact areas. As it is emphasized [38,53], Artificial Intelligence, for example elaborating data collected by in-field sensors, satellites, or drones about soil moisture, weather conditions, and plants status, can suggest cost-efficient use of resources (e.g., water, fertilizers, and pesticides), thus potentially increasing production, food quality, and farm incomes. These technologies can promote users’ autonomy in farm management, but also forms of cooperation due to data sharing among users. At the same time, the possibility of processing a large amount of data and gathering information can improve organizational resilience for both producers and consumers [54], as to say, the ability to anticipate, prepare for, respond, and adapt to relevant changes for the farms. Web-based technologies, such as digital marketplaces or e-shopping solutions, can increase the market access and the bargaining power of small farmers. Possible unclear effects of digitalization need to be taken into consideration as well. In particular, digital solutions may impose an “algorithm governance” [55]. If users rely on the digital tools—which incorporate a pre-established definition of adequacy and correctness of production—farmers could lose their autonomy in corporate management. This seems particularly evident in the case of completely automatized processes, such as irrigation. Farmers may also become dependent on digital service providers, becoming the real (invisible) farm managers.
The second domain is the “environment”, which can be positively affected by digitalization [56]. It can be defined as a complex interrelation of different elements, biotic and non-biotic ones, that supports living conditions on the Earth. It includes all the natural resources—such as air, land, water, woods, etc.—and related issues such as their protection and valorization for human purposes. In this case, four areas of impacts are identified:
Animal well-being, issues related to the principle of species-appropriate housing;
Ecosystem services, benefits provided by the natural processes (e.g., pollination and clean air);
Natural resources, “natural assets (raw materials) occurring in nature that can be used for economic production or consumption” (OECD glossary:
http://stats.oecd.org/glossary/ , accessed on 8 March 2021);Risk management, “the process by which early efforts and assessments are taken to prevent environmental risks or accidents” (Deardorff’s Glossary of International Economics:
https://iate.europa.eu/search/standard , accessed on 8 March 2021).
In the case of livestock, the Internet of Things may promote, e.g., an eco-efficient cattle management [57,58]. Battery-powered sensors on collars can monitor livestock location and crucial physiological parameters (temperature, blood pressure, heart rate) opening to the possibility of differentiated feeding. This also allows controlling the cattle health, reducing the risk of illness. At the same time, digital automatized waste management in cattle farms can reduce the ecological footprint of livestock reusing waste for energy production (e.g., anaerobic digester). This process reduces the risk of pollution to groundwater and soil, also lowering greenhouse gas emissions. Similarly, on-vehicle sensors can detect the status of soil (nutrients and water), plant conditions (their needs in the vegetative cycle), and the need for pesticides, if any. They can help in estimating the correct amount of water, fertilizers, and pesticides of each field area, which can be irrigated by automatic machines (e.g., automatized sprinkler), lowering the use of chemicals that can harm, e.g., insects [59]. At the same time, some negative ecological impact can be reported. Digital tools, for example, need power to function, thus their wide-spreading use can increase energy demands. If fossil fuels continue to be used, this may have an impact on climate emissions [38]. Because of this, the promoted energy efficiency of ICT tools may be strongly reduced or completely nullified [60]. Moreover, some digital devices, such as smartphones, need to be redesigned in a recyclable and repairable way to minimize material consumption [61].
An emerging relevant domain affected by digitalization is the “governance one”, which refers to the mechanisms and the processes through which people can articulate and mediate their interests and needs while exercising their rights and obligations. For this domain, two areas of impact are detected:
Operationality, the bureaucratic and legal procedures that enable to operate;
Equity, the conditions of access to legal/norms information and administrative tools.
In the agricultural sector, many bureaucratic requirements aim to certify the quality of food production, but also to provide recognition—and thus a competitive advantage—in the market (e.g., organic food certification). At the same time, certifications and controls guarantee consumers about agricultural products. On this, digital tools can facilitate bureaucratic procedures or forms of control to ensure farmers and consumers against trade fraud [62,63,64]. Dedicated web-solution—such as apps, websites, and digital databases—can simplify the communication between farmers and public offices, offering access to valuable information and simplified bureaucratic procedures. Automatized field mapping by drones, e.g., after a flood, can reduce the time of recovery and increase the accuracy of public support. Blockchain or similar technologies can be a solution to prevent false declarations on food, for example, the place it was produced, processed, stored, etc. However, these digital solutions may prove hard to use for people with low digital skills, increasing the complexity of the relation between farmers and public bodies. Farmers can become dependent on professional or company services, especially if digital procedures are compulsory to obtain certifications or public incentives (e.g., organic food certification).
The last domain is the “social” one [5,7]. It refers to the conditions, resources and norms that define the forms of interactions between the different subjects and their characteristics, which are also defined by the social interaction and conditions in the area where they operate or live. In this case, the areas of impacts identified are five. They refer both to the social conditions in general and social resources endowed by subjects. Specifically, there are the following:
Individual, aspects and characteristic that refers to a single person;
Access, the chance and conditions to enlarge social interactions;
Rights, effects on the related issue of fundamental rules;
Social capital, the key resources, e.g., information and social contacts and related issues;
Control, the issue of the data security, utilization, and the pervasiveness of digital change.
For example, focusing on rural communities, researchers stress that the digitalization can mitigate the disadvantages in rural areas, such as the low level of public services, e.g., through telemedicine, e-commerce, and e-learning opportunities. These solutions can also enhance social relations within rural communities and between rural communities and urban areas promoting a process of social inclusion and participation. Social media and web-solutions can contribute to share information and promote discussion among the community, stimulating an identity-building process, a sense of belonging, useful to activate local resources, and abilities not only for the socioeconomic development but also for the resilience capacity [65,66]. Digital tools can be used to reduce social asymmetries in accessing resources and information to promote self-development by breaking down traditional social gaps such as the gender gap, territorial differences, age -vantages, studies on the social domain also highlight several possible negative aspects and risks related to digitalization [7,8,67,68]. Firstly, the unequal endowment of e-skills among rural workers and the lack of IT infrastructure in rural areas reduce the change to implement and use digital tools. In this sense, the benefits of digitalization can be grasped only by actors in a good social position (high knowledge and skills, good economic resources, large social relations) increasing social asymmetries and social exclusion [69,70]. Furthermore, artificial intelligence combined with big data and real-time information collected with remote sensors can estimate potential social behaviors in several contexts. It poses serious problems on privacy, data ownership, and their use. Some works stress how digitalization can define a new form of surveillance [67], which aggravate social asymmetries by defining and suggesting a specific range of standards of “correctness” in social activities.
As illustrated, although not exhaustive, the taxonomy encompasses a wide range of possible impacts of digitalization in agriculture and rural areas. In the next section, we discuss the last step needed to understand how the taxonomy can stimulate a reflection about the impacts on digitalization.
6. A Grid to Reflect on the Digitalization
Section 5 provides the information needed to answer the last question: What are the possible impacts when using digital tools? To provide an answer, the grid, combined with digital tools, offers a solution that stimulates reflections on the possible impacts of digital technologies (see Table 1) in an application scenario. The table contains the list of outcomes and the related domains and areas of impacts. Supplementary Materials Glossary S3 provides definitions and references of each outcome, thus helping in better describing the potential effects of digital technologies in each application scenario.
Thanks to these tables, it is possible to describe (1) what are the impacts that digital technology can generate and (2) what are the technologies, presently available, that can generate said impacts. In this way, the grid may be able to support a wide reflection on digitalization in rural area and agriculture. Two examples are provided in what follows.
In the first example, we take into consideration the so-called “cloud” technology. It refers to cloud computing, the on-demand computing resources such as data storage and computing power, without direct active management by users. Through the cloud, users can store, manipulate, share, and use data thanks to the provider’s services. A well-known example is Google Drive, which combines a cloud store, office applications, and data sharing options. Cloud can be combined with other digital tools or ICTs producing an extensive range of possible outcomes. Sensor nodes can send in-field data to a remote cloud for storing purposes or for analysis, triggering a sprinkling system based on the results of the data analysis. Looking at Table 1, cloud technology has sixteen possible outcomes. For example, in the impact area “organization”, the cloud has as outcomes “autonomy” and “productivity”. Farmers can become independent in decision making thanks to data collected and analyzed with the cloud services contributing to enable cost-efficiency production [7]. At the same time, cloud has as outcome “cooperation” in the domain of “governance”. This is because cloud services by public authorities may simplify bureaucracy and controls on farms by public entities, thus stimulating forms of users’ collaboration with public administrations [71]. Cloud is also linked with the social domain in the impact area “individual” and some outcomes are “skill” and “learning”. On the one hand, they may highlight that effective use of cloud technology is affected by personal skill level (abilities and competencies). High skills reduce the risk of “digital exclusion” and appear relevant to acquire new notions and knowledge. On the other hand, it indicates that the use of cloud computing can stimulate digital skills through learning opportunities that may enhance employability in the labor market or exploit the chances of the digital society.
The second example refers to expected impacts. For instance, a more efficient use of resources, such as water or soil, in a cost-effective manner. According to the glossary (Supplementary Materials Glossary S3), “resource efficiency”—in the area of impact “organization”—refers to the use of energy, materials, chemicals, water, and other essential resources for farming and all along the food-chain [32,72,73]. In Table 1, the “resource efficiency” is linked with “local and remote sensing”, “big data and analytics”, artificial intelligence, autonomous systems, and connectivity. So, the efficient use of resources can be an outcome of several digital solutions, which may be used in different ways because they are applied to specific application scenarios. A complex system that combines sensor nodes, artificial intelligence, and automated machines in a greenhouse outlines a scenario in which farmers do not actively intervene because the smart system autonomously performs the needed actions. A large economic investment and high digital skills may be necessary to actually implement such a solution. In the case of field cultivation, simple solutions may be more feasible. For example, sensors can detect the humidity of the soil, reporting it to those in charge of irrigation. For a deeper reflection on the impacts, it is useful to check which effects are connected to the technologies indicated in the grid (e.g., artificial intelligence has impacts on “resource efficiency” and on “transparency” in the value chain) considering the application scenario (greenhouse or field cultivation). This exercise forces a broader consideration on the impacts of digitalization in concrete cases, also providing hints for unintended or unforeseen outcomes.
7. Conclusions
The literature review performed in this work has unveiled that existing studies concerning digitalization in agriculture and rural areas do not provide an overall picture concerning actual and potential outcomes. In this regard, while other works investigate a limited set of plausible outcomes of digitalization, this work proposes a more comprehensive exploration of digital systems, while attempting to classify their impact [4,5,6,7,8,9].
This paper contributes to the current discussions on digital change, defining a taxonomy and a grid that could help to determine how to face both predictable and unintended digitalization effects. Several digital technologies (e.g., blockchain, artificial intelligence, and sensors) were considered, as well as a large set of applications. The main domains and areas of impacts of digitalization, as well as their outcome, were identified and described. The proposed taxonomy and the grid may stimulate a reflection on possible outcomes and support in the shaping of appropriate responses, such as policies or practices to address the side effects. Furthermore, they provide the possibility to identify potential impacts (both positive and negative) and connections between digital solutions and their possible uses in specific application scenarios. For example, thanks to digital technologies, it will be increasingly possible to determine with extreme precision which crops to grow according to market trends and when to intervene with agricultural work (ploughing, irrigation, fertilization, and pesticide treatments) considering the state of the soil, plants, and weather forecasts. Digitalization may reduce costs for farms and the environmental impact of agricultural production and improve crop yields, farmers’ income, and offering quality and safer food. However, there is a risk that these improvements will only occur for some farmers (in high-income countries but not in low-income ones, for example). Farms can become increasingly dependent on high-tech companies providing digital tools and services, or food production may become increasingly reliant on algorithms, reducing human control. The taxonomy here reported wishes to help to prefigure negatives outcomes to reduce the unexpected impact of digitalization.
However, this work has four limitations, in our view, for which further investigations and reflections are needed. First, the literature on digitalization in agriculture and rural areas is constantly updated due to new technologies and innovation systems. Therefore, the taxonomy must be continuously updated. It contains key and almost well-established technologies, but it cannot be handy to forthcoming innovations, for example, the quantum computing. Second, the grid directly identifies systemic effects. The outcomes reported can have a retroactive effect on other domains. Third, even though both the taxonomy and the grid focus on agriculture and rural areas, the work does not discuss all the possible application scenarios (which are hundreds, or even thousands). This allows for a more flexible use of the proposed tools, but the outcomes depend on the application scenario, which must be carefully identified as the first step. Fourth, ethical issues are not considered, but only briefly mentioned.
In conclusion, a first systematization of digitalization impacts in agriculture and rural areas was presented in this work. The proposed taxonomy and grid may require further elaborations, nonetheless, contributing to manage or govern the digital transformation without leaving anyone behind.
Supplementary Materials
The following are available online at
Author Contributions
Conceptualization, G.B., I.S., and S.R.; methodology and literature review, S.R.; formal analysis, G.B., M.B., S.R., and I.S.; data curation, M.B.; writing—original draft preparation, I.S.; writing—review and editing, S.R.; supervision, G.B.; project administration, G.B. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded under the terms and conditions of Grant Agreement No. 818194 for the European Commission. It does not necessarily reflect the view of the European Union and in no way anticipates the Commission’s future policy in this area.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available the Supplementary Materials.
Conflicts of Interest
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figure and Table
Table 1
The grid of digital technologies and possible set of socioeconomic and environmental outcomes.
| Domain | Area of Impact | Outcome In | Technology | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Social Media | Cloud | Local and Remote Sensing | Distributed Ledger | Big Data and Analytics | Augmented Reality | 3D Printing | Artificial |
Autonomous Systems | Connectivity | |||
| Economic | Organization | Autonomy | x | x | x | x | x | x | x | x | x | x |
| Cooperation | x | x | x | x | x | |||||||
| Financial risk | x | x | x | x | x | x | ||||||
| Incomes | x | x | x | x | x | x | x | x | ||||
| Marketing | x | x | x | x | x | x | ||||||
| Product/process security | x | x | x | x | x | x | ||||||
| Productivity | x | x | x | x | x | x | x | |||||
| Resilience | x | x | x | x | x | x | x | |||||
| Resource efficiency | x | x | x | x | x | |||||||
| Responsibility | x | x | x | |||||||||
| Transaction costs | x | x | x | x | x | x | ||||||
| Value chain | Bargaining power | x | x | x | x | x | x | |||||
| Food quality | x | x | x | x | x | x | x | |||||
| Resource efficiency | x | x | x | |||||||||
| Transparency | x | x | x | x | ||||||||
| Trust | x | x | x | x | ||||||||
| Markets | Equal opportunities | x | x | x | x | x | ||||||
| Market concentration | x | x | x | |||||||||
| Prices | x | x | x | x | x | |||||||
| Stability | x | x | x | x | x | |||||||
| Transparency | x | x | x | x | ||||||||
| Environment | Animal well-being | Animal health | x | x | x | x | x | x | ||||
| Animal control | x | x | x | x | x | x | ||||||
| Ecosystem services | Biodiversity | x | x | x | x | x | x | |||||
| Clime | x | x | x | x | x | x | x | |||||
| Natural resources | Energy | x | x | x | x | x | ||||||
| Nutrients | x | x | x | x | x | |||||||
| Plant health | x | x | x | x | x | |||||||
| Soil | x | x | x | x | ||||||||
| Water | x | x | x | x | x | |||||||
| Risk management | Prevention | x | x | x | x | x | x | |||||
| Proactivity | x | x | ||||||||||
| Governance | Operationality | Cooperation | x | x | x | x | x | |||||
| Law compliance | x | x | x | |||||||||
| Administrative burdens | x | x | x | x | x | x | ||||||
| Transaction costs | x | x | x | x | x | x | ||||||
| Equity | Law enforcement | x | x | x | ||||||||
| Participation | x | x | x | x | ||||||||
| Social | Individuals | Health | x | x | x | x | ||||||
| Learning | x | x | x | x | x | x | x | x | ||||
| Responsibility | x | x | x | x | x | x | ||||||
| Skills | x | x | x | x | x | x | x | |||||
| Well-being | x | x | x | x | x | |||||||
| Access | ICT | x | x | x | x | x | x | x | ||||
| Resources | x | x | x | x | ||||||||
| Rights | Autonomy | x | x | x | x | x | x | x | x | x | ||
| Equity | x | x | x | x | x | x | x | |||||
| Gender gap | x | x | x | x | x | x | ||||||
| Power | x | x | x | x | x | x | x | |||||
| Resilience | x | x | x | x | x | x | x | |||||
| Social capital | Cohesion | x | x | x | x | |||||||
| Identity | x | x | x | x | ||||||||
| Inclusion | x | x | x | x | x | |||||||
| Participation | x | x | x | x | x | x | x | |||||
| Trust | x | x | x | x | ||||||||
| Control | Prediction | x | x | x | x | |||||||
| Privacy | x | x | x | x | x | x | ||||||
| Security | x | x | x | x | x | x | ||||||
| Surveillance | x | x | x | x | x | x | ||||||
| Transparency | x | x | x | x | x | x | ||||||
| Responsibility | x | x | x | x | x | x | ||||||
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Brunori, Gianluca 1
; Bacco, Manlio 2
; Scotti, Ivano 3
1 Department of Agricultural, Food and Environmental Sciences, University of Pisa, 56124 Pisa, Italy;
2 Institute of Information Science and Technologies (ISTI), CNR, 56127 Pisa, Italy;
3 Department of Agricultural, Environmental and Food Sciences, University of Molise, 86100 Campobasso, Italy