Perspective Chapter: Exploring the Possibilities and Technologies of the Digital Agricultural Platform

Abstract

In connection with the significant growth of the world’s population, global socioeconomic trends in the sufficiency of natural resources have become relevant. Agriculture as an industry produces food that every inhabitant of the planet consumes daily. The introduction of new technologies, while increasing efficiency, has not eliminated food shortages throughout human history. Modern digital solutions and technologies have a positive impact on productivity. Our study presents the results of a study of the big data platform and artificial intelligence for the agricultural sector, their capabilities and optimization technologies. This chapter analyzed and proposed recommendations on the quality of data analysis of agricultural processes and business administration in real time. The active introduction of digital platforms will have a positive effect on optimizing the use of resources, reducing waste and improving the efficiency of risk management and attracting investments under the conditions of transparent management of the agricultural enterprise.

1. Introduction

The digital transformation of agriculture is largely based on the integrated implementation of digital technologies within the interrelated concepts of precision farming and smart agriculture. Managing the life cycle of living beings is much more complicated than such tasks as driving a car without a driver, a voice assistant, that is, managing the “life cycle” of any inanimate object. For example, the algorithm for growing wheat is based on the interaction of natural and climatic conditions, soil fertility, seed, fertilizers, anthropogenic factor and compliance with the technological map of the growing season. Excess or lack of water and sun, the impact of pests and diseases, and cataclysms (fire, flood, drought, war, earthquake and epidemics) are difficult to predict and cannot be artificially compensated, only reduced, and the growing season cannot be suspended or moved to another place. However, the positive moment in the interaction with man and nature is that the lack of electricity and the Internet does not affect the growing season in open systems. If favorable conditions are not met during the growing season, if unsustainable agricultural methods are used, and this is a period of 3–9 months, there may be no harvest. Crop rotation, soil analysis and maintaining its fertility, temperature and precipitation dynamics over the past 100 years, analysis of global food markets and supply chains will make it possible to predict the most appropriate development strategy at different levels in modern information and communication parameters.

Despite the fact that individual elements of precision farming have been used for more than 20 years, only now integrated solutions in the fields, sustainable resource-saving crop production that combines various types of sensors, Internet of Things (IoT) technologies, automated and unmanned vehicles, robotic production systems, platform technologies for processing large data and machine learning are largely taking place.

The main task of the digital transformation of agriculture is to extract value from the collected big data about the internal and external environment. This is based on cloud platforms and big data solutions, as well as predictive analytics technologies and decision support systems. It is predicted that by 2050, the average farm will generate 4.1 million units of data (data point) per day [1].

Reducing the cost and increasing the accuracy of sensor equipment (field sensors, sensors for monitoring the condition of industrial premises, agricultural equipment and machinery, livestock health monitoring sensors, etc.) will allow a large number of agricultural enterprises to migrate to continuous collection and analysis of information and integrate three levels of monitoring of agricultural systems (ground, air, and space) at the level of individual farms, regions, and whole countries.

The digital transformation of agriculture is aimed at overcoming a number of global challenges, such as:

• increasing food demand (up to 60% by 2050) as a result of population growth and improved quality of life [2];

• depleting productive agricultural land, increasing environmental pressure (70% of water consumption and 30% of carbon dioxide emissions are currently accounted for world agriculture) and the reduction of areas suitable for agriculture;

• changes in agroclimatic conditions and an increase in the frequency of natural disasters that increase volatility in agricultural markets;

• transformation of consumer preferences and development of a sustainable and environmentally friendly consumption model.

Integrated application of precision farming technologies can provide a yield increase to 70% [3].

A prompt response to changes in external conditions and adjustment of equipment operation parameters based on incoming digital information can reduce the cost of seeds, fertilizer and fuel, and reduce time spent on field work.

Big data analytics and artificial intelligence technologies can improve the efficiency of selection processes and the development of new effective feeds and fertilizers, provide yield forecasting and select the optimal strategy for growing crops [4].

The usage of unmanned vehicles significantly reduces the cost of certain types of work. For instance, the usage of UAVs (unmanned aerial vehicles) for planting seeds can reduce the cost of this operation by 85% [1]. According to the American Farmers Association, cost reduction due to robotization of agricultural operations can reach 40% [5]. At the same time, labor productivity is also growing—for example, one robotic harvesting system can replace 30 farmworkers [5].

As a result of digitalization, consumers and regulatory authorities will have the opportunity to fully trace the origin of products [6], which will increase their safety, and will also become an additional factor in the development of consumer culture. Digital technologies will help to reduce the environmental pressure on agriculture, improve the efficiency of natural resources usage, and increase the resistance to adverse agroclimatic phenomena [7].

Digitization in agricultural sector makes it possible to achieve a number of indirect and social effects, including reducing disparities in the quality of life between urban and rural areas, ensuring the economic and social integration of smallholder farmers into food systems and supply chains (including through various marketplaces), providing rural residents with tools to increase digital literacy and expanding the set of competencies.

2. Facilities of modern technologies for digital agriculture

It is advisable to group modern technologies for digital agriculture, which are used by agro-industrial enterprises and enhance the digital transformation of agrarian production. Among these technologies, four main blocks can be distinguished:

• aerospace technologies;

• sensors and transducers;

• artificial intelligence;

• information and communication technologies.

It is necessary to analyze each block of individual digital technologies in terms of the facilities and benefits of practical use (Table 1).

Technology and its importance for the agricultural sector	Technology components	Possibilities
Aerospace. The basis of precision farming	Unmanned aerial vehicles, drones	measurement of land plots; visual control and monitoring; liquid fertilizer application and plant protection products;
	Satellite images	surface scanning, multispectral shooting of agricultural lands; guaranteed daily field monitoring;
	Geographic Information Systems (GIS)	centralized storage and management of the cartographic database; land management, audit implementation of agricultural land; control of work execution in the fields and tracking of crops in the crop’s context; evaluate the quality of soils and their potential yield;
Internet of Things (IoT). The basis for automation of production processes in the agricultural sector	Sensors and transducers	For crop production: tracking soil parameters; tracking plant growth parameters; finding pests and diseases of plants; monitoring of weather conditions; tracking machineries in the fields. For animal husbandry: biometrics of animals, automatic systems of feeding and maintenance of animals.
Artificial intelligence. The basis of SMART farms in the agricultural sector	Machine learning	Identification of relationships to minimize losses and increase the efficiency of production resources.
	Robotics	Automatization of repetitive operations: harvesting, weed control, pruning, sowing, sorting and packing, milking, etc.
	Big Data	in-depth analysis of business processes in the farms of the agricultural sector to make informed management decisions; creating a profile of a successful producer to develop an algorithm for the sustainable production of agricultural products;
Information and communication technologies.; The basis of effective management and decision-making in the agricultural sector	Digital platforms, mobile applications	remote control of agricultural land; management of field works that need constant updating of information; planning and forecasting of agricultural operations; tracking the operation of equipment in the field in real time; automation of the workflow process; organization of a system of general access to files of an agricultural enterprise;
	Messengers, social networks	internal communication at agricultural enterprises and farms; exchange of experience between participants in the production process

Table 1.

Modern technologies for digital agriculture.

Source: Compiled by the authors.

Space technologies, including new possibilities of satellite images like space sensing and multispectral imaging, are designed to help solve the problem of precision farming. This approach involves caring for each individual square of the field through three key areas: surveillance systems, variable rate fertilization, and navigation. Namely in areas all these innovative changes that solve the main problems of agricultural enterprises in the fields and gradually draw the agro-industrial sector into the Information Technology (IT) industry are taking place.

The integrated use of space technologies in the practice of agricultural enterprises makes it possible to realize the benefits of precision farming using satellite navigation devices, space images, special software and satellite monitoring of agricultural land. The practical implementation of precision farming concept by agricultural enterprises requires the development of a decision support system, adapted to technological map of cultivation, based on the use of satellite navigation, Geographic Information Systems (GIS) technologies, remote sensing data, agricultural robotic devices and software.

The use of space technologies by agricultural enterprises makes it possible to control the development of plants. With the help of satellite images, it is possible to build maps of plant development (Normalized Difference Vegetation Index (NDVI) maps), determine relief features of the enterprise land bank and the agrochemical composition of the soil cover, which makes it possible to apply different agricultural technologies to each section of the field.

The next block of digital agriculture technologies consists of various sensors and transducers that are used to automate production processes in the agricultural sector.

The employment of intelligent sensors and transducers, whose job it is to gather data about the environment and agriculture in real time, is directly tied to the majority of the production activities that comprise modern precision farming.

In precision farming, sensors and transducers are categorized into five classes.

The first group of sensors and transducers provides the maximum accuracy of crop yield monitoring based on the measurement of the following parameters:

• weather conditions (measurement of wind speed and direction, amount of precipitation);

• soil morphology (water potential, temperature and oxygen level in the soil);

• the need for fertilizers (determining the required amount of fertilizers by measuring electrical resistance);

• solar radiation (measurement of ultraviolet and short-wave radiation, radiation for photosynthesis);

• plant growth (dendrometers to control the growth of the root, stem and fruit).

The second group consists of agricultural sensors for determining the vegetation of plants with high accuracy. Usually, a video camera with such sensors is installed on a drone or an unmanned aerial vehicle. It can collect real-time visual data of the condition of crops in the fields, and it helps agronomists to identify problems with crops at an early stage and with great accuracy.

The third group consists of sensors for determining the soil, which analyze its condition and the required amount of water in it. The feature of the soil sensors is the ability to determine the necessity for watering in different parts of the field. This prevents waterlogging and washing out of useful microelements during artificial irrigation.

The fourth group consists of sensors that allow you to monitor the presence of pests in a certain part of the field. If the identified indicators exceed the critical number of the pest population, then the information is transmitted to the agronomist to make a decision on the localization or elimination of this problem.

The fifth group of sensors are digital IoT tools that allow you to accumulate all the information from the first four groups. They process incoming information and ensure the timeliness of its processing for management response and troubleshooting.

All of these digital technological solutions are sources of information, so there is a need to process and analyze a large amount of information in order to make effective decisions and develop forecasts. For such information processing, Big Data technologies are used, which are the main elements of modern SMART farms in the agricultural sector. In the making of data processing, machine learning (ML) technologies are used, which, based on the analysis of large information amounts, reveal new relationships in the activities of agricultural enterprises, which allow us to make informed management decisions.

Artificial intelligence (AI) technologies that are used in agriculture have a number of significant features:

• these are technical solutions, primarily software and hardware tools for performing certain agricultural work or forecasting the development of the agricultural sector, depending on various factors (climate, soil conditions, rainfall and market prices). Often AI technologies are used in conjunction with robotics, here we can talk about their interaction. So, the robot provides movement, manipulation of objects and tools, and AI technologies, in turn, carry out orientation in space, choose the optimal tools for the robot when performing a certain task and also help to recognize obstacles and objects;

• these technologies are used for agriculture, i.e., directly in the production of food or the development of an optimal agricultural management strategy. This means the need to take into account the functioning in changing natural and climatic conditions; working with living organisms—plants, animals; and functioning in livestock buildings or open areas, which makes it necessary to navigate in space, often with pattern recognition (various unsorted objects);

• these technologies perform intellectual functions in the implementation of work in agriculture, which consist in the ability to carry out abstract conclusions, act in conditions of incomplete information and show the ability to self-learn.

Information and communication technologies are actively used in agriculture nowadays and include the use of digital platforms, mobile applications, instant messengers and social networks.

3. Cloud technologies as the foundation for building digital platforms

Nowadays, technologies for providing information and communication services are being improved continuously, opening up new opportunities for business. The most promising and flexible are modern “cloud” and “cloud computing” technologies.

The “cloud” technology model is a complex infrastructure that is created on the basis of building a data center (a building that houses server and network equipment with Internet access channels) [8].

The National Institute of Standards and Technology (NIST) defined in the document “NIST Definition of Cloud Computing v15” [9] the cloud computing technology model as follows: the cloud computing model enables convenient network access on demand to cloud functions using communication channels.

The cloud model is characterized by five main features:

• service on demand;

• wide network access;

• pooled resource;

• independent location;

• fast flexibility, changeable services.

Today, there is a choice of four cloud hosting models Functions as a Service (FaaS), Platform-as-a-Service (PaaS), Software as a Service (SaaS) or Infrastructure as a Service (IaaS), which is based on traditional data centers [10]. It should be noted that in fact, this is mostly a choice of three options, since the FaaS model is still experimental both for online trading and for other business areas. In general, choosing a FaaS, PaaS, SaaS or IaaS cloud model to build, for example, a digital business platform is a decision with many criteria, and each model has its pros and cons depending on business practices and the properties of the software itself (Figure 1) [11].

Figure 1.

Cloud hosting models for organizing digital platforms. Image source: Modified by the authors based on [11].

As part of the IaaS model (Infrastructure as a Service), the data center provides dedicated servers or virtual machines, as well as data storage and network connectivity to backbone Internet links. The client (customer of the service model), for instance, a large agricultural company pays for the services of the provider according to the operating expenses model (OpEx) [12] depending on the current consumption of Pay-As-You-Go, or, in other words, as a subscription. This payment method frees the budget of the agricultural company from capital expenditures (CapEx) for the purchase of its own servers and other IT equipment. That payment method is applicable to any of the cloud models FaaS, PaaS, SaaS or IaaS [10, 13].

The advantage of the IaaS model is a fairly high level of control over equipment and applications, especially if the agricultural company has a professional IT team. However, on the other hand, this is also a disadvantage, since the company bears the costs of paying programmers, network engineers and network administrators. If the IT staff of the agricultural company makes mistakes while configuring the servers, storages, network stacks, installing or updating software, the agricultural company incurs losses due to denials in servicing user requests [10].

In modern data centers, the IaaS cloud model allows you to flexibly scale leased IT resources in accordance with the progress of projects, increase or decrease in load. Unlike other models of cloud services (FaaS, PaaS or SaaS), a feature of IaaS is the provision of leasing only physical devices and a network connection to the Internet. The software for the IaaS model is installed by the customer company independently, and the data center is not responsible for the operation of these applications [10, 13].

The IaaS model is used primarily in the field of e-commerce, and it is most suitable for online stores and marketplaces, where owners want to have control over their business’s IT infrastructure, website and other online applications, but do not want to invest a lot of money in creating their own data center (or server room) [13].

In the PaaS (Platform-as-a-Service) model, there are changes in the composition of the leased infrastructure. It is no longer provided only as a set of servers, but also provides preinstalled operating systems for the client’s choice, tools for creating, testing and running applications. The convenience of the PaaS model is that the client does not need to administer system software. There is complete freedom in which applications, software and development tools are deployed on the leased infrastructure. The client can create and upload his own code, install and administer databases.

Using the PaaS model, the vendor, in addition to the actual sale of services and platform applications, also provides services for its installation on its hosting in the data center. In other words, the vendor rents capacity in one of the data centers and creates a script for automatically installing its digital platform software on a virtual machine or dedicated server, depending on the subscription plan [11].

In the PaaS model, the client of this service retains the possibility of significant customization of the basic digital platform solution. If you need to add unique features to implement business processes without waiting for them to appear from the platform provider, then PaaS is the best choice [10, 13].

The SaaS (Software as a Service) model is a cloud-based model in which the provider takes over all the tasks of deploying and maintaining applications. This includes cloud server hosting, operating system installation, virtualization and backup tools, middleware and application software. The SaaS digital platform solution is developed and deployed on the hosting, and is also serviced entirely by the vendor. This is a huge convenience for small and medium-sized companies that are looking to get to market faster with their selling propositions and are unable to hire an IT team that includes developers, network engineers and administrators.

The application according to the SaaS model is deployed on the hosting, and is also serviced entirely by the vendor. This is a great convenience for small to medium businesses. Business users can buy access to SaaS digital platform services (applications) directly from a web browser (or mobile device) [10, 13].

Functions as a Service (FaaS) represents a new level of abstraction, in which the physical and software architecture completely disappears, except for individual “functions” that are called when there is a need for their execution. A function in FaaS has the highest level of abstraction as the smallest component (brick) of a large application. Although FaaS is often fully associated with serverless computing, it is correct to speak about FaaS as a software part or software component of serverless computing, as viewed from the user’s point of view [14].

With FaaS, a development team can break down their application into separate functions and upload them to the FaaS platform. Such platforms are offered by global cloud service providers—Microsoft Azure, AWS Lambda, IBM Cloud Functions and Google Cloud Functions.

The FaaS model is very economical, since the customer of the service pays only for the server time required to call and execute functions, but in reality, this is true only for purely computational tasks. Today, this model is still not widespread enough [14].

In cases where the basic functionality of a SaaS solution does not satisfy the customer of the service, and even applications that extend the functionality (add-ons) cannot help, a digital platform offered under the PaaS cloud model should be preferred. This is especially true for the B2B (business-to-business) market, where there may be special conditions in the selling of goods that require unique platform customization using the PaaS or even the IaaS model [10, 13].

The boundaries between available resources in each model (PaaS, SaaS and IaaS) may vary slightly across cloud providers. In an effort to provide maximum coverage of customer needs, providers are blurring the boundaries between cloud hosting models by adding services from adjacent models. The traditional areas of responsibility of the client and the service provider are shown in Figure 2.

Figure 2.

Distribution of traditional areas of responsibility of the client and the service provider for different cloud service models. Image source: Modified by the authors.

The PaaS model is most often in demand for large and medium-sized businesses, as it achieves an excellent balance between the convenience of platform administration and access to code, virtualization, server configurations and databases.

Modern digital platforms are built on the considered models of cloud services and perform a wide variety of tasks in all sectors of the economy, including agriculture.

4. Attribute-based structuring of digital platforms

The European Commission defines digital platforms, focusing on their functional purpose, such as: search engines, social networks, e-commerce platforms, app purchase stores, price comparison sites, etc. [15].

Of course, this definition, despite its recent origin, is now already outdated, although, as in the case of the concept of “digital services,” the definition of “digital platforms” still needs to be clarified.

In the literature, there are many definitions of “digital platforms” concept which are either too complex or too simplistic, but provide an opportunity to define the key attributes of the platforms. It is indicated that a digital platform is a technology-enabled business model that facilitates the exchange of information between information users and their consumers, who do not necessarily know each other, thereby achieving a certain confidentiality (trust).

According to researchers [16], a digital platform is a complex information system that provides a specific way to perform a certain function and is open for use by customers and partners, including application developers, merchants and agents. The platform can be used directly or through applications created on its basis with the owner or third parties [16].

A digital platform, as was pointed out by P. Evans and A. Gower [17], is a technology, product or service that is both a transactional and an innovative platform. This category includes companies, such as Apple, which have an App Store trading platform and a large ecosystem of independent developers who create content on the platform.

As a rule, digital platforms consist of a stable main component and a set of several additional components—services that provide the “attractiveness” of the platform for customers. The main component is connected to additional services using an interface. On the main component, an add-on is built from additional service components. Developers of additional services also use the appropriate interfaces to communicate with the main platform. In this regard, researchers [17] define a platform as building blocks that act as a basis on which firms, sometimes called a business ecosystem, can develop additional products, technologies or services.

There is an opinion that the digital platform is a means of organizing information exchange in the digital economy by concentrating information and further distributing it on the network. In this sense, the term “multilateral platform” is also used to define it, i.e., it means that the platform unites at least two groups of users, interconnected and to some extent interdependent. A typical example of the “multilateral platform” business model is various search engines that bring together people who are looking for this or that information.

In the work of the authors [18], a more accurate and meaningful definition of digital platforms is given as an organization that creates profit through direct interaction between affiliated groups of participants, in other words, parties. Thus, we can say that multilateral, or more precisely, multiplayer mode is a hallmark of multilateral platforms.

Also, digital platforms are considered as an environment for the formation of digital ecosystems that provide conditions for the development of new digital markets and demand for new services and services.

The above definitions make it possible to single out the main attributes inherent in digital platforms, on the basis of which it is possible to give a generalized definition of this concept (Table 2).

Attribute	Attribute content
Purpose	Information concentration and its distribution
Object	Set of digital data, digital services
Actors (Subjects)	Set of interested users
Location	Information network space
Ownership	Platform creator
Access method	Remote
Economic sense for the user	Reducing transaction costs due to the availability of information, the use of digital technologies and services
Economic sense for the owner	Income from rent for the usage of a digital platform and fees for access to resources

Table 2.

Attribute-based structuring of digital platforms.

Source: Compiled by the authors.

5. Types of digital platforms and examples of typology for agriculture

In the research of P. Evans and A. Gaver [17], it has been attempted to create a typology of currently existing platforms. The authors distinguish three types of platforms.

1. Transaction platforms are a technology, product or service that acts as an information channel that facilitates transactions, as well as information exchange between market participants.

2. An innovation platform is a technology, product or service that is the technological basis for providing services to other companies that develop their own technologies, products and services.

3. An integrated platform is a technology, product or service that can be considered both as a transactional and as an innovative platform, i.e., providing transactional opportunities and facilitating the work of other innovative firms.

It is advisable to give examples of the types of platforms that are created for the agricultural sector.

Recently, the PaaS model has been actively entering into the practice of agricultural enterprises. In this model, the customer is given the opportunity to use the cloud infrastructure to host the basic software, as well as new or existing user services (his own, custom-made or purchased). These user services are readymade procedures and solutions for various practical tasks.

For instance, the services of the largest digital platform for agricultural sector Cropwise (Syngenta Group) are providing such software modules [19]:

1. Cropwise Protector is a digital tool that makes agronomy decisions easier and faster by turning data into meaningful insights.

2. Cropwise Sustainability – ensuring sustainable agriculture for the large-scale production, certification, and sale of food grown in compliance with the principles of sustainable development.

3. Cropwise Operation is a comprehensive solution for monitoring the condition of fields and crops in one place in real time.

4. Cropwise Commodity Pro – protecting future premium crop volume from falling commodity prices during the growing season.

5. Cropwise Seed Selector – recommendations for choosing seeds to help consultant agronomists and agricultural producers.

6. Cropwise Planting – accounting for intrafield variability, optimizing the use of resources for each field, getting the most out of hybrids and increasing profitability.

7. Cropwise Imagery – images to monitor the status of crops anytime, anywhere.

8. Cropwise Spray Assist – helps specialists choose the best methods on the job.

Thus, the usage of the services of such a digital platform allows the user (farmer, farm or agricultural enterprise) to effectively solve their production problems. The user can choose a set of services to support a specific production situation. It is advisable to classify such a platform as a transactional one.

The development and improvement of digital platforms for the agricultural sector is an ongoing process.

Food and Agriculture Organization (FAO) has developed applications, databases and platforms to support the work being carried out in countries around the world [20]. These digital services increase access to useful data, information, digital maps and statistics.

One of the most significant geospatial platforms for accelerating agricultural transformation and sustainable rural development to eradicate poverty (Sustainable Development Goal 1 (SDG1)) and eliminate hunger and all forms of malnutrition (Sustainable Development Goal 2 (SDG2)) is the Hand-in-Hand (HiH) geospatial platform. This platform contributes to the achievement of all sustainable development goals.

Hand-in-Hand has brought together over 20 FAO units across multiple domains, from Animal Health to Trade and Markets, integrating data from across FAO on Soil, Land, Water, Climate, Fisheries, Livestock, Crops, Forestry, Trade, Social and Economics. At the moment, a million geospatial layers have been collected, thousands of statistics series with 4000 metadata records.

In addition, popular digital tools offered by FAO are [20]:

1. WAPOR, a portal to monitor Water Productivity through open access of remotely sensed derived data;

2. Crop Calendar, a tool that provides timely information about seeds to promote local crop production. It contains information on planting, sowing and harvesting periods of locally adapted crops in specific agroecological zones. It also provides information on the sowing rates of seed and planting material and the main agricultural practices.

3. Food Price Monitoring and Analysis (FPMA), this web portal contains the latest information and analysis on domestic prices of basic foods mainly in developing countries. It provides early warning on high food prices at country level that may negatively affect food security.

4. Agricultural Stress Index System (ASIS), it monitors agricultural areas with a high likelihood of water stress/drought at global, regional and country levels, using satellite-based remote sensing data at 1 km resolution. It simulates the analysis that remote sensing experts and agronomists would undertake and simplifies the usage and interpretation of the data for a broader audience.

5. FAMEWS, through the FAMEWS app and its platform, data on Fall Armyworm can be collected at the farm level and collated for sharing at local, national and global levels to manage the pest, identify priority areas and foster early warning mechanisms for all stakeholders.

Given the nature of the digital services developed by FAO, such a digital platform can be classified as innovative.

An example of an integrated platform can be the platform Datahub created under the leadership of the Data Center of the Kenya Agricultural and Livestock Research Organization (KALRO), which integrated [21]:

• the platform of The National Potato Council of Kenya (NPCK) Viazi Soko, provides seeds, market links and information exchange for participants in the potato value chain with plans to expand through the aggregation of multiple digital services and logistics.

• The Agricultural Transformation Agency (ATA) is a government agency pioneering multiple digital agriculture projects in Ethiopia with outreach to more than 5 million farmers.

The Datahub offers a suite of eight open-source digital services that cater to ecosystem actors, field organizations and farmers, and financial service providers by packaging data insights into ways that easily translate to their needs.

Digital platform actors (e.g., donors and governments) receive verified field data to inform planning and improve insights from existing models. Field organizations (e.g., tech innovators and research organizations) and farmers get agronomic information to increase farmer productivity and improve their income. Financial service providers (e.g., banks and insurance companies) receive aggregated farmer data to inform loan decisions and insurance payouts.

Services of the integrated platform KALRO-NPCK-ATA [21] are as given follows:

1. Food balance sheet (FBS) tracks Kenya’s food supply and demand, incorporating yield estimates for planning.

2. Livestock/crop suitability mapping, maps areas that are suited to specific crops and livestock.

3. 3) Modeling environmental changes, models climate change effects on agriculture ecosystems.

4. Early warning alert, signals impending disasters, food market, interruptions and their expected scale.

5. Weather/crop monitoring, provides weather forecasts and agronomic advice to farmers based on a crop location.

6. Livestock/crop selector, advises farmers on crop and livestock breeds based on map suitability

7. Agri-coach, advises farmers on the whole value chain building on inputs from other digital projects.

8. Credit scoring, assigns credit scores to farmers to access credit, leveraging agri-coach metrics.

The value proposition of the services 1–3 consists of using accurate field data to inform national planning, program development and help improve the analysis of existing models. The stakeholders in these products are politicians, donors and satellite companies.

The value propositions of the services 4–8 of the digital platform Datahub consist of providing actionable agronomic information to farmers and agronomic organizations so as to increase productivity and profitability, as well as to provide data on the activities of farmers for informing their decision to issue bank loans. The stakeholders in these products are technological innovators, research organizations, farmers, financial service providers, banks and insurance.

6. Digital platform: The decision support system tool for smart farming

A fairly large number of tools for decision support systems (DSS) for smart agriculture (Smart Farming) are currently used in agriculture. Digital platforms are one of the most effective tools for DSS, as they are data aggregators, perform the function of systematization and analysis of these data. Based on this analysis and using modern technologies, services are created that allow both small farms and large agro-industrial complexes to make informed management decisions.

Digital technologies, such as the Internet of Things (IoT) and machine learning (ML) methods, are opening up new opportunities for agriculture. These technologies include data sharing, homogenization, the ability to edit, reprogram and distribute data, meaning that no single participant has complete ownership of the data. A key element in creating value using these technologies is data, namely Big Data [22].

Data can come from different sources, for example:

• farmers and small farms;

• large agro-industrial complexes;

• satellite images;

• national agricultural statistics services; and

• thematic groups in social networks.

Such data, processed on a digital platform and presented in a user-friendly format, can greatly enhance the ability of agricultural producers to interact with suppliers and buyers.

However, producers will only be willing to share such data as long as their personal benefit outweighs the cost of such sharing. Here, an important role is played by such factors as the safety of using information for its source, the intentions of the interested party regarding the information received and the rules that exist in the issue of limiting the value that is created from the received data.

Data are the main assets of a digital platform, a fungible commodity that a farmer offers in exchange for more valuable information. The platform must collect and store data to support value creation, where the total value created will be the product of the benefit generated by the data, the benefit created by the system itself and the benefit generated by the user community [23].

All data from different sources can be combined into a single space and available in a certain form for information processing. There are features that a big data aggregator service must comply with:

• updating information should be accurate and as automatic as possible, which is necessary for building predictive models;

• the amount of incoming data should be large and always up to date;

• the cloud service must ensure the safety and reliability of saving Big Data.

Such requirements are met with, for example, by the cloud service Amazon Web Services (AWS), which is the most widespread among users of digital platforms [24].

The following model of a digital agricultural data platform can be proposed, as shown in Figure 3.

Figure 3.

Model of the digital agricultural data platform. Image source: Developed by the authors.

This model has three layers:

1. The layer of objects, in which the data come from different sources and then structures.

2. The management layer, in which the data are unified and standardized, then processed and formed DSS (decision support system).

3. The layer of value creation, which presents the services of the digital platform available to its users.

It should be noted that the construction of DSS is based on ML (Machine Learning) algorithms, which are processed in the operational core, based on cloud computing and the results can be, for example, forecasting, warning and anomaly detection.

Thus, it is obvious that digital agricultural platforms while operating add value to data and allow creating an effective DSS, which is presented to users in the form of various digital services: WWW (World Wide Web), applications and digital maps. Such a DSS can help, for example, farmers to make smart decisions, such as organizing the optimal irrigation process, managing the process of feeding crops with nutrients and making decisions about the rational feeding of animals [25].

However, in the process of practical use of such a digital agricultural data platform model, the following problems inevitably arise:

• ensuring trust and security in the process of data exchange;

• integration of a sufficiently large number of manufacturers that provide information;

• obtaining decisions and recommendations that are of significant importance in the process of agricultural activity.

The practical functioning of such a model requires the willingness of all participants to exchange data. In practice, however, this willingness is not always present. For example, farmers, farmer organizations that provide raw data do not have the right to analyze it, and also do not have the right to control the distribution of income that is received from the created data value (value creation) [26].

The following problem arises: since there is no complete transparency in the use of the data that are provided, in certain cases a potential source of information does not want to share its data that have been developed in the course of practical activities. In this case, the problem can be solved by providing, for example, interested parties, such as farmers, some digital platform services for free to encourage them to share data.

And this practice is actively used by service developers. For example, the Farmable service [27] is designed to manage an agricultural farm. Farmable is a service designed to help solve the specific production needs of producers of fruits, berries and fruits of other tree crops. The service can work on different devices, namely mobile devices, tablets and computers, thanks to a combination of different applications and additional modules.

The developers have optimized the application to help in the production of such types of products as: fruits (apples, pears), pome and stone fruits, nuts (almonds and macadamia nuts), berries (strawberries, blueberries, blackberries), grapes and vineyards, olives and avocados. The service modules are shown in Figure 4.

Figure 4.

Functional modules of the farmable service. Image source: Modified by the authors based on [27].

Farmable Core modules in Figure 4 are marked in green and they are provided to users free of charge. Additional modules, such as Safe Spraying, Reporting and Sales Management, are provided for a fee (price from 19 to 199€ per year for one farm).

It is advisable to propose the following recommendations to ensure the effectiveness of the functioning of digital agricultural platforms using Big Data:

1. It is necessary to ensure the trust in data and the transparency of their use by interested parties. This requires clear conditions (contractual and legal) for access to the use of these data. It is also necessary to provide stakeholders with information on how these data will be used and how the value created will be distributed.

2. It is necessary to establish rules and conditions for the use of data. To do this, it is necessary to define and describe the ownership of data, the conditions for user access to specific data and the mechanism for their use.

3. It is necessary to indicate to stakeholders the types of incentives that will be offered for providing private data, so that all participants in the digital agricultural platform receive fair benefits from using the platform services.

4. It is necessary to ensure data monetization. To do this, it is necessary to develop a mechanism for determining the value of personal advice, which is obtained on the basis of DSS generation. To motivate stakeholders to use the services of the digital platform, it is recommended to provide part of the services for free.

From the point of view of the quality of data analysis, the main problems here are data preservation and standardization, data scarcity and their integration.

Some data may be stored for a long time and used for a long time. To ensure the safety of data when designing a data center (Figure 3), equipment developers use the appropriate standards, for example, the TIA-942 standard (“Telecommunications Infrastructure Standard for Data Center” [28], which provides a given fault tolerance.

For example, the TIA-942 standard defines four levels of data-center uptime. The first tier, Tier 1, is the lowest, at 99.67%, which corresponds to a planned downtime of 28.8 h per year. The highest level of fault tolerance is recorded in the data center of the fourth class (Tier 4), it is at 99.995%, which means a total interruption in work of no more than 15 min per year [28]. It should be taken into account that the higher the level of fault tolerance of the data center, the higher the costs for its creation and operation, and as a result, the higher the tariffs for cloud services, which are described in Section 3 of this chapter. Consequently, this factor also contributes to an increase in the costs of maintaining and operating the digital platform, which is built on the basis of these cloud services.

A rather difficult issue is data integration, since it is impossible to determine in advance whether data sets from different sources of information are compatible with each other. This issue can be resolved using two-way communication standards [29].

Thus, digital agricultural data platforms allow organizing the process of adding value to data based on the use of DSS, which makes them indispensable for smart agriculture (Smart Farming).

7. Possibilities of digital platform services for the value cycle in agriculture

Digital agricultural data platforms improve the speed of data processing and efficiency of formation of initial indicators, control and provide environmental sustainability of the entire global agro-industrial system, as they reduce transaction costs and prevent disruptions in the sale of products, improving the quality of analysis in proportion to the amount of information aggregated.

The efficiency of the agro-industrial system is increased by reducing the cost of copying, transporting and searching for data, strengthening the integration and interconnection between all participants in the production process. As the marginal cost of delivering digital information approaches zero, it reduces the digital asymmetry, thus shortening the agricultural value chain. Digital platforms facilitate market alignment between producers and consumers, borrowers and lenders, resource providers and product manufacturers, thanks to data analysis information systems, software products, computer technology and related data processing techniques.

The use of digital platforms reduces disparities in access to agricultural information, expands the scope of financial services and provides access to global markets. With low search, transaction and verification costs, digital agricultural data platforms improve access to credit, finance and insurance.

Currently, one of the key tasks of world agriculture is to ensure its sustainable development [30]. Improving the sustainable use of water, land and biodiversity resources while improving food security and productivity growth is critical to ensuring enough food for a growing world population. Digital agricultural data platforms help producers (farmers and agricultural holdings) optimize the use of land and water. Built on a digital platform, storage capabilities enable producers to reduce wastage of resources and minimize negative external environmental impacts, including greenhouse gas emissions, soil erosion and fertilizer runoff, while at the same time increasing crop yields.

To determine the impact of digital platform services on the value creation process in agriculture, it is logical to group all stakeholders involved in this process into four groups. These groups are formed in accordance with the value creation cycle in agriculture (Figure 5):

• Input hub.

• Production hub.

• Postharvest hub.

• Consumer hub.

Figure 5.

Functions of digital platform services in the context of the value cycle in agriculture. Source: Developed by the authors.

Each hub has certain resources that help solve problems that arise during the implementation of the value cycle.

Examples of agricultural information digital platform services are shown in Table 3.

Direction number According to Figure 5	Service name, link
	International	National
1.1	myAgro https://www.myagro.org/	Lima Links Zambia http://www.limalinkszambia.com/
1.2	Hello Tractor https://hellotractor.com/	Tun Yat Myanmar https://www.tunyat.com/
1.3	Agunity https://www.agunity.com/	MyCrop Indonesia https://mycrop.tech/
1.4	Syngenta https://www.syngenta.com/	M-pesa Kenya https://www.safaricom.co.ke
1.5	neoInt https://www.neoint.ai/	QualiTrace Ghana http://www.qualitrace.com/
2.1	FarmERP https://www.farmerp.com/	Poladrone Malaysia https://www.aonic.com/my/
2.2	Sailog https://agrio.app	Sipindo Indonesia https://sipindo.id/
2.3	Digital Green https://www.digitalgreen.org/	Farmerline Ghana https://farmerline.co/
2.4	N-Finds https://andfrnds.com/	AgriProFocus Netherlands https://agriprofocus.com/agriprofocus
3.1	One Acre Fund https://oneacrefund.org/	DATAGRO Brazil https://www.datagro.com/
3.2	TruTrade http://www.trutradeafrica.net/	Twiga Kenya https://twiga.com/
3.3	Logistimo https://logistimo.com/	Selina Wamucii https://www.selinawamucii.com/
3.4	Agromovil https://www.agromovil.co/	Kumwe Rwanda https://www.kumwe.com/
3.5	Farmcloud http://www.farmcloud.io/	Choupal Saagars India https://www.itcportal.com/businesses/agri-business/e-choupal.aspx
3.6	Stellapps https://www.stellapps.com/	Olam Cameroon https://www.olamgroup.com/
4.1	Evigence https://evigence.com/	BactuSense Israel https://www.f6s.com/company/bactusensetechnologies
4.2	Scantrust https://www.scantrust.com/	Shellcatch Chile https://web.shellcatch.com/
4.3	TruTrade http://www.trutradeafrica.net/	Rego Pantes Indonesia https://regopanties.id/

Table 3.

Service examples (according to Figure 5).

Source: compiled by the authors.

Input hub services help agricultural producers provide greater access to quality inputs (seeds and fertilizers). This happens due to the optimization of disparate networks of agrodealers, elimination of the negative effect of lack of information, which is relevant primarily for small farms.

An example of such a service is myAgro, a service in Senegal and Mali that helps smallholders acquire quality agricultural inputs based on milestone payments through a mobile phone platform. By topping up their myAgro accounts, farmers save money and then pay for it over time through the application.

For sustainable farming, farm mechanization plays a key role, as it allows you to increase yields, optimize the workplace, increase profitability and at the same time reduce the risks associated with climatic conditions. Digital platforms can provide farm equipment rental services, allowing small farmers to reap the benefits of mechanization.

Tun Yat digital platform services (Myanmar) connects farmers who want to rent agricultural equipment with equipment suppliers (large or small). Tun Yat offers affordable and reliable tractor and harvester rental services.

With services that verify the quality and authenticity of the resources are used, agricultural producers can protect their brands and maintain the trust of their customers. Services created on the basis of using blockchain technology employ verification methods such as scan codes that allow you to detect fake resources.

For example, the neoInt service uses tracking technology to authenticate resources and fight counterfeit products. neoInt not only performs authentication, but also provides analytics tools to track products as they move from one stage to the next, until the end consumer also checks the source and quality of the product.

Production hub services help agricultural producers to carry out the production process more efficiently. Service products of digital agricultural platforms can improve the yield and quality of agricultural products in precision farming, increasing economic sustainability.

For instance, Poladrone (Malaysia) provides drones and field mapping services that allow farmers to calculate yields and detect trees or crops affected by diseases or pests. Farmers can use these field maps to improve their farming accuracy. In addition, using field coordinates obtained through the service, drones can also treat individual diseased plants with a pesticide sprayer attached to the drone.

Saillog is an international company, which offers Agrio, an AI-powered service that allows farmers to identify and treat plant diseases and pests. Agrio is a free interactive app for smart phones. Agrio users upload images of their diseased crops, which are then analyzed using an artificial intelligence algorithm to automatically identify diseases. Users can receive a response to their request within seconds, while they can also share their images with a team of experts who will provide recommendations on effective plant treatments.

Postharvest hub services facilitate the exchange of information regarding markets and prices for agricultural products, which is of great importance during the harvest season. These services make it easier and faster to establish links between product manufacturers and their potential customers, thereby reducing revenue losses due to insufficient and timely market information.

With the help of the DATAGRO (Brazil) service, agricultural producers can get an individual assessment of their market niche. This assessment includes competitors’ production forecasts, customer requests, price estimates and logistics risks.

The services of the international digital platform Agromovil (Latin America and Africa) connect manufacturers and carriers of products, as well as markets, forming a single chain from “farm to market.” When the harvest is ready for sale, producers contact buyers through the Agromovil application of the same name, and carriers carry out group export of the crop to increase the efficiency of each trip.

An interesting service, Choupal Saagars (India), was developed by ITC to improve the efficiency of processing and storage systems for agricultural products. Choupal Saagars are Internet kiosks that allow Indian farmers to access market information, including details about the collection and storage of agricultural products. Moreover, the service offers a unique virtual rural marketplace, providing a wide range of products beneficial to farmers. It also offers information and consultations related to the production of agricultural goods and even extends to banking.

Consumer hub services help to ensure the safety of consumption of both raw and processed agricultural products through tracking systems and the detection of spoiled products. These systems allow to solve such problems as poor access of products to the market and insufficient monitoring of product quality. These services help ensure that food products are handled and processed correctly, improving quality and reducing food waste.

BactuSense (Israel) has developed an innovative patented technology for the rapid detection and quantification of bacteria in food and water samples. Instead of the 48–72 h incubation periods required for Petri dishes, the Bac-Tracker gives the same results after 3–5 h with a 97% correlation. The service is easy to use and can be installed next to any food production line. The use of this technology makes it possible to optimize supply chains and ensure the release of products in the shortest possible time with guaranteed quality, which significantly reduces the manufacturer’s storage costs.

As can be seen from the above, services can contribute to the growth of agricultural entrepreneurship, especially when the right enabling environment is in place. In addition, such services help agricultural producers make rational management decisions that can ensure sustainable agricultural production.

Nowadays, many development companies offer a variety of interesting digital services to ensure sustainable agricultural production.

For example, Source Trace (a SaaS company) has developed the DATAGREEN platform, which provides comprehensive solutions for managing all aspects of the agricultural value chain [31]. The DATAGREEN (DG) Agri Solution software consists of modules (Figure 6) that are designed to meet the specific requirements of various crop value chains and processes.

Figure 6.

DG Agri solution modules. Image source: Modified by the authors based on [31].

Source Trace has also developed a useful software service called the Carbon Trace, which helps organizations solve the data challenge in carbon farming [31]. Carbon Trace has the unique distinction of being useful for large farms as well as small farm holdings.

With the help of this service, it is possible to unite farmers in order to include them in the sustainability network and passing on the benefits of carbon sequestration down to the smallest contributors. Carbon Trace allows to bring all stakeholders (farmers, inspectors and traders) on one platform and capture verifiable data from the ground at very stage by the combination of ground and satellite data to leave no gaps.

As might be known, sustainable agriculture is closely related to the organic sector, but in essence it is much more. The transition from conventional to sustainable agriculture is not an easy task, as it requires useful technical advice for farmers, investment, charitable donations and stakeholder support (large manufacturing companies, national government and a global initiative).

For example, the largest corporation Syngenta, in its Modern Agriculture Platform (MAP) business model, has established a soil testing service (China) to provide farmers with customized end-to-end planting solutions for precision farming [32]. With the help of MAP, the entire process has been digitized and informatized, from field sampling of soils to the development of online solutions. Soil samples are taken using an automatic vehicle, then an individual QR (quick-response) code is generated for each sample (the data contain the coordinates of the sampling site, farm profile and test results). These data are uploaded to the MAP Soil digital platform database. After that, agronomists and farmers receive a message on client terminals with a reminder to check the test results online. The MAP Soil big data platform contains a nationwide soil-type database and a 30x30 m square soil nutrient database. These data are used to develop regional fertilizer packages and recommendations for precision farming. At present, the MAP Soil big data platform has soil data for 2552 counties in China, as well as distribution data maps of more than 200 soil-type subcategories, accounting for almost 100% of China’s soil types.

Farmers are both users and creators of big data, and the impact of digital services on farmer behavior is all-encompassing and far-reaching. Agricultural platform digital services not only provide database development, they also encourage farmers to continuously upload their data to help them make sound management decisions. These services shape the specific behavior of farmers in the following aspects:

• rational and optimal distribution of agricultural resources;

• gradual reduction in environmental pollution;

• improvement in the agricultural production system for the rapid development of agricultural economy,

• changing the thinking and consciousness of farmers regarding the need to conduct agriculture in accordance with the concept of its sustainable development.

For example, supported by the European Commission’s DG Agriculture and Rural Development, by the EU Space Programme (The Directorate-General for Defense Industry and Space (DG DEFIS)) and by the EU ISA2 Programme (The European Commission’s Directorate General for Informatics (DG DIGIT)), the FaST digital service platform will make available capabilities for agriculture, environment and sustainability to European Union (EU) farmers, Member State Paying Agencies, farm advisors and developers of digital solutions [33].

The FaST digital platform helps farmers to improve crop management models, simplify day-to-day management and improve economic efficiency while protecting the environment. Also, FaST facilitates the exchange of information between farmers, as well as between farmers and other stakeholders regarding the experience of growing crops. For the relevant EU institutions, the FaST platform helps to provide environmental monitoring of agricultural land, improve two-way communication with farmers, computerize agriculture, simplify work processes and develop appropriate standards. For policymakers, the FaST platform provides a quick search for agricultural data, which helps to analyze the current state of agriculture and develop a strategy for its development [34].

Access to easy-to-use digital services that track forest cover, land-use patterns and their changes over time will become increasingly important as countries around the world take action to adapt to and mitigate climate change.

The use of satellite images combined with digital services is fundamentally changing how national governments can assess, control and plan the use of their natural resources, including monitoring deforestation and desertification.

Effective policies by national governments regarding the safety and quality of agricultural inputs, products and services can help develop agribusiness by increasing the productivity and profits of farmers.

Thus, digital agricultural platform services make it possible to efficiently collect and disseminate information, compare supply and demand in the markets, and optimize market activities. They also help to facilitate the exchange of knowledge in the field of agriculture, which significantly increases the efficiency of its management.

8. Conclusions

Nowadays, with full confidence, it can be argued that the digitalization of agriculture is not a goal, but a means to ensure its sustainable development. However, this process is rather complicated and slow. It constantly requires the public and private sectors of the economy of each country to adjust the regulatory framework and effective management.

This concerns, first of all, the open data policy. In Europe, since 2003 [35, 36], the practice has been to provide stakeholders with as much data as possible. This practice helps farmers access data for use in the production process. However, there are still not enough valuable data sets published at present. The public sector of national economies can intervene in many areas of open data policy to improve initiatives to provide open data, manage big data and strengthen farmers’ rights. These may include measures such as increasing public investment in open database development, improving data ownership and integrity policies, developing sound standards for data processing and use, enforcing data protection and privacy policies, enacting competition laws and securing public-private partnerships to that agricultural data be a public good, not a private good.

At the same time, the development of governance mechanisms that build user confidence and trust in digital agricultural platforms will increase the demand for digital services for the agricultural sector and the food system.

Undeniable advantages are high speed of data processing and efficiency in the formation of initial indicators for an unlimitedly wide set of grouping characteristics and factorial criteria, process of analysis and the optimization of production and communication processes carried out on its basis.

The integration of the control function of data verification at the stage of their receipt, and not after the end of processing, allows to improve the quality of management decisions, as well as limiting the access of a person as a negative factor and reducing the likely negative impact.

The analytical function can be implemented at each stage of the process through a deep analysis of the totality of the array of information that naturally develops in the process of continuous operation of the enterprise.

World experience shows that new technologies are the driver of socioeconomic development.

The technology of risk-free agriculture is a striking example of a new innovative technology that has been widely developed [37]. The usage of this technology in the production of agricultural products allows you to obtain a stable crop quality, efficient use of water, a controlled plant factory environment, and allows you to achieve a level of ultra-high efficiency with minimal land use.

Today, digital technologies are especially important for sustainable agricultural development, which are very effective for the circular economy. Circular economy is also an industrial system that is restorative or regenerative by intention and design. It replaces the “end-of-life” concept with restoration, shifts toward the use of renewable energy, eliminates the use of toxic chemicals, which impair reuse, and aims for the elimination of waste through the superior design of materials, products, systems and, to develop within this, business models [38].

One of the significant steps toward the practical construction of a circular economy is the European Green Deal (EGD) initiative of the EU countries. In accordance with the EGD, the goal is to make the EU climate carbon neutral by 2050 [39]. At the heart of this EU initiative is the goal of transforming the economy for sustainable agricultural development and the transition to a sustainable food system.

Unfortunately, corruption weakens global and national institutions of sustainable development, which creates ineffectiveness of strategic achievements and people’s confidence in acquiring positive changes, which in turn leads to the transfer of strong players and resources to other projects. “Corruption drains more than 5 per cent of the global GDP,” said Lachezara Stoeva. “Of the approximately $13 trillion in global public spending, up to 25 per cent is lost to corruption.” That translates into at least $3 trillion for public spending. Under the theme, Unleashing the transformative power of Sustainable Development Goal (SDG) 16: Improving governance and reducing corruption, the special meeting aimed at identifying concrete solutions to promote anti-corruption practices at all levels [40].

Evidence from 22 OECD (Organization for Economic Cooperation and Development) countries suggests that the negative impact of corruption on the Total Factor Productivity growth over a 20-year period is due to corruption undermining technological change and companies’ incentives to build up entrepreneurial skills, invest in innovation, research and development, which are crucial ingredients for sustainable growth (Salinas-Jiménez and Salinas-Jiménez, 2007 [41].

Perspective for further research: due to the persistent trend of reducing the number of agricultural workers and the increase in the number of final consumers of food, it is necessary to adapt digital solutions primarily for food-critical agricultural products, the production of which will become problematic. Also, digital solutions will help reduce the risks of climate and environmental aspects, which urgently require workers who can manage data using platforms. We anticipate that in the next 10 years there may be food shortages where human participation is most needed. The labor shortage issue in agricultural sector, artificial intelligence and the preferences of the young generation will only increase this trend of food safety risks in the future.

The agricultural platform must control all the resources included in agricultural production, as well as the full production chain ending with the end consumer, the threats that bring us closer to a global catastrophe and neutralize their impact on the rhythm, sufficiency, balance and sustainability of the natural environment. This is a complex accounting and analytical task—to collect information and communication links of different industries and regions; however, with the help of modern digital capabilities, this will be feasible in the near future.

Acknowledgments

The corresponding author ensures that all contributing coauthors and no uninvolved persons are included in the author list. The corresponding author also verifies that all coauthors have approved the final version of the article and have agreed to its submission for publication. The authors are responsible for the content of the article and for the fact of its publication.