Analysis of the market of cloud IoT platforms and applications for digital agriculture in the world and prospects in Russia

December 2018

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Analytical Report (full version)

Analysis of the market of cloud IoT platforms and applications for digital agriculture in the world and prospects in Russia
Analysis of the market of cloud IoT platforms and applications for digital agriculture in the world and prospects in Russia
December 2018

Analysis of the market of cloud IoT platforms and applications for digital agriculture in the world and prospects in Russia

December 2018

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J'son & Partners Consulting presents results of the research devoted to the world market of cloud platforms of the Internet of Things (IoT) for agriculture and prospects of their use in Russia. Such IoT platforms and services are the technological basis of digital agriculture (precise agriculture or smart farming), which is defined by the American Association AgGateway as production of agricultural products using adaptive (self-optimizing) production and business processes, increasingly autonomous from direct human participation. The property of adaptability is based on the use of mathematical models describing interactions of process metrics, with mainly direct receipt of primary data directly in the field of their origin from IoT devices and sensors, which allows achieving high quality of data: relevance, accuracy and completeness.


IoT cloud platforms and applications for digital agriculture


Providing model of agricultural IoT platforms can be cloud, public or hybrid, so the terms "IoT platform" and "cloud service" are synonymous. According to the classification given by Berg Insight and First Analysis, most of IoT platforms can be attributed to one or several categories:


- Connectivity Management Platforms, CMP;

- Network / Data (Subscriber) Management (NM);

- Device Management Platforms, DMP;

- Application Enablement Platforms, AEP;

- Application Development Platforms, ADP.


The present study examines only Application Enablement Platforms (AEP), since only they are specific to the industry. Specialized AEP’s for agriculture have two types: agricultural data aggregator platforms, which can be called basic, and application platforms and specialized agricultural services. Intensive two-way data exchange is implemented between these two types of platforms. Data analysis is carried out in both types of platforms, and the functions of automation of production and business processes of agricultural enterprises with the use of these data are implemented only in application platforms and services.


A long with the applications, initially developed as cloud-based IoT platforms, there are Farm Management System (FMS) industry applications migrated to cloud platforms. Such migration is done when FMS developers change the on-premise deployment model to SaaS model, which allows the provider to accumulate a large amount of data from companies that use this application. Cloud FMS can be considered as industry IoT platforms as most of them use direct automatic data input from sensors and actuators and their integration with a large number of external systems and services, such as weather services, GIS, end-to-end traceability services, etc. Thus, among applied cloud services for agriculture there are not only IoT-platforms, but also transactional applications provided by the SaaS model, so there is no clear boundary between "platforms" and "services" and in this study considers both.


These cloud applications (application services) do not include cross-industry applications that are used not only in agriculture but also in other industries, such as supply chain and sales management applications, if they are not part of industry applications (services).


The rarest are platforms/applications that have not only the function of information support of decision-making and control of their execution, but also the actual execution, that is, they are control systems. Such platforms/applications are also considered in this study.


The analysis of secondary sources of information was used as the main method of research, as well as construction of a quantitative model of the market, describing the metrics and their interaction, and verification of the analysis results with involvement of industry experts. The results of the study are practical and can be used by developers of industry platforms and services to determine the target market niches, the appearance of competitive products for these niches and achievable parameters for the subscriber base and the volume of payments from it.


Market of IoT platforms for agribusiness


The principal possibility of obtaining a significant economic effect from the digitalization of agriculture, which is much higher than the cost of tools to achieve it, is that this industry is characterized by the presence of a large number of parameters, objective control and management of which is critical to the final result. Such multi-factor optimization is impossible without the use of appropriate cloud platforms and services, especially cloud, because only the cloud model makes them available to farms of any size, not only to the largest ones. The emergence of these services, which are also available to small farms, creates the necessary prerequisites for dramatically improving efficiency and reducing risks in the industry, and for all participants in the value chain, including suppliers and the sales and logistics link.


Thus, the basis of digital agriculture are mathematical models of end-to-end processes of production and marketing of agricultural products, that is why such agriculture is called digital, allowing in nearly automatic mode to optimize production and sales in terms of profitability, business sustainability and minimization of the negative impact on the environment.


Mass use of this approach in agricultural business is just beginning. Even in the US, the most advanced regional market, cloud platforms and services have become widely used only in the last 2-3 years, so it is difficult to assess the economic impact of the digital transformation of agriculture in terms which are written above. However, even with the current initial phase of the transition to digital agriculture, characterized so far only by more developed and detailed information support than earlier decision-making, the proven economic effect gives tens of percent increase in productivity, reduces losses and unit costs per unit of production. In combination with the acceptable cost of using such services for most farms, even for small farms – just few dollars a year per acre with free subscriptions, the obvious economic benefit even from entry-level digitalization means that the level of penetration of cloud platforms and services will grow rapidly and in the coming years will become a mandatory element of any successful agricultural business.


Implementation of end-to-end digitalization of the whole process of creating added value of agricultural "from field to fork" products can lead to a multiple reduction in the unit cost of production and marketing of agricultural products and radically transform the agricultural industry and related industries with the emergence of fundamentally new business models, such as cloud model of use of not only automation, but also mechanization, food production under the requirements of a specific end user with end-to-end traceability of its properties, new approaches to seed selection and so on.


According to J'son & Partners Consulting, in monetary terms, the global market of cloud platforms and services for digital agriculture amounted to $815 million in 2017, and has the prospect of more than double growth to $1.9 billion in 2022. This market volume includes only payments for use of cloud platforms and digital agriculture services, while the costs of related services and equipment are not included in this assessment. The main segment of the market under consideration is cloud transactional and analytical platforms and applications for crop production and universal platforms and applications that form 86% of the total consumption.


The key regional market is the North American market (USA, Canada), which formed almost 40% of the final consumption in 2017. The market of Southeast Asia and Oceania (China, India, Australia, New Zealand) has the greatest growth potential: the share of consumption of cloud platforms and services in this region can grow from 22% of the global market in 2017 to 30% in 2022. It is obvious that Russian developers should pay more attention to this regional market because of both the greatest growth potential and the lower level of competition than in the North American market. However, to develop promising solutions, it is advisable to use the data accumulated in the North American market due to the greatest penetration of cloud IoT applications and services for agriculture in this region, as well as the abundance of data accumulated over a long historical period.


The volume of consumption of cloud applications and services for agriculture in Russia amounted to only about $6 million in 2017 and the prospects for rapid growth of this volume in the future look doubtful due to the presence of powerful constraints, which clearly indicates the need to focus on the global market for any Russian developer of applications for digital agriculture.



Ecosystem of IoT cloud applications and their key functionality


Currently, a global ecosystem of cloud IoT applications and services is being formed, each of these applications and services performs its role and interacts with others. These are platforms for primary data collection and accumulation (basic platforms), such as Monsanto FieldView and aWhere - they are distinguished by the global principle of data collection and analysis and the lack of automation of production and business processes of agriculture. This cloud-based transactional (accounting) application with functions of analysis and planning, so-called Farm Management Systems, is integrated with basic IoT platforms and enrich global data of basic platforms with local data of connected farms, such as data on field operations, field investigation results and data from sensors installed on the control objects, for example humidity sensors and data on the content of nitrogen in the soil. These are specialized analytical applications based on complex mathematical models and allowing scenario analysis of planning with the option of the most optimal scenario. These are end-to-end traceability and supply chain management applications integrated with accounting and analytical applications for production estimation and early contracting with its online adjustment implemented in an end-to-end manner. These are applications for management of agricultural machinery and predictive maintenance, on the basis of which the services of joint use of agricultural machinery are implemented, which increase the level of its loading (utilization) and make it available for small farms.


The window of opportunity to become a part of this emerging global ecosystem has not yet been closed for Russian developers. At the same time, initially any development in this area should be positioned as part of the global ecosystem, not as a local analogue of any global platform or service.


Regardless of their purpose, the key characteristics of IoT applications and platforms for digital agriculture that fundamentally distinguish them from "traditional" automation tools are:


- Cloud (public, hybrid) model of providing application functions, which, unlike the model of selling licenses for on-premise installations allows the provider/developer to accumulate and analyze the data of all connected users of the application. Data array and models of their analysis is the main asset of any developer of such applications, which strategic investors are guided by when assessing the value of the developer.


- The openness of the platform and services to carry out intensive bilateral exchange of information with a wide variety of external systems. API-integration with weather services, remote sensing data storage and processing services (satellite images of fields), with systems of suppliers and customers with the implementation of end-to-end traceability is required.


- The presence of not only manual data entry into the system, but also automatic input from connected sensors and actuators, due to API-integration with third – party sensor and controller products (less often-due to the release of its own pre-integrated sensors with the platform), as well as through API-integration with the developer platforms of these sensors and controllers or with specialized platforms collecting data from them, which can dramatically improve the quality and efficiency of data receipt.


- In terms of data processing, a distinctive feature is the introduction of mathematical models using machine learning technologies that allow to correctly interpret the collected data, build forecasts with high accuracy and detail, and produce scenario analysis with the choice of the most optimal scenario not only by production criteria, but also by the financial criteria of the upper level (revenue, marginality) in the context of specific fields, crops, etc. taking into account the probability of these scenarios.


- There are the first attempts to automate not only the stages of planning, accounting and control, that is, the function of information support of people's actions, but also the automatic execution of planned actions, thus closing the control loop and making it fully automatic and adaptive.


The appearance of any service being developed for digital agriculture must meet the above requirements.


Constraints to development


Constraints to successful development and implementation of such applications and platforms in Russia are:


- A pronounced lack of accumulated over a long historical period of quality agronomic data in Russia (there are only remote sensing data), which does not allow to create adequate models using only Russian data and produce scenario analysis. In Russia, there are very few connected equipments, there are practically no sensors in the fields that transmit data to cloud applications.


- A small number of machinery in agriculture, especially in medium and small, that is, there is no tools to implement the recommendations and plans.


- The practical absence of "traditional" on-premise means of business automation (ERP) and production (APCS) processes - there are only accounting systems, systems of class FMS virtually no, as a consequence, there is no culture of business management using such systems and accumulated accounting data on transactions in the fields, which makes it impossible to compare remote sensing data with actions in the fields.


- Information closeness of farms and distrust of the external environment, extremely aggressive, supported by regional and Federal official’s position of large agribusiness to absorb medium and small farms.


- Limited presence of global app providers in Russia, the lack of ecosystems built by Russian developers around these platforms.


- Another problem is the extremely limited financial capacity of Russian agricultural producers.


For application developers these constraints are:


1. Lack of accumulated agronomic data (with the exception of remote sensing data that are global in nature) due to the low level of penetration of automation in the agricultural business and the prevalence of on-premise systems, as a result, in the absence of reliable benchmarks and the ability to build complex predictive models, which in turn significantly reduces the efficiency of the use of platforms and cloud applications.


2. Extreme difficulty of promoting modern platforms and applications in the Russian agricultural business due to:


- The depressed state of the vast majority of enterprises, except for large holdings that are on state subsidies, as a result, the overall impression of hopelessness, lack of prospects and disbelief in the ability to change anything for the better.


- Conservatism of management and owners of agricultural enterprises, lack of experience with advanced automation tools and lack of confidence in them as a tool to improve efficiency.


- The low level of wages in the village as a consequence of the widespread theft of the staff of fuels and lubricants, animal feeds, fertilizers, seeds as a way to compensate for low wages and, as a consequence, the perception of automation (including the automation of control staff) not as a tool-assistant, but as an enemy.


- The lack of local developer ecosystems around global platforms and applications and, as a consequence, the ability to go through ecosystem participants in agricultural companies that already use any particular functionality, with additional functionality, which is the main way to promote new products and services in the United States. The exception is the providers of mechanization tools, "pulling" the developers of fleet management systems, which, in turn, "pull" the basic platforms and application services. But this is the case only in large farms and holdings (AIC) that have access to financing and are able to buy modern agricultural machinery.


- Lack of developed networks of partners involved in the installation and maintenance of hardware elements of automation systems.


Development strategy for IoT platforms and applications in the Russian agricultural market


It is important for Russian agricultural producers and state bodies of agricultural management to understand that digitalization is not only an auxiliary process of informatization of the industry, and is crucial for the development of agriculture in the country.


Consumption of the vast majority of food products in Russia is at a much lower level of medical standards. On the other hand, the potential for optimizing the processes of production and marketing of agricultural products, and, as a consequence, reducing the cost and retail food prices in Russia is a multiple, which creates the possibility of a significant increase in agricultural consumption even in the face of a decrease in real disposable income. There is no such growth potential in developed markets, such as North American and Western European, but there is, in addition to Russia, in a number of countries in Eastern Europe and South-East Asia.


Through digitalization, allowing you to radically redesign the whole process of production and marketing of agricultural products enables multiples to reduce the retail price of food, at the same time to increase the margins of business farmers and to improve the quality of the products. First, the unavailability of modern mechanization and automation equipment for the vast majority of agricultural enterprises in Russia is the main reason for the extremely low labor productivity, respectively, the high cost per unit of production. The transition from the model of sale to the ownership of agricultural machinery and automation equipment to the model of payment for their functions on the actual volume or even the results of consumption, which is the basis of digital transformation, solves the problem of availability of equipment and, consequently, increase productivity.


Since Russian farms start with a very low level of productivity, its increase can be up to 3-5 times. Second, digitalization, through its cross-cutting nature, allows the information to link the needs of a particular end user and the capabilities of a particular agricultural producer, thus eliminating many unnecessary intermediaries/third hand dealers, which now account for up to 80% of the cost of the retail price of the product. Together, these two factors will increase the volume of consumption of agricultural products in Russia in monetary terms by 1.5 times, that is, the effect of the growth of consumption will block the decline in retail prices, while the marginality of the business of agricultural producers will even grow, and the risks will decrease. The Park of tractors can increase by 300,000 units, harvesters - by 200,000, and the consumption of fertilizers will grow by 9 times. In game theory, it is called the win-win model (games with a positive prize amount) - all participants of the digitalization process, including the end user, win.


This information note was prepared by the J'son & Partners Consulting. We work hard to provide factual and prognostic data that fully reflect the situation and available at the time of release. J'son & Partners Consulting reserves the right to revise the data after publication of new official information by individual players. 


Copyright © 2018, J'son & Partners Consulting. The media can use the text, graphics and data contained in this market review only using a link to the source of information - J'son & Partners Consulting or with an active link to the JSON.TV portal


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Detailed results of the study are presented in the full version of the Report:


"Analysis of the market of cloud IoT platforms and applications for digital agriculture in the world and prospects of their implementation in Russia”



1. Definition of cloud IoT platforms and applications for digital agriculture, research methodology and boundaries   

2. Conclusions and recommendations    

3. Development of IT-support tools for agricultural business on the model of cloud platforms and services, formation of digital agriculture ecosystems       

3.1.    Quantification of the global market for cloud IoT platforms and applications for agriculture

3.2.    Basic platforms for data collection and storage  

3.3.    Platforms and applications with advanced analytics and modeling.      

3.4.    Cloud-based transactional systems for crop and livestock applications (Farm/Field Management Systems)     

3.5.    Cloud platforms and applications for fleet management of mechanization (mobile and stationary) in the field, platforms for agricultural machinery sharing

3.6.    Platforms and applications for climate control in agricultural buildings and structures

4. Profiles of the world's largest and / or most promising cloud platforms and applications for agriculture       

4.1.    Basic cloud platforms for data collection and storage    

4.1.1.  Monsanto Climate FieldView

4.1.2.  aWhere        

4.2.    Applied cloud transactional systems for crop and livestock

4.2.1.  Argian

4.2.2.  Farmers Edge

4.3.    Cloud platforms and applications with advanced analytics and modeling       

4.3.1.  Granular Business    

4.3.2.  Conservis     

4.4.    Cloud platforms and applications for fleet management of mobile and stationary mechanization in the field

4.4.1.  John Deer Operations Center       

4.4.2.  Trimble Connected Farm    

4.5.    Cloud platforms and applications for climate control in agricultural buildings and structures

4.5.1.  Metabolic Robots    

5. Profiles cloud platforms (under development and operating) and applications for agriculture in Russia      

5.1.    Exact Farming         

5.2.    VitalFields     

6. Potential consumers of digital services in Russia and compliance of their financial capabilities with the pricing policy and monetization models of global and Russian providers of digital services for agriculture

6.1.    Assessment of the existing and potential consumption in Russia through the demand elasticity model (the ratio of the size of agricultural business and the possible amount of payment for digital services)

6.2.    Assessment of the possible economic impact


List of pictures

Pic. 1. Specific level of losses of agricultural products in the context of regions at the stages of harvesting and marketing (production to retail) and consumption (consumer), kg per year  

Pic. 2. Level and structure of losses by stages of the value chain and types of agricultural products in rich (high level of mechanization), medium and poor (low level of mechanization) countries, %       

Pic. 3. Structure of trade margins in the typical chain of sales of agricultural products, %  

Pic. 4. Development of an ecosystem of cloud IoT platforms and applications for agriculture         

Pic. 5. Penetration level of IoT platforms and cloud applications for agriculture in the United States on the example of growing corn, in % of the total number of farms growing corn and corn-occupied land      

Pic. 3. Assessment of the regional structure and dynamics of the global market of cloud IOT platforms and services for digital agriculture, the fact for 2017 and the forecast for 2022, % and million dollars

Pic. 4. Assessment of the volume, product structure and dynamics of the global market of cloud IOT platforms and services for digital agriculture, the fact for 2014-2017 and the forecast for 2018-2022, million dollars.

Pic. 8. Dynamics of M&A transactions in AgTech in the world      

Pic. 9. Typical appearance of the platform for monitoring and optimization management of engineering systems and means of mechanization  

Pic. 10. Quantitative assessment of the impact of the main factors on the yield on the example of corn, in % yield growth and bushels of corn per acre

Pic. 11. Obtained in practice the effects of the use of adaptive automatic control of irrigation systems        

Pic. 12. Potential cumulative economic effect from the use of all component of digitalization of plant, billion. in 2050.

Pic. 13. Possible increase in corn yield using all components of digitalization of crop production, bushels per acre in 2050


List of tables

Table 1. Structure of the gross product production in agriculture in Russia and the United States in terms of farm size (J'son & Partners Consulting based on statistical data of Russia and the United States)

Table 2. Gross value of agricultural production per employee (labour productivity), thous. (The World Factbook, CIA)

Table 3. Number of tractors in agriculture per 100 ha of land in Russia compared to the USA, Germany, China and India (J'son & Partners Consulting based on national statistics)          

Table 4. Values of specific indicators incorporated in the market model    

Table 5. Basic IoT platforms and cloud services for agriculture    

Table 6. Platforms and applications with advanced Analytics and modeling

Table 7. Basic applied transactional systems for crop and livestock production

Table 8. Platforms and applications for the management and sharing of the fleet of mobile and stationary mechanization in the field     

Table 9. Platforms and applications for management of engineering systems of agricultural buildings and structures

Table 10. Availability of major cloud platforms and crop digitalization services for agricultural enterprises of various sizes in Russia