Global Blockchain in the Agriculture and Food Supply Chain Market

Report ID : 1436 | Published : 2022-11-11 | Pages: | Format: PDF/EXCEL

The market size of the Global Blockchain in the Agriculture and Food Supply Chain Market in 2021 is valued at 202.17 million and is predicted to reach 5059.78 million by the year 2030 at an 43.25% CAGR during the forecast period.

Blockchain in agriculture and food supply chain is becoming increasingly important in the food and agricultural supply chain since it will increase transparency in producing food and agricultural products. Some of the main advantages of blockchain in agriculture and food supply chain that drive the market of the supply chain include the requirements for affordable and accessible foods in times of calamities, dietary management strategies, performance measurement, the record of the complete history of the product, fairer transfer of funds, customer satisfaction, and logistics. Additionally, the development of agricultural start-ups due to technological improvements in the market has positively influenced the market. The main driving force of market expansion is the rising concern over misappropriation and food waste. Another aspect driving market expansion is the rising demand for transparency. The sector for food and agriculture is seeing increased demand for the blockchain due to rising food fraud.

However, regulatory ambiguity, operational standards related to blockchain in agriculture and the food supply chain, and the need for in-depth expertise are the major factors restraining the market growth. Organizations have closed their locations, people have been told to stay at home, and strict home invasions have been implemented. These limitations will also affect the market growth.

Market Segmentation:

The agriculture and food supply chain's blockchain is segmented by type, applications, organization size, product and provider. Based on type, the market is segmented as public, private and hybrid/consortium. The market is segmented by application: growers, food manufacturers/processors, and retailers. The market is segmented by providers: application providers, middleware providers, and infrastructure providers. By organization size, the market is segmented into small and medium-sized enterprises and large enterprises. By applications, the market is segmented as product traceability, tracking, visibility, payment and settlement, smart contract, governance, risk, and compliance management.

Based on stakeholders, the grower's segment is accounted as a significant contributor to the blockchain in the agriculture and food supply chain market.

The grower category is expected to hold a significant share of the global blockchain in the agriculture and food supply chain market. The growers have a marginally more significant market share than food distributors or producers. This results from higher rates of cultivators like farmers using blockchain in agriculture and food supply chains. Blockchain in agriculture and food supply chain is anticipated to be used in the agricultural supply chain as a new approach to giving farmers a larger interest in the chain and additional opportunities to promote their products to customers.

The large Enterprise segment witnessed growth at a rapid rate.

The large enterprise segment is projected to increase in the global blockchain's agriculture and food supply chain market. To handle the agriculture and food supply chain, North America embraced cutting-edge technology much more quickly than other regions, as a large enterprise can provide clients with a customized approach based on innovation that addresses the unique needs of each supply chain. This enables businesses to meet the market's rising standards. Large companies emphasize implementing efficient data ways to categorize and foresee likely hazards and use this data to make quicker business choices. These elements are projected to fuel market expansion.

The North American blockchain in the region's agriculture and food supply chain market holds a significant revenue share.

The North American blockchain in the agriculture and food supply chain market is expected to register the highest market share in revenue shortly. To handle the agriculture and food supply chain, North America embraced cutting-edge technology much more quickly than other regions. As a result, the continent became the most significant market shareholder for blockchain in agriculture and the food supply chain. The region's most economically stable nations, the US and Canada, hold a sizable market share of the blockchain in the agriculture and food supply chain.

Europe region is estimated to show the highest growth in the market during the forecast period. Due to the region's increasing embrace of cutting-edge technology like blockchain and informatics. The blockchain in India's agriculture sector is anticipated to be driven by increasing investment from various stakeholders and the government to stimulate research and implementation of innovative technologies.

Competitive Landscape

The key players in the blockchain in the agriculture and food supply chain market have shifted their focus towards bio-based components for product manufacturing and are initiating significant strategies such as mergers, acquisitions, and joint ventures of major and domestic players to enhance product portfolios and strengthen their market footprint across the globe. Some of the major key players in the Blockchain in Agriculture and Food Supply Chain market are "IBM, Microsoft, Ambrosius, Sap Se, Origintrail, Provenance, Agridigital, Abaco Group, Ripe.Io, Vechain, Chainvine, Agrichain, Skuchain, Bext360, Fce Group Ag, Coin 22, Te-Food International Gmbh, Modum.Io Ag, Viveat, Eharvesthub Inc., Grainchain, Cargochain, Farm2kitchen Foods Pvt. Ltd., Genuino, Agri 10x .

Chapter 1. Methodology and Scope

1.1. Research Methodology

1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global Blockchain in Agriculture and Food Supply Chain Market Snapshot

Chapter 4. Global Blockchain in Agriculture and Food Supply Chain Market Variables, Trends & Scope

4.1. Market Segmentation & Scope

4.2. Drivers

4.3. Challenges

4.4. Trends

4.5. Investment and Funding Analysis

4.6. Industry Analysis – Porter’s Five Forces Analysis

4.7. Competitive Landscape & Market Share Analysis

4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis

5.1. by Type & Market Share, 2019 & 2030

5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2030 for the following by Type:

5.2.1. Public

5.2.2. Private

5.2.3. Hybrid/Consortium

Chapter 6. Market Segmentation 2: by Stakeholders Estimates & Trend Analysis

6.1. by Stakeholders & Market Share, 2019 & 2030

6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2030 for the following by Stakeholders:

6.2.1. Growers

6.2.2. Food Manufacturers/Processors

6.2.3. Retailers

Chapter 7. Market Segmentation 3: by Providers Estimates & Trend Analysis

7.1. by Providers & Market Share, 2019 & 2030

7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2030 for the following by Providers:

7.2.1. Application Providers

7.2.2. Middleware Providers

7.2.3. Infrastructure Providers

Chapter 8. Market Segmentation 4: by Organization Size Estimates & Trend Analysis

8.1. by Organization Size & Market Share, 2019 & 2030

8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2030 for the following by Organization Size:

8.2.1. Small And Medium-Sized Enterprises

8.2.2. Large Enterprises

Chapter 9. Market Segmentation 5: by Application Estimates & Trend Analysis

9.1. by Application & Market Share, 2019 & 2030

9.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2030 for the following by Application:

9.2.1. Product Traceability, Tracking, And Visibility

9.2.2. Payment And Settlement

9.2.3. Smart Contract

9.2.4. Governance, Risk, And Compliance Management

Chapter 10. Blockchain in Agriculture and Food Supply Chain Market Segmentation 6: Regional Estimates & Trend Analysis

10.1. North America

10.1.1. North America Blockchain in Agriculture and Food Supply Chain Market Revenue (US$ Million) Estimates and Forecasts by Type, 2022-2030

10.1.2. North America Blockchain in Agriculture and Food Supply Chain Market Revenue (US$ Million) Estimates and Forecasts by Stakeholders, 2022-2030

10.1.3. North America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Providers, 2022-2030

10.1.4. North America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Organization Size, 2022-2030

10.1.5. North America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Application, 2022-2030

10.1.6. North America Blockchain in Agriculture and Food Supply Chain Market Revenue (US$ Million) Estimates and Forecasts by country, 2022-2030

10.2. Europe

10.2.1. Europe Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Type, 2022-2030

10.2.2. Europe Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Stakeholders, 2022-2030

10.2.3. Europe Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Providers, 2022-2030

10.2.4. Europe Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Organization Size, 2022-2030

10.2.5. Europe Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Application, 2022-2030

10.2.6. Europe Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by country, 2022-2030

10.3. Asia Pacific

10.3.1. Asia Pacific Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Type, 2022-2030

10.3.2. Asia Pacific Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Stakeholders, 2022-2030

10.3.3. Asia-Pacific Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Providers, 2022-2030

10.3.4. Asia Pacific Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Organization Size, 2022-2030

10.3.5. Asia Pacific Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Application, 2022-2030

10.3.6. Asia Pacific Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by country, 2022-2030

10.4. Latin America

10.4.1. Latin America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Type, 2022-2030

10.4.2. Latin America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Stakeholders, 2022-2030

10.4.3. Latin America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Providers, 2022-2030

10.4.4. Latin America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Organization Size, 2022-2030

10.4.5. Latin America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Application, 2022-2030

10.4.6. Latin America Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by country, 2022-2030

10.5. Middle East & Africa

10.5.1. Middle East & Africa Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Type, 2022-2030

10.5.2. Middle East & Africa Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Stakeholders, 2022-2030

10.5.3. Middle East & Africa Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Providers, 2022-2030

10.5.4. Middle East & Africa Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Organization Size, 2022-2030

10.5.5. Middle East & Africa Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by Application, 2022-2030

10.5.6. Middle East & Africa Blockchain in Agriculture and Food Supply Chain Market revenue (US$ Million) by country, 2022-2030

Chapter 11. Competitive Landscape

11.1. Major Mergers and Acquisitions/Strategic Alliances

11.2. Company Profiles

11.2.1. IBM

11.2.2. Microsoft

11.2.3. Arc-Net

11.2.4. Ambrosus

11.2.5. Sap Se

11.2.6. Origintrail

11.2.7. Provenance

11.2.8. Agridigital

11.2.9. Abaco Group

11.2.10. Ripe.Io

11.2.11. Vechain

11.2.12. Chainvine

11.2.13. Agrichain

11.2.14. Skuchain

11.2.15. Bext360

11.2.16. Fce Group Ag

11.2.17. Coin 22

11.2.18. Te-Food International Gmbh

11.2.19. Modum.Io Ag

11.2.20. Viveat

11.2.21. Eharvesthub Inc.

11.2.22. Grainchain

11.2.23. Cargochain

11.2.24. Farm2kitchen Foods Pvt. Ltd.

11.2.25. Genuino

11.2.26. Agri 10x

11.2.27. Other Prominent Players

By type:

  • Public
  • Private
  • Hybrid/Consortium

By stakeholders:

  • Growers
  • Food manufacturers/processors
  • Retailers

By providers:

  • Application providers
  • Middleware providers
  • Infrastructure providers

By organization size:

  • Small and medium-sized enterprises
  • Large enterprises

By application:

  • Product traceability, tracking, and visibility
  • Payment and settlement
  • Smart contract
  • Governance, risk, and compliance management

By Region-

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of Middle East and Africa

InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.

Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.

Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.

Secondary research

The secondary research sources that are typically mentioned to include, but are not limited to:

  • Company websites, financial reports, annual reports, investor presentations, broker reports, and SEC filings.
  • External and internal proprietary databases, regulatory databases, and relevant patent analysis
  • Statistical databases, National government documents, and market reports
  • Press releases, news articles, and webcasts specific to the companies operating in the market

The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista

Primary Research:

Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies

The contributors who typically take part in such a course include, but are not limited to: 

  • Industry participants: CEOs, CBO, CMO, VPs, marketing/ type managers, corporate strategy managers, and national sales managers, technical personnel, purchasing managers, resellers, and distributors.
  • Outside experts: Valuation experts, Investment bankers, research analysts specializing in specific markets
  • Key opinion leaders (KOLs) specializing in unique areas corresponding to various industry verticals
  • End-users: Vary mainly depending upon the market

Data Modeling and Analysis:

In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.

The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.

To know more about the research methodology used for this study, kindly contact us/click here.

Need Complete Report ?
Our Clients