South America AI in Agriculture Market Analysis from 2022 to 2034 Containing Market Size, Share along with its CAGR, Forecast and Trends

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South America AI in Agriculture Market Analysis — Presence

Geographical Analysis

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Country Analysis

South America AI in Agriculture Market Analysis 2026

Region / Country 2021 (A)2025 (A)2033 (P) CAGR
South America AI in Agriculture Totalxxxxxxxxxxxx22.9%
Brazilxxxxxxxxxxxx23.5%
Argentinaxxxxxxxxxxxx23.8%
Colombiaxxxxxxxxxxxx22.7%
Peruxxxxxxxxxxxx23.1%
Chilexxxxxxxxxxxx23.2%
Rest of South Americaxxxxxxxxxxxx22%

A = Actual · E = Estimated · P = Projected · 🔒 Locked values require full access. Click headers to sort.

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Segmentation Analysis

Technology breakdown · Illustrative 2025 shares shown free · Full-period forecast in paid report.

Machine Learning South America AI in Agriculture Market Analysis
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Machine Learning Market Size 2025
XX %
CAGR
Locked · Full data in paid report
Computer Vision South America AI in Agriculture Market Analysis
XX
Computer Vision Market Size 2025
XX %
CAGR
Locked · Full data in paid report
Predictive Analytics South America AI in Agriculture Market Analysis
XX
Predictive Analytics Market Size 2025
XX %
CAGR
Locked · Full data in paid report

Market size by (Illustrative, 2025)
Share distribution (2025)

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Competitor Analysis

Competitive Landscape of the AI in Agriculture Market

The competitive landscape of the AI in agriculture market is dynamic, featuring major players like John Deere, Bayer CropScience, and IBM, who lead with advanced technologies in precision farming, crop monitoring, and autonomous machinery. Smaller innovators, such as FarmWise and Taranis, offer specialized AI solutions for pest management and data analytics. The market is characterized by rapid technological advancements and collaborations between tech companies and agricultural firms, driving growth and shaping the future of sustainable farming practices.

In July 2024, Google has introduced its Agricultural Landscape Understanding (ALU) tool to provide farmers with essential insights and boost crop productivity. Initially available in a limited capacity, this tool aims to revolutionize farming practices by integrating data-driven approaches. Utilizing high-resolution satellite imagery and machine learning, the ALU will outline field boundaries and offer guidance on drought management, irrigation, and market accessibility, among other aspects. (Source: https://www.livemint.com/companies/googles-ai-based-agricultural-information-tool-for-india-to-offer-information-on-drought-preparedness-irrigation-11721285446847.html ) In May 2022, AGCO has acquired JCA Industries to enhance its engineering and software development expertise, aiming to speed up the creation of advanced automated and autonomous machinery. (Source: https://investors.agcocorp.com/news-releases/news-release-details/agco-acquires-jca-industries ) In April 2022, Deere & Company and GUSS Automation established a joint venture with an LLC based in Kingsburg, California. The Global Unmanned Spray System (GUSS) is a leading innovator in semi-autonomous spraying technology specifically designed for use in orchards and vineyards. (Source: https://www.deere.com/en/our-company/static/john-deere-forms-joint-venture-with-guss-automation/ )
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Top Companies (In no particular order)2022 (A)2023 (A)2024 (A)2025 (A)
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Report Scope & Analysis

According to Cognitive Market Research, the global AI in Agriculture market size was estimated at USD 2142.20 Million, out of which the Latin America held the market share of around 5% of the global revenue with a market size of USD 107.11 million in 2024 and will grow at a compound annual growth rate (CAGR) of 22.9% from 2024 to 2031.

  • According to Cognitive Market Research, the Brazil AI in Agriculture market size was valued at USD 45.84 million in 2024 and is projected to grow at a CAGR of 23.5% during the forecast period. AI in Agricultures flourished in Brazil due to expanding agricultural sector and increasing demand for technology-driven solutions to enhance productivity.
  • Argentina's AI in Agriculture market had a market share of USD 17.99 million in 2024 and is projected to grow at a CAGR of 23.8% during the forecast period. Argentina's investments in agri-tech is driving the AI in Agriculture market.
  • Colombia AI in Agriculture market is projected to witness growth at a CAGR of 22.7% during the forecast period, with a market size of USD 9.53 million in 2024.
  • Peru AI in Agriculture market is projected to witness growth at a CAGR of 23.1% during the forecast period, with a market size of USD 8.78 million in 2024.
  • Chile AI in Agriculture market is projected to witness growth at a CAGR of 23.2% during the forecast period, with a market size of USD 7.71 million in 2024.
  • The Rest of Latin America's AI in Agriculture market is projected to witness growth at a CAGR of 22.0% during the forecast period, with a market size of USD 17.24 million in 2024.
The Latin America region is growing in the AI in agriculture market due to its expanding agricultural sector and increasing demand for technology-driven solutions to enhance productivity. Government initiatives and investments in agri-tech are fostering innovation and adoption of AI tools. Additionally, the region's need to improve crop yields and manage resources efficiently drives the growth of AI applications, supporting more sustainable and profitable farming practices.

Analyst Conclusion

Our study will explain complete manufacturing process along with major raw materials required to manufacture end-product. This report helps to make effective decisions determining product position and will assist you to understand opportunities and threats around the globe.

The South America AI in Agriculture Market Analysis is witnessing significant growth in the near future.

In 2023, the Machine Learning segment accounted for a notable share of the South America AI in Agriculture Market Analysis.

Sneha Mali
Sneha Mali Verified Analyst
Research Analyst at Cognitive Market Research
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South America AI in Agriculture Market Analysis market size and growth rate is provided in the report covering 2021-2025 historical and 2025-2033 forecast data.
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Segments include Technology, Offering and additional sub-segments.
Regional analysis covers all major markets. The report identifies the dominant region and provides country-level data.
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South America AI in Agriculture Market Analysis — Table of Contents

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Report Scope
TechnologyMachine Learning, Computer Vision, Predictive Analytics
OfferingHardware, Software, Al-as-a-Service, Service
ApplicationPrecision Farming, Agriculture Robots, Livestock Monitoring, Drone Analytics, Labor Management, Others
List of CompetitorsBayer CropScience, John Deere, Corteva Agriscience, IBM, Microsoft, Syngenta, Monsanto, Climate Corporation, Trimble Inc., AG Leader Technology, PrecisionHawk, Raven Industries, FarmWise, Taranis, Arama

  • Chapter 1. Competitor Analysis (Subject to Data Availability (Private Players))

    • 1.1 Top Competitors Analysis
      • 1.1.1 Top Players Ranking 2024
      • 1.1.2 New Product Launch Analysis
    • 1.2 Company Profile (Data Subject to Availability) Sample Format
      • 1.2.1 Bayer CropScience
        • 1.2.1.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.1.2 Business Overview
        • 1.2.1.3 R&D Investment (Subject to data availability)
        • 1.2.1.4 Financials (Subject to data availability)
        • 1.2.1.5 Product Types Specification
        • 1.2.1.6 Business Strategy
        • 1.2.1.7 Recent Developments
        • 1.2.1.8 Management Change
        • 1.2.1.9 S.W.O.T Analysis
      • 1.2.2 John Deere
        • 1.2.2.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.2.2 Business Overview
        • 1.2.2.3 R&D Investment (Subject to data availability)
        • 1.2.2.4 Financials (Subject to data availability)
        • 1.2.2.5 Product Types Specification
        • 1.2.2.6 Business Strategy
        • 1.2.2.7 Recent Developments
        • 1.2.2.8 Management Change
        • 1.2.2.9 S.W.O.T Analysis
      • 1.2.3 Corteva Agriscience
        • 1.2.3.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.3.2 Business Overview
        • 1.2.3.3 R&D Investment (Subject to data availability)
        • 1.2.3.4 Financials (Subject to data availability)
        • 1.2.3.5 Product Types Specification
        • 1.2.3.6 Business Strategy
        • 1.2.3.7 Recent Developments
        • 1.2.3.8 Management Change
        • 1.2.3.9 S.W.O.T Analysis
      • 1.2.4 IBM
        • 1.2.4.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.4.2 Business Overview
        • 1.2.4.3 R&D Investment (Subject to data availability)
        • 1.2.4.4 Financials (Subject to data availability)
        • 1.2.4.5 Product Types Specification
        • 1.2.4.6 Business Strategy
        • 1.2.4.7 Recent Developments
        • 1.2.4.8 Management Change
        • 1.2.4.9 S.W.O.T Analysis
      • 1.2.5 Microsoft
        • 1.2.5.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.5.2 Business Overview
        • 1.2.5.3 R&D Investment (Subject to data availability)
        • 1.2.5.4 Financials (Subject to data availability)
        • 1.2.5.5 Product Types Specification
        • 1.2.5.6 Business Strategy
        • 1.2.5.7 Recent Developments
        • 1.2.5.8 Management Change
        • 1.2.5.9 S.W.O.T Analysis
      • 1.2.6 Syngenta
        • 1.2.6.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.6.2 Business Overview
        • 1.2.6.3 R&D Investment (Subject to data availability)
        • 1.2.6.4 Financials (Subject to data availability)
        • 1.2.6.5 Product Types Specification
        • 1.2.6.6 Business Strategy
        • 1.2.6.7 Recent Developments
        • 1.2.6.8 Management Change
        • 1.2.6.9 S.W.O.T Analysis
      • 1.2.7 Monsanto
        • 1.2.7.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.7.2 Business Overview
        • 1.2.7.3 R&D Investment (Subject to data availability)
        • 1.2.7.4 Financials (Subject to data availability)
        • 1.2.7.5 Product Types Specification
        • 1.2.7.6 Business Strategy
        • 1.2.7.7 Recent Developments
        • 1.2.7.8 Management Change
        • 1.2.7.9 S.W.O.T Analysis
      • 1.2.8 Climate Corporation
        • 1.2.8.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.8.2 Business Overview
        • 1.2.8.3 R&D Investment (Subject to data availability)
        • 1.2.8.4 Financials (Subject to data availability)
        • 1.2.8.5 Product Types Specification
        • 1.2.8.6 Business Strategy
        • 1.2.8.7 Recent Developments
        • 1.2.8.8 Management Change
        • 1.2.8.9 S.W.O.T Analysis
      • 1.2.9 Trimble Inc.
        • 1.2.9.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.9.2 Business Overview
        • 1.2.9.3 R&D Investment (Subject to data availability)
        • 1.2.9.4 Financials (Subject to data availability)
        • 1.2.9.5 Product Types Specification
        • 1.2.9.6 Business Strategy
        • 1.2.9.7 Recent Developments
        • 1.2.9.8 Management Change
        • 1.2.9.9 S.W.O.T Analysis
      • 1.2.10 AG Leader Technology
        • 1.2.10.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.10.2 Business Overview
        • 1.2.10.3 R&D Investment (Subject to data availability)
        • 1.2.10.4 Financials (Subject to data availability)
        • 1.2.10.5 Product Types Specification
        • 1.2.10.6 Business Strategy
        • 1.2.10.7 Recent Developments
        • 1.2.10.8 Management Change
        • 1.2.10.9 S.W.O.T Analysis
      • 1.2.11 PrecisionHawk
        • 1.2.11.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.11.2 Business Overview
        • 1.2.11.3 R&D Investment (Subject to data availability)
        • 1.2.11.4 Financials (Subject to data availability)
        • 1.2.11.5 Product Types Specification
        • 1.2.11.6 Business Strategy
        • 1.2.11.7 Recent Developments
        • 1.2.11.8 Management Change
        • 1.2.11.9 S.W.O.T Analysis
      • 1.2.12 Raven Industries
        • 1.2.12.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.12.2 Business Overview
        • 1.2.12.3 R&D Investment (Subject to data availability)
        • 1.2.12.4 Financials (Subject to data availability)
        • 1.2.12.5 Product Types Specification
        • 1.2.12.6 Business Strategy
        • 1.2.12.7 Recent Developments
        • 1.2.12.8 Management Change
        • 1.2.12.9 S.W.O.T Analysis
      • 1.2.13 FarmWise
        • 1.2.13.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.13.2 Business Overview
        • 1.2.13.3 R&D Investment (Subject to data availability)
        • 1.2.13.4 Financials (Subject to data availability)
        • 1.2.13.5 Product Types Specification
        • 1.2.13.6 Business Strategy
        • 1.2.13.7 Recent Developments
        • 1.2.13.8 Management Change
        • 1.2.13.9 S.W.O.T Analysis
      • 1.2.14 Taranis
        • 1.2.14.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.14.2 Business Overview
        • 1.2.14.3 R&D Investment (Subject to data availability)
        • 1.2.14.4 Financials (Subject to data availability)
        • 1.2.14.5 Product Types Specification
        • 1.2.14.6 Business Strategy
        • 1.2.14.7 Recent Developments
        • 1.2.14.8 Management Change
        • 1.2.14.9 S.W.O.T Analysis
      • 1.2.15 Arama
        • 1.2.15.1 Company Basic Information, Manufacturing Base, Sales Area, and Competitors
        • 1.2.15.2 Business Overview
        • 1.2.15.3 R&D Investment (Subject to data availability)
        • 1.2.15.4 Financials (Subject to data availability)
        • 1.2.15.5 Product Types Specification
        • 1.2.15.6 Business Strategy
        • 1.2.15.7 Recent Developments
        • 1.2.15.8 Management Change
        • 1.2.15.9 S.W.O.T Analysis

  • Chapter 2. Global AI in Agriculture Market Analysis

    • 2.1 Global AI in Agriculture Market Analysis
    • 2.2 Global AI in Agriculture Market Analysis by Technology
    • 2.3 Global AI in Agriculture Market Analysis by Offering
    • 2.4 Global AI in Agriculture Market Analysis by Application

  • Chapter 3. South America AI in Agriculture Market Analysis

    • 3.1 South America AI in Agriculture Market Analysis
    • 3.2 South America AI in Agriculture Market Analysis by Country
    • 3.3 South America AI in Agriculture Market Analysis by Technology
    • 3.4 South America AI in Agriculture Market Analysis by Offering
    • 3.5 South America AI in Agriculture Market Analysis by Application
    • 3.6 South America AI in Agriculture Market Analysis by Key Players

  • Chapter 4. Brazil AI in Agriculture Market Analysis

    • 4.1 Brazil AI in Agriculture Market Analysis
    • 4.2 Brazil AI in Agriculture Market Analysis by Technology
    • 4.3 Brazil AI in Agriculture Market Analysis by Offering
    • 4.4 Brazil AI in Agriculture Market Analysis by Application
    • 4.5 Brazil AI in Agriculture Market Analysis by Key Players

  • Chapter 5. Argentina AI in Agriculture Market Analysis

    • 5.1 Argentina AI in Agriculture Market Analysis
    • 5.2 Argentina AI in Agriculture Market Analysis by Technology
    • 5.3 Argentina AI in Agriculture Market Analysis by Offering
    • 5.4 Argentina AI in Agriculture Market Analysis by Application
    • 5.5 Argentina AI in Agriculture Market Analysis by Key Players

  • Chapter 6. Colombia AI in Agriculture Market Analysis

    • 6.1 Colombia AI in Agriculture Market Analysis
    • 6.2 Colombia AI in Agriculture Market Analysis by Technology
    • 6.3 Colombia AI in Agriculture Market Analysis by Offering
    • 6.4 Colombia AI in Agriculture Market Analysis by Application
    • 6.5 Colombia AI in Agriculture Market Analysis by Key Players

  • Chapter 7. Peru AI in Agriculture Market Analysis

    • 7.1 Peru AI in Agriculture Market Analysis
    • 7.2 Peru AI in Agriculture Market Analysis by Technology
    • 7.3 Peru AI in Agriculture Market Analysis by Offering
    • 7.4 Peru AI in Agriculture Market Analysis by Application
    • 7.5 Peru AI in Agriculture Market Analysis by Key Players

  • Chapter 8. Chile AI in Agriculture Market Analysis

    • 8.1 Chile AI in Agriculture Market Analysis
    • 8.2 Chile AI in Agriculture Market Analysis by Technology
    • 8.3 Chile AI in Agriculture Market Analysis by Offering
    • 8.4 Chile AI in Agriculture Market Analysis by Application
    • 8.5 Chile AI in Agriculture Market Analysis by Key Players

  • Chapter 9. Rest of South America AI in Agriculture Market Analysis

    • 9.1 Rest of South America AI in Agriculture Market Analysis
    • 9.2 Rest of South America AI in Agriculture Market Analysis by Technology
    • 9.3 Rest of South America AI in Agriculture Market Analysis by Offering
    • 9.4 Rest of South America AI in Agriculture Market Analysis by Application
    • 9.5 Rest of South America AI in Agriculture Market Analysis by Key Players

  • Chapter 10. Technology Analysis

    • 10.1 Machine Learning
    • 10.2 Computer Vision
    • 10.3 Predictive Analytics

  • Chapter 11. Offering Analysis

    • 11.1 Hardware
    • 11.2 Software
    • 11.3 Al-as-a-Service
    • 11.4 Service

  • Chapter 12. Application Analysis

    • 12.1 Precision Farming
    • 12.2 Agriculture Robots
    • 12.3 Livestock Monitoring
    • 12.4 Drone Analytics
    • 12.5 Labor Management
    • 12.6 Others

  • Chapter 13. Qualitative Analysis (Subject to Data Availability)

    • 13.1 Market Drivers
    • 13.2 Market Restraints
    • 13.3 Market Trends
    • 13.4 Market Opportunity
    • 13.5 Technological Road Map (Subject to Data Availability)
    • 13.6 Product Life Cycle (Subject to Data Availability)
    • 13.7 Customer and Buyer Behavior Analysis
      • 13.7.1 Digital Engagement, Customer Experience & Relationship Analysis
      • 13.7.2 Customer Buying Behavior & Purchase Decision Analysis
      • 13.7.3 Vendor Selection, Supplier Preferences & Future Demand Trends
      • 13.7.4 Pricing, Affordability & Value Perception Analysis
      • 13.7.5 Customer Segmentation & Demand Pattern Analysis
    • 13.8 Industrial Chain Analysis (Subject to Data Availability)
      • 13.8.1 Industry Chain Analysis
      • 13.8.2 Manufacturing Cost Analysis
      • 13.8.3 Supply Side Analysis
        • 13.8.3.1 Raw Material Analysis
        • 13.8.3.2 Raw Material Procurement Analysis
        • 13.8.3.3 Raw Material Price Trend Analysis
    • 13.9 PESTEL Analysis
      • 13.9.1 Environmental Factors
      • 13.9.2 Legal Factors
      • 13.9.3 Technological Factors
      • 13.9.4 Economic Factors
      • 13.9.5 Political Factors
      • 13.9.6 Social Factors
    • 13.10 Porter’s Five Forces Analysis
      • 13.10.1 Degree of Competition
      • 13.10.2 Threat of Substitutes
      • 13.10.3 Threat of New Entrants
      • 13.10.4 Bargaining Power of Buyers
      • 13.10.5 Bargaining Power of Suppliers
    • 13.11 Patent Analysis (Subject to Data Availability)
    • 13.12 ESG Analysis
    • 13.13 Geopolitical Outlook
      • 13.13.1 Global Power Realignment & Strategic Alliances
      • 13.13.2 Geopolitical Risk Landscape & Conflict Hotspots
      • 13.13.3 International Trade Relations & Market Access Environment
      • 13.13.4 Regulatory & Policy Shifts Impacting Cross-Border Operations
      • 13.13.5 Supply Chain Resilience, Localization & Resource Nationalism
      • 13.13.6 Technology Sovereignty & Digital Geopolitics
      • 13.13.7 Strategic Implications for Investment, Growth & Market Entry
    • 13.14 AI & Market Transformation
      • 13.14.1 Competitive Landscape Disruption & Strategic Shifts
      • 13.14.2 AI-Driven Transformation of Industry Value Chain
      • 13.14.3 Evolution of Business Models & Revenue Streams
      • 13.14.4 AI-Driven Product, Service & Innovation Transformation
      • 13.14.5 Customer Behavior, AI Adoption & Future Market Evolution

  • Chapter 14. Research Findings

    • 14.1 Key Takeaways
    • 14.2 Analyst Point of View
    • 14.3 Assumptions and Acronyms

  • Chapter 15. Research Methodology and Sources

    • 15.1 Primary Data Collection
      • 15.1.1 Steps for Primary Data Collection
        • 15.1.1.1 Identification of KOL
      • 15.1.2 Backward Integration
      • 15.1.3 Forward Integration
      • 15.1.4 How Primary Research Help Us
      • 15.1.5 Modes of Primary Research
    • 15.2 Secondary Research
      • 15.2.1 How Secondary Research Help Us
      • 15.2.2 Sources of Secondary Research
    • 15.3 Data Validation
      • 15.3.1 Data Triangulation
    • 15.4 Data Representation

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Research Framework · 70:30 Primary:Secondary

Our Proprietary Methodology

Cognitive Market Research and Consulting "The Full Truth" methodology — a rigorous triangulation process that combines primary research, secondary validation, and expert calibration. Implemented by Sneha Mali and team for the South America AI in Agriculture Market Analysis Market analysis.

01

Primary Intelligence Gathering

Direct interviews with 50+ industry stakeholders including manufacturers, distributors, end-users, and regulatory bodies across all six regions.

02

Secondary Data Triangulation

Cross-referencing against trade databases, customs records, financial filings, patent databases, and verified industry publications.

03

Expert Validation Protocol

Each data point undergoes validation by minimum two independent domain experts with 15+ years of industry experience.

04

Athenaeum AI Processing

Our proprietary AI platform aggregates, normalizes, and identifies patterns across 10,000+ data points to surface non-obvious insights.

05

Editorial & QA Review

Final review by senior analysts ensures accuracy, coherence, and actionability of all insights and recommendations.

Data Assurance Metrics
Data Points Validated 10,400+
Expert Interviews 54
Countries Covered 39+
Company Profiles 22+
Forecast Accuracy (Historical) 94.2%
Analytical Coverage
Market Sizing Revenue Forecast CAGR Analysis Competitor Benchmarking SWOT Porter's Analysis PESTEL Value Chain ESG Analysis Tariff Impact Patent Mapping Tech Trends

To maintain the integrity of our proprietary methodology and protect our elite expert network, specific source disclosures are reserved for full-access partners. Our research framework is anchored by a 70:30 primary-to-secondary ratio, ensuring your strategy is driven by real-time market intelligence rather than recycled, publicly available, or AI-generated data. Every deliverable includes an exhaustive source directory and grants direct analyst access.

Sources from the Agriculture Industry

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