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💬 Blockchain in Farming

💬 Blockchain in Farming

Tesco Issue Warning

July 18, 2024

Good Morning. Rain will taper off in Northern Ireland and western Scotland throughout the day. Elsewhere, it will be mostly dry with sunny spells, feeling very warm. Tomorrow will be mostly dry and very warm or hot. Expect thundery showers on Saturday, with hot conditions continuing in the east or southeast. Sunday will be cooler and cloudier with some rain likely.

In todays email:

Blockchain in Agriculture and Food Supply Chain Market Trends.
AI in Precision Agriculture: Potential and Legal Risks.
Tesco Issues Warning to Those Buying Cheese.

Blockchain in Agriculture and Food Supply Chain Market Trends

Advance Market Analytics has released a new research report titled "Blockchain in Agriculture and Food Supply Chain Market Insights to 2030." This 232-page report includes detailed tables and charts and provides an overview of the latest trends, drivers, challenges, and opportunities in the market. The growth of this market is mainly driven by increased R&D spending worldwide.

Key Highlights:

Market Drivers: The market is driven by rising consumer concerns about food safety and the need for transparency in the supply chain, as well as the growth of online trading and tracking systems, particularly during COVID-19.
Market Trends: There is increasing popularity of blockchain among retailers and distributors for better supervision and data management, alongside concerns about food wastage and post-harvest losses.
Growth Opportunities: Increased funding and investments in agri-food blockchain present significant growth opportunities. There is also potential for adopting blockchain solutions to simplify supply chain complexities in agriculture.

Market Segmentation:

By Type: Public, Private, Hybrid/Consortium
By Application: Product traceability, tracking and visibility, payment and settlement, smart contracts, governance, risk and compliance management
By Organization Size: Small and medium-sized enterprises, large enterprises
By Stakeholders: Growers, food manufacturers/processors, retailers
By Providers: Application providers, middleware providers, infrastructure providers

Key Players: IBM (United States), TE-FOOD International GmbH (Europe), Microsoft (United States), ACR-NET (Ireland), Ambrosus (Switzerland), SAP SE (Germany), Chainvine (United Kingdom), Ripe.io (United States), AgriDigital (Australia), OriginTrail (Slovenia)

Definition: Blockchain is a collection of data and records linked using cryptography. In agriculture, blockchain creates transparency in the farming process and supply chain, reducing transaction costs and saving time and money. It improves logistics, quality assurance, nutrient management plans, and more. The rise of smart agricultural systems offers opportunities for blockchain applications, supported by government initiatives to increase agricultural production.

As competition in the blockchain in agriculture and food supply chain market increases, businesses must monitor competitor strategies and market trends closely. Comprehensive analysis and analytics are essential for updating business models to meet current requirements.

AI in Precision Agriculture: Potential and Legal Risks

Precision agriculture, driven by artificial intelligence (AI) and machine learning (ML), has the potential to revolutionize farming. However, farmers and tech providers must be aware of the legal risks involved.

What is Precision Agriculture?

Precision agriculture uses advanced technologies like AI, robotics, cloud computing, and smart sensors to improve farming practices. These methods enhance crop yields, increase profit potential, and benefit the environment by making farming less intensive and more efficient.

Legal Risks and Challenges:

Liability Concerns:

Example: If an AI model recommends pesticide use that violates regulations, who is liable?
Potential issues with AI recommendations leading to regulatory violations.

Bias in AI Algorithms:

AI predictions can reflect biases in the training data, which may not apply to all farming situations.
Smaller farms could be disproportionately affected by AI trained on data from large-scale farms.

Data Privacy and Security:

Ownership and control of farm data are critical issues.
Risks include misuse of data, loss of privacy, and cyberattacks targeting agricultural data.

Hallucinations or Manipulated Results:

AI systems can produce incorrect or misleading outputs.
Example: Incorrect pesticide recommendations leading to legal and financial consequences for farmers.

Government Initiatives and Recommendations:

Government support for precision technologies and loan programs could help adoption.
Establishing data-sharing standards and cooperatives could enhance data privacy and trust.
Clear guidelines and legislation are needed to address liability, data ownership, and accuracy of AI outputs.

Conclusion: AI has immense potential to transform agriculture, but stakeholders must navigate legal risks carefully. Ongoing collaboration between tech developers, farmers, and policymakers is essential to maximize benefits and minimize risks in precision agriculture.