74. An Artificial Intelligence-driven Blockchain Platform for Data-enabled Valuation and Tokenization of United States Beef Cattle Systems.

Abstract Limited transfer of animal-level information across marketing stages constrains value-based pricing, reduces incentives for improved management, and restricts access to capital for small- and mid-sized cattle producers in the United States. An integrated decision-support platform that combines animal performance data, predictive analytics, and blockchain technology to enable transparent valuation and tokenization of beef cattle assets was developed. The system has capability to ingest real-time and historical data streams including genetics, health records, weight gain, and feed cost indicators, which are processed valuation models to generate dynamic estimates of animal and herd value under varying market conditions. Animal- and herd-level data are linked to digital tokens through blockchain-based smart contracts that automate ownership tracking, compliance verification, and revenue distribution. Both fungible tokens representing pooled herd investments and non-fungible tokens representing individual animals are supported, enabling flexible participation across production scales and management systems. A real-time analytics dashboard translates complex data into interpretable indicators for producers and investors, including projected performance, price sensitivity, disease risk, and exposure to feed and market volatility. Investment risk scores are generated using multi-factor models that integrate biological performance metrics with external market signals. To address data security and ethical considerations, the platform incorporates encryption, differential privacy, and role-based data access to protect sensitive ranch information while maintaining traceability and auditability. Blockchain immutability ensures that animal records, health history, and transaction data cannot be altered, supporting trust among supply chain participants without incentivizing harmful management practices. The system architecture emphasizes computational efficiency to minimize energy requirements associated with distributed ledger technologies. This framework addresses structural inefficiencies in the U.S. beef industry, where cattle of heterogeneous quality are often priced uniformly due to limited data continuity across ownership transitions. By preserving and analyzing animal-level information throughout the production and marketing lifecycle, the platform enables data-driven price discovery and rewards investments in genetics, health, and management. For small- and mid-sized producers, the ability to tokenize cattle assets creates new pathways to capital access and risk sharing without relinquishing operational control. We demonstrated the potential of integrating precision livestock data, artificial intelligence, and blockchain infrastructure to enhance economic efficiency and transparency. The approach provides a scalable foundation for future decision-support tools that link biological performance to financial outcomes, supporting more informed management, investment, and policy decisions in animal agriculture. Such integrated systems are particularly relevant as beef markets face increasing volatility, consolidation, and demand for transparency from downstream buyers and consumers. Adoption of our framework may support more resilient producer livelihoods, and improved capital allocation, while providing researchers and extension professionals with new tools to evaluate performance, risk, and sustainability outcomes across diverse cattle production systems.

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Publication Details

Journal
Journal of Animal Science
Published
2026-09-29
DOI
https://doi.org/10.1093/jas/skag272.222
Primary Topic
Food Supply Chain Traceability
Type
article
Field-Weighted Citation Impact
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article

74. An Artificial Intelligence-driven Blockchain Platform for Data-enabled Valuation and Tokenization of United States Beef Cattle Systems.

Karun Kaniyamattam, Megha Poyyara Saiju, Surada Suwansathit, Ananya Adiki et al.
Journal of Animal Science
Food Supply Chain Traceability
article

74. An Artificial Intelligence-driven Blockchain Platform for Data-enabled Valuation and Tokenization of United States Beef Cattle Systems.

Karun Kaniyamattam, Megha Poyyara Saiju, Surada Suwansathit, Ananya Adiki, Brandon Shim, Siddhi Mittal, Shreyas Kumar, Gregg Barfield, Hiya Sharma, Dylan Marintzer
article en

Abstract

Abstract Limited transfer of animal-level information across marketing stages constrains value-based pricing, reduces incentives for improved management, and restricts access to capital for small- and mid-sized cattle producers in the United States. An integrated decision-support platform that combines animal performance data, predictive analytics, and blockchain technology to enable transparent valuation and tokenization of beef cattle assets was developed. The system has capability to ingest real-time and historical data streams including genetics, health records, weight gain, and feed cost indicators, which are processed valuation models to generate dynamic estimates of animal and herd value under varying market conditions. Animal- and herd-level data are linked to digital tokens through blockchain-based smart contracts that automate ownership tracking, compliance verification, and revenue distribution. Both fungible tokens representing pooled herd investments and non-fungible tokens representing individual animals are supported, enabling flexible participation across production scales and management systems. A real-time analytics dashboard translates complex data into interpretable indicators for producers and investors, including projected performance, price sensitivity, disease risk, and exposure to feed and market volatility. Investment risk scores are generated using multi-factor models that integrate biological performance metrics with external market signals. To address data security and ethical considerations, the platform incorporates encryption, differential privacy, and role-based data access to protect sensitive ranch information while maintaining traceability and auditability. Blockchain immutability ensures that animal records, health history, and transaction data cannot be altered, supporting trust among supply chain participants without incentivizing harmful management practices. The system architecture emphasizes computational efficiency to minimize energy requirements associated with distributed ledger technologies. This framework addresses structural inefficiencies in the U.S. beef industry, where cattle of heterogeneous quality are often priced uniformly due to limited data continuity across ownership transitions. By preserving and analyzing animal-level information throughout the production and marketing lifecycle, the platform enables data-driven price discovery and rewards investments in genetics, health, and management. For small- and mid-sized producers, the ability to tokenize cattle assets creates new pathways to capital access and risk sharing without relinquishing operational control. We demonstrated the potential of integrating precision livestock data, artificial intelligence, and blockchain infrastructure to enhance economic efficiency and transparency. The approach provides a scalable foundation for future decision-support tools that link biological performance to financial outcomes, supporting more informed management, investment, and policy decisions in animal agriculture. Such integrated systems are particularly relevant as beef markets face increasing volatility, consolidation, and demand for transparency from downstream buyers and consumers. Adoption of our framework may support more resilient producer livelihoods, and improved capital allocation, while providing researchers and extension professionals with new tools to evaluate performance, risk, and sustainability outcomes across diverse cattle production systems.

Journal of Animal ScienceVol. 104(Supplement_5)
Entrust (GB), Block Engineering (United States) (US), Texas A&M University (US)
Openalex Percentile: Top 14%
Food Supply Chain Traceability
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