Blockchain Papers

David Krause

No abstract is available for this record.

Duc Bui Tien, Bang K. Le, Triet M. Nguyen

The transition from paper-based to digital records has significantly enhanced healthcare practices, including the management of farm animal health.Electronic Health Records (EHRs), referred to as Animal Medical Records (AMRs), are essential for improving clinical workflows, supporting evidence-based decisions, and advancing veterinary research.However, the adoption of digital records introduces challenges related to data security, quality, and interoperability.This paper explores the use of blockchain technology to address these challenges.We propose a blockchain-based system for managing AMRs that leverages Non-Fungible Tokens (NFTs) and smart contracts to ensure data integrity, transparency, and accessibility.The system is evaluated through a proof-of-concept deployment on four EVM-supported platforms: BNB Smart Chain, Celo, Fantom, and Polygon.Our findings indicate that blockchain technology can enhance the security and efficiency of managing electronic health records for farm animals.

Suresh Neethirajan

The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.

E.J.B. Bleumer, G. André, G. van Duinkerken

Daily concentrate allowances for individual dairy cows are usually based on empiric models. These models are generally based on regression equations derived from population data and do not take into account individual and temporal variation. An application was implemented in common practice which consists of an adaptive model for estimating the actual individual response in milk yield on concentrate intake using individual real time process data. Before the application was implemented, a prototype was developed by a team consisting of biometricians, animal nutritionists and ICT application specialists. It was tested in an animal experiment and further developed into a proof of principal, which was implemented for testing in a common practical setting on a research farm. Because the results were very promising, a workshop was organised to introduce the concept to software, hardware and feed industries where they were challenged to participate. In the next collaborative phase with industry involvement the further implementation into a management system was stepwise: (1) technical documentation of algorithms, (2) programming, (3) verification of algorithms, (4) on-farm implementation of the integrated software, and (5) on-farm evaluation. During the implementation it became clear that steps 1 to 3 were not difficult to perform and did not take much time. Steps 4 and 5 were more complicated because: (1) correct data must be generated from the management system as an input for the model, and (2) the output of the model has to be interpreted correctly for calculating concentrate allowances in the management system. However, not only technical aspects of an implementation process are important, also the communication with end users and stakeholders requires particular attention, for successful implementation of a new concept. While testing and implementing the application it became clear that end users and stake holders were willing to accept and use the innovative concept but interpreted the outcome based on traditional population knowledge and paradigms.