# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Data Management
#### 4.1.2 Data Sharing and Security
Potential for streamlined operations: By automating processes such as insurance claims (reference [6]) and implementing frameworks for high-throughput crop breeding data (reference [87]), blockchain solutions can reduce manual overhead, shorten processing times, and foster more efficient data handling workflows. Disadvantages: high computational and energy costs: Although some studies ([73]) claim reduced costs in certain areas, blockchain frameworks--especially those using energy-intensive consensus mechanisms--may still demand considerable computational power. This challenge may negate some of the purported efficiency gains, particularly in resource-constrained IoT settings. Scalability issues: References that discuss high-throughput use cases (e.g., [87]) often point out that while blockchain can secure and track data effectively, scaling these systems to accommodate large volumes of data remains a challenge. Current blockchain infrastructures may not always cope well with the speed and frequency of data generation in real-world IoT applications. Complexity of integration: Merging blockchain with existing IoT infrastructures (references [83; 84; 85; 86]) demands careful planning and technical expertise. Network architecture, communication protocols, and consensus mechanisms must be aligned to ensure compatibility and optimal performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Targeted Challenges** & **Papers** & **Short Introductions** & **Blockchain-Enabled Opportunities** \\ \hline \multirow{6}{*}{Data Sharing and Security} & \multirow{2}{*}{[91]} & The paper combines IoT technology with Ethereum-based blockchain to enhance precision agriculture by securing and making irrigation data more reliable. & By integrating blockchain technology with IoT for agriculture, decentralized data management is introduced, which enhances security, transparency, and integrity in smart irrigation systems. \\  & & This study explores the enhancement of data security in smart farming through the application of blockchain technology, focusing on protecting sensitive crop growth parameters. & Blockchain technology presents a groundbreaking potential for managing IoT data, offering a secure, decentralized, and immutable system for handling sensitive data in agriculture. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Best Results: Hybrid (On-/Off-Chain) storage approaches: Some papers suggest combining on-chain and off-chain solutions to balance security with scalability. For instance, large or sensitive datasets can be stored off-chain, while key integrity hashes remain on-chain to maintain authenticity and traceability. Efficient consensus mechanisms: References [89; 91] note that using optimized consensus algorithms (e.g., PBFT) can simultaneously improve the speed of data processing and maintain strong security guarantees. Cross-domain integration: Beyond agriculture, studies highlight that blockchain's decentralized approach could enhance trust in any ecosystem requiring reliable, tamper-proof data management. By adopting a multi-layered architecture (e.g., local data aggregation plus decentralized verification), solutions can adapt to diverse use cases in manufacturing, supply chain, or smart cities. Emphasis on collaboration and standardization: A key takeaway is the importance of establishing common standards and protocols, ensuring that disparate IoT devices and data sources can interoperate seamlessly within a blockchain framework. This collaboration among researchers, practitioners, and industry players can accelerate widespread adoption and foster more robust, secure systems. In summary, the table's entries illustrate that blockchain-based solutions can offer significant benefits for data management, storage, sharing, and security--particularly in IoT- and agriculture-related contexts--while also facing challenges related to scalability, integration complexity, and energy costs.