Agricultural Data Intelligence Service for Near-Real-Time Market Metrics
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Solution Overview
Problem
The USDA AMS specialty crop reports provide limited and inaccurate information due to their reliance on voluntary data and lack of real-time updates, failing to meet the dynamic and competitive needs of the modern agricultural market, especially for perishable commodities.
Innovation Solution
An Agricultural Data Intelligence service that aggregates transactional data from multiple subscribers to provide near-real-time market metrics and benchmark data, ensuring data accuracy and completeness by implementing minimum aggregate levels, anonymization, and load balancing, while preventing data leakage and free riding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If USDA AMS specialty crop reports are used to provide market information, then a starting point for industry data is available, but the data is unreliable and inaccurate due to limited observation points and lack of real-time updates
Solution Approach 1:
The patent combines transactional data from multiple subscribers (growers, marketers, distributors) into a unified data pool. This aggregation of diverse data sources creates a more comprehensive and reliable market information system that overcomes the limitations of single-source USDA reports.
Solution Approach 2:
The system implements continuous data collection and updates from multiple subscribers throughout the day, providing near-real-time market information. This continuous action replaces the periodic, limited observation points of traditional reports with ongoing, comprehensive market tracking.
2Ease of manufacture
If voluntary information from growers and marketers is collected for FOB pricing and movement reports, then data collection is simple and low-cost, but the resulting data tables are missing information and lack transparency
Solution Approach 1:
The system creates a universal data collection framework that serves multiple functions: collecting transactional data, validating completeness, aggregating from multiple sources, and providing various market metrics. This multi-functional approach ensures comprehensive data collection while maintaining ease of participation for subscribers.
Solution Approach 2:
The system implements feedback mechanisms where data completeness is monitored and validated. When minimum aggregate levels are not met, the system identifies gaps and encourages additional data submission, ensuring comprehensive information collection while maintaining voluntary participation.
3Device complexity
If AMS specialty crop reports are updated on a daily basis, then data processing is manageable, but the data lags 1-4 days behind the industry and is not timely for perishable commodities
Solution Approach 1:
The system performs preliminary data aggregation and validation during the day as transactions occur, rather than waiting for end-of-day processing. This preliminary action ensures data is ready for near-real-time dissemination, reducing the time lag for perishable commodity decision-making.
Solution Approach 2:
The system dynamically adjusts data update frequencies and processing priorities based on commodity type, market conditions, and subscriber needs. For perishable commodities, the system provides more frequent, near-real-time updates, while adjusting processing complexity accordingly.
4Loss of information
If transactional data from multiple subscribers is aggregated to provide market metrics, then comprehensive market data is available, but data leakage and free riding must be prevented
Solution Approach 1:
The system acts as an intermediary that aggregates and anonymizes transactional data before dissemination. By processing data through this intermediate layer, the system provides comprehensive market metrics while protecting individual subscriber information and preventing direct data leakage.
Solution Approach 2:
The system extracts and separates identifiable subscriber information from the aggregated market data. This extraction ensures that comprehensive market metrics are provided while individual subscriber data is protected, preventing free riding and data misuse.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for agricultural data intelligence. Data from multiple subscribers (growers and marketers) are aggregated to provide market intelligence to the subscribers. Data and insights are presented in a dashboard format that is intuitive and facilitates ease of use. To prevent data leakage, data anonymization and aggregation processes are implemented.