Agricultural Greenhouse Gas Emissions Estimation System
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Solution Overview
Problem
Current methods fail to accurately estimate agricultural greenhouse gas emissions, which are a significant contributor to global warming, and do not provide effective strategies for reduction.
Innovation Solution
A system and method that obtain and analyze farm data, including revenue, crop information, land use, and weather data, to estimate emissions and provide insights on reducing them, using a network of servers and databases to calculate emissions based on different data inputs and display actionable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources (revenue, crop information, land use data) are integrated to improve emissions estimation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the emissions estimation process into distinct modules: data collection module (revenue, crop information, land use data), data processing module, and emissions calculation module. Each module handles specific data types and operations, making the complex system manageable and maintainable while achieving comprehensive emissions estimation through integrated module outputs.
2Loss of information
If comprehensive farm data is collected to improve emissions estimation, then information completeness improves, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing data collection templates and structures for revenue, crop information, and land use data. These templates are prepared in advance and can be quickly populated during field visits or through automated data retrieval, reducing the time required for comprehensive data collection while ensuring all necessary information is captured.
3Adaptability or versatility
If multiple emissions estimation methods are provided for user selection, then adaptability improves, but ease of operation worsens
Solution Approach 1:
The system implements self-service functionality by automatically guiding users through the emissions estimation process. Based on the data that users provide (revenue, crop information, land use data), the system automatically selects and applies the appropriate emissions estimation method, eliminating the need for users to manually choose between different methodologies and simplifying the operational interface.
Data Source
AI summary
Methods, systems, and techniques for agricultural greenhouse gas estimation. Farm data in the form of at least one of revenue generated by a farm, crop information for one or more crops grown on the farm, and land use/farm practice data for land used on the farm to grow the one or more crops is obtained. An emissions estimate is determined based on the obtained data and caused to be displayed to the user via a graphical user interface. A user may be a person responsible for managing multiple farms. That user may be presented with aggregate emissions-related information for all farms, including projected future emissions under various scenarios, and may also iteratively experiment with different farm data values in order to attempt to reduce projected emissions or increase data quality/emissions estimate accuracy.