Agricultural Intelligence System for Field Trial Quantification
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Solution Overview
Problem
Field managers face challenges in identifying and implementing effective agricultural practices across multiple fields, as existing methods lack the ability to quantify benefits and account for external factors such as weather.
Innovation Solution
An agricultural intelligence computer system that receives field data from multiple fields, identifies target fields for trial implementation, sends trial participation requests to field managers, determines optimal trial locations, tracks trial compliance, and computes benefit values based on trial results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If field managers implement new agricultural practices based on recommendations, then potential improvements in field performance may be achieved, but the risk of uncertain outcomes increases due to inability to quantify benefits
Solution Approach 1:
The system implements a feedback mechanism by conducting controlled trials that measure actual performance outcomes of new agricultural practices. Field managers receive quantitative feedback data from these trials, allowing them to assess whether recommended practices deliver expected benefits before widespread implementation, thereby reducing uncertainty and improving decision reliability.
Solution Approach 2:
The system performs preliminary testing through controlled trials before recommending practices for widespread adoption. By conducting experiments in advance on selected fields and measuring outcomes, the system generates evidence-based recommendations that reduce risk for field managers implementing new practices across their operations.
2Measurement precision
If field managers rely on external factors like weather to explain performance variations, then attribution of results becomes ambiguous, but the ability to determine true practice effectiveness is lost
Solution Approach 1:
The system segments fields into control groups and treatment groups for controlled trials. By dividing the field population and applying different practices to different segments while holding other variables constant, the system can precisely attribute performance differences to specific practices rather than external factors like weather, thereby improving measurement precision and preserving causal relationship data.
3Productivity
If comprehensive field data collection and analysis systems are implemented, then trial identification and benefit quantification improve, but system complexity increases
Solution Approach 1:
The system employs a multi-functional data platform that serves multiple purposes: collecting field data, identifying trial opportunities, analyzing performance outcomes, and generating recommendations. By creating a universal system that performs all these functions integrated, the patent reduces overall complexity compared to having separate systems for each function, while still achieving comprehensive data collection and analysis capabilities.
Data Source
AI summary
A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.