Agricultural Field Trial Modeling for Quantified Practice Benefits
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Solution Overview
Problem
Agricultural field managers face challenges in identifying and implementing effective changes to management practices due to the lack of quantifiable benefits and the risk of external factors influencing results, making it difficult to determine whether improvements are from new practices or other factors.
Innovation Solution
An agricultural intelligence computer system that receives field data from multiple fields, identifies target fields for trials, determines optimal locations for implementing changes, tracks compliance with trial protocols, and computes the benefits of these changes, allowing for informed decision-making on future practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If field managers implement new agricultural practices based on recommendations, then potential improvements in field performance may be achieved, but the risk of failure increases due to inability to quantify expected benefits
Solution Approach 1:
The system performs preliminary actions by conducting trials before full implementation. Field managers test new practices on a small scale first, collecting data to quantify benefits before committing to broader adoption, thus reducing implementation risk while maintaining productivity improvement potential
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring trial results and using this information to inform future decisions. Quantifiable data from trials provides feedback on actual practice effectiveness, enabling field managers to make informed decisions about scaling successful practices
2Ease of operation
If field managers adopt recommended practices without quantifiable benefit analysis, then implementation simplicity is maintained, but the ability to determine practice effectiveness is lost
Solution Approach 1:
The system segments the field into trial areas and control areas, allowing separate measurement of practice effects. This segmentation enables precise benefit quantification while maintaining ease of operation by using standardized trial protocols that are straightforward to implement
Solution Approach 2:
The system introduces an intermediary layer of trial implementation and data collection infrastructure. This intermediary mechanism captures precise measurement data while keeping the core agricultural practices simple and easy to execute, bridging the gap between operational simplicity and measurement precision
3Measurement precision
If trials are conducted to quantify practice benefits, then measurement precision is improved, but device and process complexity increases
Solution Approach 1:
The system enables self-service by allowing field managers to conduct trials using their own resources and existing farm equipment. The trial protocol is designed to be self-executing with automated data collection, reducing the need for external complex infrastructure while maintaining high measurement precision
Solution Approach 2:
The system uses parameter changes in the trial design, such as varying only one practice parameter at a time while holding others constant. This controlled approach achieves precise measurement of individual practice effects without requiring complex multi-variable experimental setups
4Measurement precision
If comprehensive field data is collected from multiple fields, then measurement precision and benefit quantification improve, but loss of time and data processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-planning trial designs and pre-positioning measurement infrastructure before data collection begins. This preliminary preparation streamlines the actual data collection process, reducing the time required while maintaining comprehensive data gathering across multiple fields
Solution Approach 2:
The system ensures continuity of useful action by implementing automated, continuous data collection during trials. Rather than intermittent manual measurements, sensors and monitoring systems continuously capture data, improving measurement precision without proportionally increasing processing time
Data Source
AI summary
A system for implementing a trial in one or more fields is provided. In an embodiment, a agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device.