Spatial Statistical Model for Agronomic Trial Yield Analysis
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Solution Overview
Problem
Farmers face challenges in determining the effectiveness of new agricultural practices due to unclear benefits or detriments in agronomic trials, particularly when effects are small or statistically insignificant, and in identifying optimal locations for trials within an agricultural field.
Innovation Solution
An agricultural intelligence computer system utilizes spatial statistical models to compute yield values for different treatment areas, compare results, and generate prescription maps for implementing more beneficial treatments, while identifying efficient locations for trials within the field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional agronomic trials are implemented to test new practices, then farmers can evaluate treatment effects, but the trials require large field areas and may not provide statistically significant results when treatment effects are small
Solution Approach 1:
The patent transforms trial design from physical space allocation to statistical parameter optimization. By using spatial statistical models that analyze yield data patterns, treatment effects, and field variability parameters, the system determines statistical significance without requiring large physical trial areas. The model changes the parameters being measured and analyzed rather than increasing physical trial size.
Solution Approach 2:
The patent replaces the mechanical approach of physically dividing field areas for trials with a computational statistical model. Instead of using physical trial plots and mechanical measurement comparisons, the system uses spatial statistical algorithms to evaluate treatment effects across the field, substituting computational analysis for physical trial implementation.
2Reliability
If farmers implement trials with different treatments to identify beneficial practices, then they can improve management, but it is difficult to determine whether observed benefits are real or statistical anomalies
Solution Approach 1:
The patent introduces a spatial statistical model as an intermediary between raw yield data and trial result interpretation. This model acts as a mediator that accounts for field variability, spatial patterns, and treatment effects simultaneously, providing a more reliable interpretation of whether observed benefits are real or anomalies rather than direct comparison of raw yields.
Solution Approach 2:
The system incorporates feedback loops where the spatial statistical model continuously refines its analysis by comparing observed yield patterns against expected patterns under different treatment scenarios. The model provides feedback on the statistical significance and reliability of treatment effects, allowing farmers to make informed decisions based on validated results rather than ambiguous observations.
3Productivity
If strip trials are used to test treatments, then farmers can compare different practices, but the trials tie up large portions of the field and reduce productive acreage
Solution Approach 1:
The patent extracts the trial evaluation function from physical field plots and relocates it to a computational model. By taking out the need for physical trial strips and replacing them with spatial statistical analysis of yield data, the system maintains treatment effect detection capability while eliminating the requirement to remove productive acreage from cropping.
Data Source
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AI summary
Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment. In an embodiment, in response to selecting the second treatment, the system generates a prescription map, the prescription map including the second treatment. The system may also generate one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply the second treatment