Spatial Agronomic Trial Modeling for Small-Plot Yield Decisions
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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, especially when effects are small or statistically insignificant, and in identifying optimal locations for trials within a field to maximize efficiency.
Innovation Solution
An agricultural intelligence computer system utilizes spatial statistical models to compute yield values and select trial locations, generating prescription maps that implement beneficial treatments across the field based on data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strip trials are used to test different management practices, then the effectiveness of new practices can be evaluated, but a large portion of the field is required which reduces productive area
Solution Approach 1:
The patent changes the parameter of trial design from traditional strip trials to randomized complete block designs with replicated plots. This allows for more efficient statistical analysis and reduces the total area required while maintaining measurement precision through proper experimental design and spatial statistical modeling.
Solution Approach 2:
The field is divided into multiple smaller trial plots arranged in a randomized complete block design rather than using large continuous strips. This segmentation allows for better control of spatial variability and reduces the overall area needed while improving the precision of treatment effect estimation through replication and blocking.
2Loss of information
If trials are implemented to determine practice effectiveness, then better management decisions can be made, but the trial area produces reduced yield compared to optimal field use
Solution Approach 1:
The patent uses a randomized complete block design with replication that requires a smaller proportion of the field for trials compared to traditional strip trials. This partial action approach provides sufficient statistical power to make management decisions while minimizing the yield loss from trial areas, achieving the right balance between information gain and production maintenance.
3Measurement precision
If small effect treatments are tested in agronomic fields, then subtle improvements can be detected, but results are difficult to distinguish from field level aberrations and statistical anomalies
Solution Approach 1:
The patent implements spatial statistical modeling and analysis plans before conducting trials to account for spatial variability and potential confounding factors. This preliminary action includes designing randomized complete block layouts that control for spatial trends, allowing small treatment effects to be detected with confidence that they are not artifacts of field heterogeneity.
Solution Approach 2:
The patent employs rigorous statistical analysis including spatial modeling and replication to provide feedback on treatment effects. The randomized complete block design with multiple replicates allows for statistical testing that distinguishes true treatment effects from random variation and field level aberrations, ensuring reliable detection of small effects.
Data Source
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.