Spatial Yield Modeling for Agronomic Trial Location Selection

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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 for different treatment areas, compare results, and select optimal trial locations based on field data, generating prescription maps to implement beneficial treatments across the field.

Engineering Contradictions & Design Principles

VSEngineering 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 productivity

Engineering Contradiction:

Improvetrial effectiveness evaluationVSAvoidfield productivity

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent divides the field into multiple management zones based on soil type, topography, and historical yield data. Instead of using large strip trials across the entire field, the system segments the field into homogeneous zones and identifies specific test locations within each zone, reducing the overall trial area while maintaining statistical validity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies different trial designs to different management zones based on their specific characteristics. High-variance zones receive trials with larger sample sizes or repeated measurements, while low-variance zones require smaller trial areas. This localized approach optimizes trial effectiveness while minimizing the total area required.

Inventive Principle:

Principle #3Local quality

2Adaptability or versatility

If trials are implemented in regions with high innate variance, then more locations can be tested, but the statistical significance of yield changes is reduced

Engineering Contradiction:

Improvetrial location flexibilityVSAvoidyield change statistical significance

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system dynamically adjusts trial design parameters based on the measured variance of each management zone. For high-variance zones, the system increases the number of replicate measurements or extends the trial duration to achieve the same statistical power. For low-variance zones, smaller trial areas suffice, allowing those regions to be used for multiple simultaneous trials.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes key trial parameters (sample size, measurement frequency, trial duration) based on the inherent variance of each management zone. This parameter adjustment ensures that trials in high-variance regions maintain statistical significance while trials in low-variance regions use minimal area, optimizing both adaptability and measurement precision.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If trials are compared to neighboring regions' or prior years' yield, then baseline performance can be established, but the comparison may be affected by reasons other than treatment variance

Engineering Contradiction:

Improvebaseline performance establishmentVSAvoidtreatment effect accuracy

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system introduces management zones as an intermediary layer between the trial treatment and the yield comparison. Instead of directly comparing trial yields to neighboring regions or prior years, the system first normalizes yields within each management zone based on its historical performance and characteristics. This intermediary step removes the confounding effects of regional and temporal variability, isolating the true treatment effect.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11487254B2Utilizing spatial statistical models for implementing agronomic trials

Publication Date: 2022.11.01 MONSANTO TECHNOLOGY LLC

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.