Spatial Yield Modeling for Statistically Reliable Agronomic Trials
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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 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 for implementing beneficial treatments and minimizing trial areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If farmers implement agronomic trials to test new practices, then they can identify potential improvements in yield, but it becomes difficult to determine whether observed benefits are statistically significant or merely field-level aberrations
Solution Approach 1:
The patent introduces spatial statistical models as an intermediary between raw yield data and trial effectiveness determination. These models incorporate environmental covariates (soil type, topography, weather) to act as mediators that explain yield variation, allowing farmers to distinguish true treatment effects from environmental noise and make reliable decisions about practice effectiveness
Solution Approach 2:
The system implements feedback by using historical yield data and environmental information to continuously refine spatial statistical models. This feedback loop allows the models to learn from past trials and improve their ability to accurately attribute yield differences to treatment effects versus environmental factors, enhancing both measurement precision and result reliability
2Reliability
If farmers use large portions of the field for strip trials to achieve statistical significance, then they can obtain reliable trial results, but they reduce the area available for productive farming
Solution Approach 1:
The patent applies local quality by using spatial statistical models to identify specific locations within the field where environmental conditions are most favorable for detecting treatment effects. Instead of uniformly large strip trials, the system pinpoints optimal test locations where even small treatment effects can be detected with high statistical power, thereby maintaining trial reliability while minimizing the area required
Solution Approach 2:
The system changes parameters by incorporating multiple environmental covariates (soil properties, topography, weather data) into the statistical models. This multi-parameter approach increases the sensitivity and precision of trial detection, allowing for smaller, more efficient trial areas to achieve the same statistical reliability as much larger conventional trials
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 having received a first treatment, and second yield data for a second portion of the agronomic field having received a second treatment 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, where the yield value indicates 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 and generates a prescription map including the second treatment.