Causal Machine Learning for Nitrogen Management
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Solution Overview
Problem
In computer-assisted agriculture, determining causal relationships between field management practices, soil characteristics, and crop yield is challenging, particularly for in-season nitrogen management, as existing methods rely on correlations rather than causality, leading to inefficient nitrogen fertilizer application and potential over-use, especially in fields with high organic matter or cover crops.
Innovation Solution
The implementation of machine learning algorithms and causal discovery methods to analyze historical soil metrics and weather data, generating directed acyclic graphs that identify causal relationships, allowing for site-specific nitrogen fertilizer recommendations and optimizing the use of the Pre-Sidedress Nitrate Test (PSNT) for improved crop yield prediction and fertilizer application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional correlation-based methods are used for nitrogen management recommendations, then the system is simple to implement, but the accuracy of yield prediction and fertilizer optimization is insufficient
Solution Approach 1:
The patent replaces traditional correlation-based statistical methods with machine learning algorithms that can identify causal relationships. This substitution enables the system to move from simple correlation analysis to complex causal inference, improving yield prediction accuracy by understanding the underlying mechanisms rather than just observing associations between variables.
Solution Approach 2:
The patent introduces causal discovery algorithms as an intermediary layer between raw agricultural data and management recommendations. This intermediary component processes historical data, soil metrics, and weather information to identify causal structures, enabling more accurate predictions while maintaining a modular system architecture that manages complexity.
2Productivity
If in-season nitrogen sidedress is applied based on traditional methods, then nitrogen can be added when crops need it most, but over-application occurs leading to wasted fertilizer and reduced grower profit
Solution Approach 1:
The patent implements a feedback mechanism using historical yield data, soil nitrate measurements, and weather information to continuously refine nitrogen recommendations. The causal machine learning model analyzes past responses to nitrogen applications and adjusts future recommendations accordingly, preventing over-application by learning from actual crop responses rather than relying on fixed thresholds.
Solution Approach 2:
The patent dynamically adjusts nitrogen management parameters based on causal relationships identified in the data. Instead of using fixed critical nitrate levels, the system modifies recommendation thresholds based on learned causal structures from historical data, allowing optimal nitrogen rates to adapt to varying soil conditions, crop responses, and environmental factors.
3Measurement precision
If the Pre-Sidedress Nitrate Test (PSNT) is used to guide nitrogen applications, then nitrogen management can be optimized, but measurement errors occur due to insufficient sampling and lack of knowledge on where to sample
Solution Approach 1:
The patent performs preliminary analysis of historical data and soil characteristics before conducting PSNT sampling. The causal discovery algorithms identify which soil parameters and locations are most strongly causally related to nitrogen response, allowing the system to pre-determine optimal sampling locations and strategies, thereby reducing measurement errors and sampling difficulties.
Solution Approach 2:
The patent creates a universal recommendation system that integrates multiple data sources including PSNT results, historical yields, soil properties, and weather data. This multi-functional approach allows the system to compensate for individual measurement uncertainties by synthesizing information from multiple sources, reducing the impact of sampling errors on final recommendations.
Data Source
AI summary
In one embodiment, a computer-implemented method includes receiving digital field data from an agricultural field representing one or more parameters of the field, soil, or crops in the field; retrieving historical data for the same field from one or more field databases; training and/or applying machine learning models to the field data and the historical data to derive representations of causality of one or more agronomic processes pertaining to the field; receiving user input specifying an anomaly to address via treatment, application or experiment; automatically adjusting the treatment, application or experiment to create a modified treatment, application or experiment that is most likely to generate result data that is usable to train machine learning models in an optimal manner.