Digital Nitrogen Availability Modeling via Soil Layer Segmentation

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Solution Overview

Problem

Current methods for determining nutrient availability in soils are inefficient and often result in either wastage or insufficient nutrient application for crops, due to the complexity of factors affecting nutrient flow and uptake, such as moisture content, soil type, and temperature, which are difficult to model accurately without extensive computational resources.

Innovation Solution

Agricultural intelligence computer systems that receive and process field data, weather data, and soil data to create digital models of nutrient availability, including temperature, hydrology, and crop models, enabling intelligent nutrient application decisions by predicting future availability and optimizing application timing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If comprehensive modeling of moisture content, soil type, and temperature is performed to accurately predict nutrient availability, then prediction accuracy is improved, but computational complexity and resource requirements increase significantly

Engineering Contradiction:

Improvenutrient availability prediction accuracyVSAvoidcomputational model complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex soil system into multiple discrete layers, each with its own moisture content, temperature, and nutrient properties. This segmentation allows the model to capture vertical variability in soil conditions while maintaining computational tractability by solving simplified diffusion equations for each layer independently rather than attempting a continuous complex model of the entire soil profile.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms the complex partial differential equations governing nutrient transport into simplified algebraic equations by applying numerical integration methods and making reasonable assumptions about boundary conditions. This parameter transformation reduces computational complexity while preserving the essential physics of nutrient diffusion, advection, and crop uptake processes.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If nutrient application is increased to ensure crop needs are met, then crop yield is improved, but nutrient waste and environmental impact increase

Engineering Contradiction:

Improvecrop yieldVSAvoidnutrient waste

Core Design Contradiction:

ProductivityVSLoss of substance

Solution Approach 1:

The patent implements a feedback mechanism where the model continuously monitors predicted nutrient availability and compares it against crop requirements at each time step. Based on this feedback, the system dynamically adjusts nutrient application recommendations to match actual crop needs, preventing both under-application (which would limit yield) and over-application (which would cause waste and environmental harm).

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary modeling of nutrient availability trends and crop uptake patterns before making application decisions. By predicting future nutrient availability based on current soil conditions, weather forecasts, and crop development stage, the system can proactively schedule nutrient applications at optimal times, ensuring crops receive nutrients when needed most while minimizing losses from leaching, volatilization, or denitrification.

Inventive Principle:

Principle #10Preliminary action

3Loss of substance

If nutrient application is delayed to avoid waste, then environmental impact is reduced, but crop nutrient deficiency may occur

Engineering Contradiction:

Improvenutrient loss reductionVSAvoidcrop nutrient supply reliability

Core Design Contradiction:

Loss of substanceVSReliability

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent employs a dynamic modeling approach that continuously updates nutrient availability predictions as new information becomes available (soil moisture changes, temperature variations, crop growth stage transitions). This dynamic adaptation allows the system to respond to changing conditions in real-time, adjusting application timing to balance the competing objectives of preventing nutrient loss and ensuring reliable crop nutrient supply.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS9519861B1Generating digital models of nutrients available to a crop over the course of the crop's development based on weather and soil data

Publication Date: 2016.12.13 MONSANTO TECHNOLOGY LLC

AI summary

A system for generating digital models of nitrogen availability based on field data, weather forecast data, and models of water flow, temperature, and crop uptake of nitrogen and water is provided. In an embodiment, field data and forecast data are received by an agricultural intelligence computing system. Based on the received data, the agricultural intelligence computing system models changes in temperature of different soil layers, moisture content of different soil layers, and loss of nitrogen and water to the soil through crop uptake, leaching, denitrification, volatilization, and evapotranspiration. The agricultural intelligence computing system creates a digital model of nitrogen availability based on the temperature, moisture content, and loss models. The agricultural intelligence computing system may then send nitrogen availability data to a field manager computing device and/or use the nitrogen availability data to create notifications, recommendations, agronomic models, and/or control parameters for an application controller.