Soil Stratified Sampling for Accurate Low-Density SOC Measurement
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Solution Overview
Problem
Determining the level of nature-based CO2 sequestration in agricultural processes is complex and costly due to the high density of soil sampling required for measuring soil organic carbon (SOC), which is a barrier for landowners to enter carbon markets.
Innovation Solution
A system and method for optimal soil sampling with soil stratification that reduces sampling density by identifying appropriate soil strata parameters, determining the number and location of samples, and using Monte Carlo simulations to minimize costs while ensuring statistical confidence in SOC measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense grid soil sampling is performed to accurately measure SOC supply, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The patent divides the sampling domain into distinct strata based on soil characteristics (texture, organic matter content, depth). By segmenting the field into homogeneous groups, the system can use different sampling densities for different strata, reducing overall sampling requirements while maintaining measurement accuracy for each soil type.
Solution Approach 2:
The patent applies different sampling densities to different soil strata based on their specific characteristics. High-variance strata receive higher sampling density while low-variance strata receive lower density, optimizing the balance between measurement precision and cost across the entire field.
2Reliability
If high sampling density is used to ensure statistical confidence in SOC measurements, then reliability is improved, but productivity decreases due to increased cost and time
Solution Approach 1:
The patent performs preliminary stratification based on available soil data before conducting actual sampling. This preliminary classification allows the system to pre-determine optimal sampling locations and densities for each stratum, ensuring statistical confidence is achieved with minimal sampling effort.
Solution Approach 2:
The patent changes the sampling parameters (density, location, depth) based on stratum characteristics. By adapting sampling parameters to match the variability and importance of each soil stratum, the system achieves reliable SOC measurements while minimizing the total number of samples required.
Data Source
AI summary
A system and method for determining optimal sampling parameters is described. The method gathers soil data from a soil data source, which is associated with a soil organic carbon (SOC) project area. The crop prediction engine then determines that the soil data is less than optimal, but that the soil data is sufficient to generate an optimal sampling plan. The method completes a Monte Carlo simulation, which generates an empirical sampling distribution. The optimal sampling plan is determined by defining a margin of error, which provides a deviation from a predictive analysis of measured soil chemistry for a plurality of collected soil samples, and performing Monte Carlo simulations that include one Monte Carlo simulation having a lowest sampling density that satisfies the margin of error. The optimal sampling plan has the lowest sampling density and includes one or more sampling locations.