Method for detecting soil microbial carbon use efficiency based on fermented bed litter

By constructing a feature parameter set and machine learning model for fermentation bed materials, the problems of low efficiency and high cost of existing detection methods are solved, and rapid and accurate prediction of soil microbial carbon use efficiency is achieved, which can meet the needs of rapid decision-making in agricultural production.

CN121762814BActive Publication Date: 2026-06-09NANTONG COLLEGE OF SCIENCE & TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
NANTONG COLLEGE OF SCIENCE & TECHNOLOGY
Filing Date
2026-02-28
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing methods for detecting soil microbial carbon use efficiency are inefficient, complex to operate, costly, and lack efficient prediction systems, making it difficult to meet the needs of batch sample testing and rapid decision-making.

Method used

By constructing a set of characteristic parameters based on fermented bedding materials, including bedding physicochemical parameters and microbial community parameters, and using machine learning algorithms to establish a predictive model, rapid and non-destructive prediction of soil microbial carbon use efficiency can be achieved.

Benefits of technology

It enables rapid and accurate prediction of soil microbial carbon use efficiency after the application of fermentation bed substrate, reduces detection costs and time consumption, is suitable for high-throughput detection of batch samples, and improves detection accuracy and practicality.

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Abstract

The application discloses a soil microbial carbon utilization efficiency detection method based on fermented bed litter, and particularly relates to the technical field of soil utilization efficiency detection, which comprises the following steps: obtaining target fermented bed litter samples and corresponding post-application soil samples; determining the target fermented bed litter samples and the post-application soil samples, and constructing a characteristic parameter set; determining the microbial carbon utilization efficiency of the post-application soil samples by using an isotope labeling method; training input variables and output variables by using a machine learning algorithm, establishing a microbial carbon utilization efficiency prediction model; and predicting the carbon utilization efficiency of the target fermented bed litter to be detected. Through sample data acquisition, characteristic parameter determination, carbon utilization efficiency benchmark value determination, prediction model construction and training, and carbon utilization efficiency prediction, the application constructs a soil microbial carbon utilization efficiency detection method, and solves problems such as low detection efficiency, insufficient detection pertinence and accuracy, and lack of an efficient prediction system.
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Citation Information

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