Method and system for simulating microbial abundance of saline-alkali soil in seasonal freeze-thaw period

Abstract

The invention discloses a seasonal freeze-thaw saline-alkali soil microorganism abundance simulation method and system, and relates to the technical field of soil analysis, and the method comprises the following steps: collecting initial data, establishing a Logistic model, and simulating the change process of the seasonal freeze-thaw saline-alkali soil microorganism abundance; according to the simulation change process, a salinity value suitable for survival of soil microorganisms in the seasonal freezing and thawing period is obtained, the salinity value is optimized, and the optimal salinity value suitable for survival of the soil microorganisms is obtained; parameter adjustment of the Logistic model is carried out; and performing soil microorganism abundance simulation according to the adjusted Logistic model. According to the method, the Logistic model for determining the abundance of the soil microorganisms and the salinity concentration is used for accurately expressing the abundance change of the soil microorganisms, a large number of combination tests are reduced, the cost is saved, and technical support is provided for stable seasonal freeze-thaw period soil analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of soil analysis, and more particularly to a method and system for simulating the abundance of microorganisms in salinized soil during seasonal freeze-thaw periods. Background Art

[0002] One of the ways of global soil degradation is salinization. At present, the total salinized area in my country is 36 million hectares, of which 26% is salinized arable land. Soil salinization affects the global ecological environment and the stable development of agricultural production. At present, the salinized arable land area in Xinjiang is 1.2639 million hm2. 2 The salt content is complex, generally dominated by chlorides and sulfates. The seasonal freeze-thaw period is a critical period for the transition from the fallow period to the spring sowing period, and for the restoration of soil fertility. Soil microorganisms participate in the transformation and sequestration of SOC (organic carbon), drive the soil nitrogen nutrient cycle, and are an important component of the soil ecosystem. Salinized soil will not only lead to the deterioration of the physical and chemical properties of the soil, but also cause physiological drought of crops, leading to crop production areas, which seriously threatens agricultural food security and sustainable development.

[0003] Seasonal freezing and thawing of soil microorganisms has a profound impact on ecosystem function, diversity and soil health. Different freezing and thawing cycles can promote the reproduction of certain microbial species and increase the diversity of soil microorganisms. This diversity is crucial to the resilience and stability of ecosystems and is also one of the important indicators for judging soil health. However, there are many types of soil microorganisms, and the population can reach millions to billions per gram of soil. Experiments cannot accurately describe the changes in the abundance of soil microorganisms.

[0004] Therefore, how to propose a method and system for simulating the abundance of salinized soil microorganisms during seasonal freeze-thaw period and accurately describe the abundance changes of soil microorganisms is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method and system for simulating the abundance of salinized soil microorganisms during seasonal freeze-thaw period, which accurately describes the abundance changes of soil microorganisms. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for simulating microbial abundance in salinized soil during seasonal freeze-thaw period, comprising:

[0007] Collect initial data and build a logistic model;

[0008] The Logistic model was used to simulate the changes in soil microbial abundance in saline-alkali land during seasonal freeze-thaw period.

[0009] Obtain the salt content value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process, and optimize the salt content value to obtain the optimal salt content value suitable for the survival of soil microorganisms;

[0010] Adjust the parameters of the Logistic model based on the measured soil microorganism abundance and the optimal salt content value;

[0011] Simulate the soil microorganism abundance according to the adjusted Logistic model.

[0012] Optionally, the collection of initial data includes: collecting the concentrations of soil salts and soil microorganism abundances at different freeze-thaw periods during the seasonal freeze-thaw period.

[0013] Optionally, it also includes cleaning and preprocessing the initial data, including handling missing values, outliers, data standardization, and data normalization.

[0014] Optionally, the Logistic model is:

[0015]

[0016] Where y is the soil microorganism abundance (cfu / g), x is the soil salt concentration (g / kg), and A1, A2, x0, p are undetermined coefficients.

[0017] Optionally, the process of simulating the change of soil microorganism abundance in saline-alkali land during the seasonal freeze-thaw period by the Logistic model includes: using the soil salt concentration with P < m in the single-factor analysis as the independent variable and the microorganism abundance as the dependent variable, and performing multi-factor Logistic regression analysis using the forward method.

[0018] Optionally, the obtaining of the salt content value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process includes: constructing a nomogram model for predicting soil microorganism abundance, and obtaining the risk probability corresponding to the influence on soil microorganism abundance by calculating the sum of the scores of soil salt concentration.

[0019] Optionally, the optimizing the salt content value to obtain the optimal salt content value suitable for the survival of soil microorganisms includes: calculating the ACU value, goodness-of-fit test, and saturated model determination of the nomogram model for predicting soil microorganism abundance.

[0020] Optionally, it also includes calculating the absolute value of the residual. When the absolute value of the residual > n, it indicates that the data is an outlier in the multi-dimensional space.

[0021] Optionally, adjusting the parameters of the Logistic model based on the measured soil microbial abundance and the simulated soil microbial abundance under the optimal salinity value includes: performing consistency test on the simulated soil microbial abundance and the measured soil microbial abundance to complete the simulation correction; wherein the calibration index includes the determination coefficient R 2 , root mean square error RMSE and Nash efficiency coefficient NSE.

[0022] Optionally, a seasonal freeze-thaw period salinization soil microbial abundance simulation system, comprising:

[0023] Acquisition module: used to collect initial data and establish a Logistic model;

[0024] Fitting module: used to simulate the changing process of soil microbial abundance in saline-alkali land during seasonal freeze-thaw period through Logistic model;

[0025] Optimization module: used to obtain the salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process, and optimize the salt value to obtain the optimal salt value suitable for the survival of soil microorganisms;

[0026] Adjustment module: used to adjust the parameters of the Logistic model based on the measured soil microbial abundance and optimal salinity value;

[0027] Output module: used to simulate soil microbial abundance based on the adjusted Logistic model.

[0028] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for simulating the abundance of microorganisms in salinized soil during seasonal freeze-thaw period, which has the following beneficial effects:

[0029] The present invention discloses a method for simulating soil microbial abundance in salinized soil during seasonal freeze-thaw period, including: collecting initial data and establishing a Logistic model; simulating the change process of soil microbial abundance in saline-alkali land during seasonal freeze-thaw period by using the Logistic model; obtaining a salt value suitable for soil microbial survival during seasonal freeze-thaw period according to the simulated change process, and optimizing the salt value to obtain an optimal salt value suitable for soil microbial survival; adjusting the parameters of the Logistic model based on the measured soil microbial abundance and the optimal salt value; simulating soil microbial abundance according to the adjusted Logistic model. The present invention accurately describes the abundance change of soil microorganisms by determining the Logistic model of soil microbial abundance and salt concentration, reduces a large number of combined experiments, saves costs, and provides technical support for stable seasonal freeze-thaw period soil analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0031] Figure 1 A schematic flow chart of a method for simulating microbial abundance in salinized soil during seasonal freeze-thaw period provided by the present invention.

[0032] Figure 2 This is a structural framework diagram of a seasonal freeze-thaw period salinization soil microbial abundance simulation system provided by the present invention.

[0033] Figure 3 This is a data fitting result diagram provided by the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The embodiment of the present invention discloses a method for simulating the abundance of microorganisms in salinized soil during seasonal freeze-thaw period, such as Figure 1 As shown, including:

[0036] Collect initial data and build a logistic model;

[0037] The Logistic model was used to simulate the changes in soil microbial abundance in saline-alkali land during seasonal freeze-thaw period.

[0038] According to the simulated change process, the salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period is obtained, and the salt value is optimized to obtain the optimal salt value suitable for the survival of soil microorganisms;

[0039] The logistic model parameters were adjusted based on the measured soil microbial abundance and optimal salinity value;

[0040] Soil microbial abundance was simulated based on the adjusted Logistic model.

[0041] Furthermore, the collecting of initial data includes: collecting soil salt concentration and soil microbial abundance at different freeze-thaw periods during the seasonal freeze-thaw period.

[0042] Furthermore, it also includes cleaning and preprocessing the initial data, including processing missing values, outliers, data standardization and data normalization.

[0043] Furthermore, the Logistic model is:

[0044]

[0045] Where y is the soil microbial abundance (cfu / g), x is the soil salt concentration (g / kg), A1, A2, x0, and p are unknown coefficients.

[0046] Furthermore, the simulation of the change process of soil microbial abundance in saline-alkali land during seasonal freeze-thaw period by Logistic model includes: taking the soil salt concentration with statistical significance (P<0.05) in univariate analysis as the independent variable, taking the microbial abundance (active microorganisms=1, dead microorganisms=0) as the dependent variable, and performing multifactor Logistic regression analysis by forward method.

[0047] Furthermore, the method of obtaining a salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process includes: constructing a nomogram model for predicting soil microbial abundance, and obtaining the corresponding risk probability affecting the soil microbial abundance by calculating the sum of the scores of soil salt concentration.

[0048] Furthermore, the salt value is optimized to obtain the best salt value suitable for the survival of soil microorganisms, including: ACU value calculation of the nomogram model for predicting soil microbial abundance, goodness of fit test and saturation model determination.

[0049] Specifically, the AUC of the prediction model was calculated. The AUC was small, but the goodness-of-fit test was significant, indicating that the information in the data has not been fully explained by the model and the current model is not the best.

[0050] Goodness of fit test: A good goodness of fit only means that the information in the current data can be fully explained, but it does not mean that the model prediction effect is necessarily good. The goodness of fit refers to whether the difference between the current model and the saturated model is statistically significant.

[0051] Saturated model: If the number of parameters in the model p+1 = the number of independent variable combinations, it is called a saturated model. In fact, such a model must include all the main effects and all-order interaction effects of the independent variables. For prediction, the saturated model has the highest sample compliance rate.

[0052] Furthermore, it also includes calculating the absolute value of the residual. When the absolute value of the residual>n, it indicates that the data is an outlier in the multidimensional space.

[0053] Furthermore, the Logistic model parameter adjustment based on the measured soil microbial abundance and the optimal salinity value includes: performing consistency test on the simulated soil microbial abundance and the measured soil microbial abundance to complete the simulation correction; wherein the calibration index includes the determination coefficient R 2 , root mean square error RMSE and Nash efficiency coefficient NSE.

[0054] Specifically,

[0055]

[0056]

[0057] Where: S i is the simulation value, O i is the measured value, is the average of the measured values, and n is the number of samples.

[0058] Furthermore, the model parameters were optimized according to the correction results to improve the simulation accuracy, and the simulation results of different soil salinity were analyzed to select the salt concentration value of the best soil microbial survival environment. The changes in soil microbial abundance were predicted based on the model.

[0059] In a specific embodiment, a seasonal freeze-thaw period salinization soil microbial abundance simulation system, such as Figure 2 As shown, including:

[0060] Acquisition module: used to collect initial data and establish a Logistic model;

[0061] Fitting module: used to simulate the changing process of soil microbial abundance in saline-alkali land during seasonal freeze-thaw period through Logistic model;

[0062] Optimization module: used to obtain the salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process, and optimize the salt value to obtain the optimal salt value suitable for the survival of soil microorganisms;

[0063] Adjustment module: used to adjust the parameters of the Logistic model based on the measured soil microbial abundance and optimal salinity value;

[0064] Output module: used to simulate soil microbial abundance based on the adjusted Logistic model.

[0065] In a specific embodiment, a method for simulating the abundance of salinized soil microorganisms during the seasonal freeze-thaw period includes: collecting initial data, establishing a Logistic model, and simulating the change process of the abundance of saline-alkali soil microorganisms during the seasonal freeze-thaw period; generating recommended values ​​suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the Logistic model and optimizing the recommended values ​​to obtain the best salt value suitable for the survival of soil microorganisms; and performing feedback adjustment based on the best soil salt concentration value and changes in the seasonal freeze-thaw period environment.

[0066] The experiment was designed as follows:

[0067] (1) Design stress experiments and determine appropriate computational models:

[0068] Select the 0-20cm surface soil of the test area, remove visible impurities, measure the soil moisture content, and pass it through a 2mm sieve for later use. The sieved soil is divided into paper cups with a diameter of 7cm and a height of 20cm. Use CaCl2 and NaCl (molar ratio 1:1) to simulate the salinity of chloride-salinated soil, and use Na2SO4 and CaCl2 (to prevent soil alkalinization) to simulate the salinity of sulfate-salinated soil (to ensure that Cl - With SO4 2- The molar ratio was 3), and a total of 2 salt types were set. The experimental treatments included the CK group (no salt treatment). The experimental design is shown in Table 1. Each experimental treatment was repeated three times. All paper cups were buried in the field at a depth of 20 cm, with the top of the paper cup flush with the ground surface. After the entire seasonal freeze-thaw period, soil samples were finally taken to monitor the actual soil salinity and soil microbial abundance.

[0069] Table 1 Experimental design

[0070]

[0071]

[0072] (2) Monitoring indicators: Soil salinity: Weigh 5 g of air-dried soil sample and mix with 50 mL of distilled water at a ratio of 1:10, stir well, and monitor the soil salinity within 24 hours;

[0073] (3) Soil microbial abundance: monitored by dilution plate method;

[0074] (4) Determine the calculation model:

[0075] The measured data are fitted, and the results are as follows Figure 3 As shown, the determination of salt and microbial abundance conforms to the Logistic model:

[0076]

[0077] Where y is the soil microbial abundance (cfu / g), x is the soil salt concentration (g / kg), A1, A2, x0, and p are unknown coefficients.

[0078] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0079] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating the abundance of microorganisms in salinized soil during seasonal freeze-thaw period, characterized in that: Including: Collect initial data and establish a Logistic model; Simulate the change process of soil microbial abundance in saline-alkali land during the seasonal freeze-thaw period through the Logistic model; Obtain the salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process, and optimize the salt value to obtain the optimal salt value suitable for the survival of soil microorganisms; Adjust the parameters of the Logistic model based on the measured soil microbial abundance and the optimal salt value; Simulate the soil microbial abundance according to the adjusted Logistic model.

2. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 1, characterized in that: The said collection of initial data includes: collecting the concentration of soil salt and the soil microbial abundance at different freeze-thaw periods during the seasonal freeze-thaw period.

3. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 2, characterized in that: It also includes cleaning and preprocessing the initial data, including handling missing values, outliers, data standardization, and data normalization.

4. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 1, characterized in that: The said Logistic model is: Where y is the soil microbial abundance, x is the soil salt concentration, and A1, A2, x0, p are undetermined coefficients.

5. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 1, characterized in that: The said simulation of the change process of soil microbial abundance in saline-alkali land during the seasonal freeze-thaw period through the Logistic model includes: taking the soil salt concentration with P < m in the single-factor analysis as the independent variable and the microbial abundance as the dependent variable, and using the forward method for multi-factor Logistic regression analysis.

6. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 1, characterized in that: The said obtaining the salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process includes: constructing a nomogram model for predicting soil microbial abundance, and obtaining the corresponding risk probability affecting soil microbial abundance by calculating the sum of the scores of soil salt concentration.

7. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 6, characterized in that: The said optimizing the salt value to obtain the optimal salt value suitable for the survival of soil microorganisms includes: calculating the ACU value, goodness-of-fit test, and determination of the saturated model of the nomogram model for predicting soil microbial abundance.

8. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 6, characterized in that: It also includes calculating the absolute value of the residual. When the absolute value of the residual > n, it indicates that the data is an outlier in the multi-dimensional space.

9. The method for simulating soil microbial abundance in seasonal freeze-thaw period according to claim 1, characterized in that: The parameter adjustment of the Logistic model based on the measured soil microbial abundance and the optimal salinity value includes: performing consistency test on the simulated soil microbial abundance and the measured soil microbial abundance to complete the simulation correction; the calibration indicators include the determination coefficient R 2 , root mean square error RMSE and Nash efficiency coefficient NSE.

10. A seasonal freeze-thaw period salinization soil microbial abundance simulation system, characterized in that: Including: Collection module: used to collect initial data and establish a Logistic model; Fitting module: used to simulate the change process of soil microbial abundance in saline-alkali land during the seasonal freeze-thaw period through the Logistic model; Optimization module: used to obtain the salt value suitable for the survival of soil microorganisms during the seasonal freeze-thaw period according to the simulated change process, and optimize the salt value to obtain the optimal salt value suitable for the survival of soil microorganisms; Adjustment module: used to adjust the parameters of the Logistic model based on the measured soil microbial abundance and the optimal salt value; Output module: used to simulate the soil microbial abundance according to the adjusted Logistic model.

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