Method for efficiently activating soil potassium under action of potassium bacteria

Abstract

The invention provides a method for efficiently activating soil potassium under the action of potassium bacteria, and relates to the technical field of soil activation, the method comprises the following steps: carrying out soil potassium-dissolving influence factor mining on potassium-dissolving bacteria to obtain influence factors and an influence function relationship thereof; performing relation carding according to the influence factors and the influence function relation thereof, and constructing an influence matrix; target soil information is obtained, analysis is performed according to the target soil information and the influence matrix, and constraint conditions are obtained; according to the constraint conditions and the influence function relation, compensation analysis is carried out with the activation target maximization, and a potassium bacteria application scheme is obtained. The technical problem that in the prior art, due to the lack of deep analysis on the action mechanism of the potassium bacteria, the potassium bacteria application scheme cannot be adaptively adjusted according to different soil conditions, and consequently the accuracy and effectiveness of the potassium bacteria application scheme are insufficient is solved, the accuracy of potassium bacteria application under different soil conditions is improved, and the application cost is reduced. Therefore, the soil potassium activation efficiency and the crop potassium utilization rate are improved.

Description

Technical Field

[0001] The present application relates to the technical field of soil activation, and in particular to a method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria. Background Art

[0002] Potassium-solubilizing bacteria are a type of microorganism that can decompose insoluble potassium sources in the soil and convert them into a form that can be absorbed by plants. They are widely used in soil potassium activation to improve the utilization efficiency of soil potassium. In recent years, with the advancement of biofertilizer technology, the application of potassium-solubilizing bacteria in agriculture has gradually attracted attention and has become one of the important means of soil improvement and fertilizer substitution.

[0003] The current potassium-solubilizing bacteria application method is usually based on experience or simple application amount setting, and potassium-solubilizing bacteria are applied to the soil, expecting it to decompose the insoluble potassium in the soil. The soil conditions of different soils are different, and the impact on the potassium-solubilizing process of potassium-solubilizing bacteria is also different. However, the prior art lacks in-depth exploration of the potassium-solubilizing mechanism of potassium-solubilizing bacteria in the soil, and the potassium-solubilizing effect of potassium-solubilizing bacteria cannot be accurately evaluated. When facing different soil conditions, the application scheme of potassium-solubilizing bacteria cannot be adjusted in a targeted manner, which causes potassium-solubilizing bacteria to be unable to fully exert their potassium-solubilizing potential, affecting the effect of soil potassium activation. Summary of the invention

[0004] The present application provides a method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria, which solves the technical problem in the prior art that the potassium-solubilizing bacteria application scheme cannot be adaptively adjusted according to different soil conditions due to the lack of in-depth analysis of the action mechanism of potassium-solubilizing bacteria, resulting in insufficient accuracy and effectiveness of the potassium-solubilizing bacteria application scheme, and affecting the soil potassium activation efficiency. The application achieves the technical effect of improving the accuracy of potassium-solubilizing bacteria application under different soil conditions, thereby improving the soil potassium activation efficiency and crop potassium utilization rate.

[0005] In view of the above problems, the present application provides a method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria, the method comprising: mining soil potassium-solubilizing influencing factors of potassium-solubilizing bacteria to obtain influencing factors and their influence function relationships; combing relationships according to the influencing factors and their influence function relationships to construct an influence matrix; acquiring target soil information, the target soil information including soil insoluble potassium components, soil moisture, soil acidity and alkalinity, and soil organic matter; analyzing according to the soil insoluble potassium components, soil moisture, soil acidity and alkalinity, and soil organic matter according to the influence matrix to obtain constraint conditions; performing compensation analysis according to the constraint conditions and the influence function relationship with the activation target as the maximum, and obtaining a potassium-solubilizing bacteria application plan, the potassium-solubilizing bacteria application plan being used for the implementation design of soil potassium activation.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By identifying and understanding the key factors that affect the activity of potassium-solubilizing bacteria and soil potassium transformation, the influencing factors and their influence function relationships are determined, providing a scientific basis for subsequent analysis and the formulation of application plans. After identifying the influencing factors, the influencing factors and their influence function relationships are integrated to construct an influence matrix to determine the interactions and comprehensive effects between the influencing factors, providing a framework for the systematic analysis of soil potassium activation. Different soil characteristics will have different effects on the potassium-solubilizing effect of potassium-solubilizing bacteria. By collecting specific soil information, the key basis for the formulation of personalized potassium-solubilizing bacteria application plans is provided. According to soil information and the influence matrix, the constraints in the process of soil potassium activation are analyzed to determine the boundaries and limitations of the application plan. Under the guidance of the constraints and the influence function relationship, a compensation analysis is carried out to optimize the application plan of potassium-solubilizing bacteria with the goal of maximizing the activation target, ensuring that the potassium-solubilizing bacteria can play the best potassium-solubilizing role in a specific soil environment and achieve efficient activation of soil potassium.

[0008] In summary, this application constructs an influence matrix by mining and sorting out the influencing factors of potassium-solubilizing bacteria, and finally formulates a personalized potassium-solubilizing bacteria application plan by combining the target soil information to analyze the constraints. This method comprehensively considers the various factors and their interrelationships in the process of potassium-solubilizing bacteria, and accurately adapts to the characteristics of different soils, which can significantly improve the potassium-solubilizing efficiency of potassium-solubilizing bacteria in the soil, thereby effectively activating potassium in the soil, providing more absorbable potassium elements for crops, and improving the potassium utilization rate of crops. It also helps to optimize the soil ecological environment and realize the sustainable management of soil potassium.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of the process of the efficient activation method of soil potassium under the action of potassium-dissolving bacteria provided in the embodiment of the present application.

[0011] Figure 2 A schematic flow chart of obtaining influencing factors and their influencing function relationships in the method for efficient activation of soil potassium under the action of potassium-dissolving bacteria provided in an embodiment of the present application.

[0012] Figure 3 This is a schematic flow chart of a scheme for applying potassium-solubilizing bacteria in a method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiment of the present application provides a method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria, mines soil potassium-solubilizing influencing factors of potassium-solubilizing bacteria, obtains influencing factors and their influence function relationships, and constructs an influence matrix; according to soil conditions, analyzes according to the influence matrix to determine constraints; according to the constraints and the influence function relationship, performs compensation analysis to maximize the activation target, and obtains a potassium-solubilizing bacteria application plan. The embodiment of the present application solves the technical problem that the potassium-solubilizing bacteria application plan cannot be adaptively adjusted according to different soil conditions due to the lack of in-depth analysis of the action mechanism of potassium-solubilizing bacteria in the prior art, resulting in insufficient accuracy and effectiveness of the potassium-solubilizing bacteria application plan, and affecting the efficiency of soil potassium activation, and achieves the technical effect of improving the accuracy of potassium-solubilizing bacteria application under different soil conditions, thereby improving the efficiency of soil potassium activation and the utilization rate of crop potassium.

[0014] like Figure 1 As shown, the present application embodiment provides a method for efficiently activating soil potassium under the action of potassium-degrading bacteria, the method comprising:

[0015] Step S1: mining the influencing factors of soil potassium dissolving bacteria to obtain the influencing factors and their influencing function relationships.

[0016] Specifically, soil potassium dissolution influencing factors refer to various factors that affect the potassium dissolution ability of potassium dissolution bacteria in the soil. These factors can be biological factors, such as the interaction between other microbial species in the soil and potassium dissolution bacteria; they can also be abiotic factors, such as soil temperature and humidity. The influence function relationship is a mathematical relationship that describes the influence factor and the potassium dissolution ability of potassium dissolution bacteria.

[0017] By consulting a large amount of literature, collecting research results on the mechanism of action of potassium-solubilizing bacteria, and determining some common influencing factors. Then, through experimental research, under the control of other variables, the influence of each influencing factor on the potassium-solubilizing ability of potassium-solubilizing bacteria is studied separately. For example, a soil environment with different temperature gradients is set up, and the amount of potassium solubilized by potassium-solubilizing bacteria at different temperatures is observed under the same other conditions. By mining the influencing factors of soil potassium solubilization of potassium-solubilizing bacteria, we can fully understand the various factors that affect the potassium-solubilizing ability of potassium-solubilizing bacteria and their quantitative relationship with the potassium-solubilizing ability, obtain the influencing factors and their influence function relationship, and provide basic data for the subsequent formulation of precise potassium-solubilizing bacteria application plans.

[0018] Step S2: Sorting out the relationships according to the influencing factors and their influencing function relationships to construct an influence matrix.

[0019] Specifically, the influence matrix is ​​a tool that represents the relationship between influencing factors in the form of a matrix. The rows and columns of the matrix represent the influencing factors, and the elements in the matrix represent the degree of relationship between the influencing factors of the corresponding rows and columns. Based on the influencing factors obtained in step S1 and their influence function relationship, the relationship between the influencing factors is sorted out by data analysis and logical reasoning. Statistical methods such as correlation analysis can be used to determine whether there is an association between two influencing factors. For example, the correlation coefficient between the soil pH value and the potassium-solubilizing amount of potassium-solubilizing bacteria is calculated. If the coefficient is close to 1 or -1, it means that there is a strong linear relationship between the two. The relationship combing process can be completed with the help of statistical analysis software, and the experimental data is input into the software to perform operations such as correlation analysis and regression analysis to determine the relationship between the influencing factors and construct an influence matrix.

[0020] By constructing an influence matrix, we can integrate the scattered influencing factors and their influence function relationships, grasp the complex relationship network in the potassium-solubilizing process of potassium-solubilizing bacteria as a whole, and provide a systematic analysis framework for the subsequent formulation of plans based on different soil conditions.

[0021] Step S3: Obtain target soil information, wherein the target soil information includes soil insoluble potassium content, soil moisture, soil acidity and alkalinity, and soil organic matter.

[0022] Specifically, the target soil information refers to the relevant characteristic information of the soil where potassium-dissolving bacteria are required to be applied. It includes soil insoluble potassium components, soil moisture, soil acidity and alkalinity, soil organic matter, etc. Among them, the soil insoluble potassium components include information such as the type and content of insoluble potassium components in the soil. Soil moisture indicates the water content in the soil, which will affect the aeration of the soil, the activity of microorganisms, etc. Soil acidity and alkalinity (pH value) is an indicator to measure the acidity or alkalinity of the soil. Different potassium-dissolving bacteria may have different activities in soils with different pH values. Soil organic matter includes animal and plant residues, microorganisms and their decomposed and synthesized substances, which will affect the fertility and structure of the soil and the living environment of microorganisms.

[0023] Obtain target soil samples through soil sampling. In the sampling process, to ensure the scientificity and representativeness of the sampling, a multi-point mixed sampling method can be used. For example, for a large area of ​​farmland, multiple soil samples are collected at different locations and then mixed evenly as a comprehensive sample. Then the target soil samples are experimentally analyzed to determine specific soil information such as soil insoluble potassium components, soil moisture, soil acidity and alkalinity, and soil organic matter. For the insoluble potassium components of the soil, chemical analysis methods such as acid dissolution-flame photometry can be used to determine; soil moisture can be directly measured using a soil moisture sensor; soil pH can be measured using a pH meter; soil organic matter content can be determined by potassium dichromate oxidation-external heating method. These target soil information provides key data for the subsequent formulation of personalized potassium-solubilizing bacteria application plans.

[0024] Step S4: According to the soil insoluble potassium content, soil moisture, soil acidity and alkalinity, and soil organic matter, the influence matrix is ​​analyzed to obtain constraint conditions.

[0025] Specifically, the constraint condition refers to various conditions under which the potassium-solubilizing ability of potassium-solubilizing bacteria is limited under a specific soil environment. The target soil information (soil insoluble potassium component, soil moisture, soil acidity and alkalinity, soil organic matter) obtained in step S3 is substituted into the influence matrix constructed in step S2. According to the relationship in the influence matrix, the various influencing factors of how these soil characteristics affect the potassium-solubilizing of potassium-solubilizing bacteria are analyzed, so as to derive the constraint condition of potassium-solubilizing bacteria under this soil environment. For example, if the influence matrix shows that soil acidity and alkalinity have a great influence on the activity of potassium-solubilizing bacteria metabolic enzymes, and the pH value of the target soil is in a certain specific range, then this pH value range and its influence on the activity of potassium-solubilizing bacteria metabolic enzymes are a constraint condition. The constraint condition clarifies the constraint condition of potassium-solubilizing bacteria under the target soil environment, and provides boundaries and criteria for formulating a reasonable potassium-solubilizing bacteria application plan.

[0026] Step S5: According to the constraint conditions and the influence function relationship, compensation analysis is performed with the activation target maximized to obtain a potassium-solubilizing bacteria application plan, wherein the potassium-solubilizing bacteria application plan is used for the implementation design of soil potassium activation.

[0027] Specifically, compensation analysis refers to the process of adjusting and optimizing the potassium-solubilizing bacteria application plan based on the constraints and influence function relationships, with the goal of maximizing the soil potassium activation target under the existing constraints. The potassium-solubilizing bacteria application plan is a detailed plan on how to apply potassium-solubilizing bacteria in a specific soil, including the type selection of potassium-solubilizing bacteria, inoculation amount, application time, application method, etc., which is used for the implementation design of soil potassium activation.

[0028] First, according to the relationship between constraints and influence functions, determine the parameters related to the application of potassium-solubilizing bacteria that need to be adjusted. For example, if the soil acidity and alkalinity constrain the activity of potassium-solubilizing bacteria, it may be necessary to select a type of potassium-solubilizing bacteria that is more suitable for the pH range. Then, taking the maximization of the soil potassium activation target as the optimization goal, the parameters such as the inoculation amount and application time of potassium-solubilizing bacteria are adjusted and optimized through mathematical models or empirical judgment. For example, a linear programming model based on the objective function (maximum soil potassium activation) and constraints (such as soil moisture, pH value, etc.) can be established to solve the optimal potassium-solubilizing bacteria inoculation amount and application time. This scheme can ensure that potassium-solubilizing bacteria can play the best potassium-solubilizing role in a specific soil environment and achieve efficient activation of soil potassium.

[0029] Further, the potassium-solubilizing bacteria application scheme obtained in step S5 includes:

[0030] Step S5-1: Analyze the full-cycle potassium demand of target soil crops based on the influence relationship of soil potassium.

[0031] Step S5-2: constructing a time-series potassium activation target according to the full-cycle potassium demand.

[0032] Step S5-3: According to the time sequence potassium activation target, a cycle activation target maximization scheme search is performed to obtain each cycle application scheme, and each cycle application scheme is sequentially connected according to the time sequence relationship to obtain the potassium-solubilizing bacteria application scheme.

[0033] Specifically, the full-cycle potassium demand of target soil crops refers to the demand for potassium during the entire growth cycle of crops from sowing to harvesting under the target soil environment.

[0034] By consulting relevant data on crop growth, we can determine the relationship between the crop's physiological characteristics and potassium requirements at different growth stages. For example, studies have shown that corn's demand for potassium increases significantly during the jointing and heading stages. Then, combined with the relationship between soil potassium, such as the form of potassium in the soil, its effectiveness, and the soil's ability to supply potassium, we can comprehensively analyze the crop's full-cycle potassium requirements in the target soil.

[0035] The sequential potassium activation target is to determine the target amount of soil potassium that needs to be activated at different time stages according to the crop's full-cycle potassium demand in chronological order. Based on the crop's full-cycle potassium demand, the entire growth cycle is divided according to the key stages or time intervals of crop growth. For example, it can be divided according to the seedling stage, the vigorous vegetative growth stage, the early reproductive growth stage, and the late reproductive growth stage of the crop. Then, based on the potassium demand characteristics of each stage, the target amount of potassium that needs to be activated from the soil at each stage is determined, thereby constructing a sequential potassium activation target. The sequential potassium activation target can refine the crop's full-cycle potassium demand into different time stages, providing a specific target orientation for the subsequent formulation of a potassium-solubilizing bacteria application plan that conforms to the crop growth rhythm.

[0036] For each cycle, according to the time sequence potassium activation target of the cycle, combined with the characteristics of potassium-solubilizing bacteria, soil constraints and influence function relationships, through experimental research, mathematical model analysis or reference to existing empirical data, the potassium-solubilizing bacteria application scheme that can maximize the potassium activation target of the cycle is searched. For example, in a certain cycle, a multi-factor optimization model can be established, with the maximum potassium activation amount as the goal, and the potassium-solubilizing bacteria inoculation amount, soil temperature, etc. as variables to solve the optimal potassium-solubilizing bacteria application scheme. Then, according to the time sequence relationship of crop growth, the application schemes of each cycle are connected to form a complete potassium-solubilizing bacteria application scheme. In the actual implementation process, mathematical modeling software (such as Matlab) can be used for mathematical modeling and solution of scheme search. You can also refer to previous agricultural test data, potassium-solubilizing bacteria application cases and other materials.

[0037] Through the above steps, a complete potassium-solubilizing bacteria application plan for the target soil crop growth cycle was obtained. This plan not only takes into account the crop's demand for potassium at different growth stages, but also combines soil conditions and the characteristics of potassium-solubilizing bacteria, and can maximize the goal of soil potassium activation, improve crop potassium utilization, and thus promote crop growth and increase yield.

[0038] Further, such as Figure 2 As shown, step S1 includes:

[0039] Step S11: Acquire reaction record data of potassium-solubilizing bacteria, and preliminarily screen variables related to potassium-solubilizing bacteria activity and soil potassium release according to the reaction record data to obtain preliminarily screened influencing variables, wherein the preliminarily screened variable conditions are those that are strongly correlated with potassium-solubilizing bacteria activity and are controllable under actual field conditions.

[0040] Step S12: Based on the primary screening influencing variables, a single factor gradient experiment is performed in the soil sample to change the soil characteristics one by one to obtain an experimental data set, wherein the experimental data set includes the primary screening influencing variables, the potassium ion concentration in the soil solution and the corresponding potassium release amount.

[0041] Step S13: According to the experimental data set, a quantitative relationship between the primary screening influencing variables and the potassium release efficiency is established, and the influencing factors and their influencing function relationships are determined according to the quantitative relationship.

[0042] Specifically, the reaction record data of potassium-solubilizing bacteria refers to the experimental data of the interaction between potassium-solubilizing bacteria and potassium in the soil under different conditions, including the growth of potassium-solubilizing bacteria, the amount of potassium released in the soil, etc. The initial screening of influencing variables is to screen out variables with strong correlation with the activity of potassium-solubilizing bacteria and the release of potassium in the soil through preliminary analysis of the reaction record data. These variables can be controlled under actual field conditions, such as soil temperature, humidity, pH value, etc. First, the reaction record data of potassium-solubilizing bacteria under different soil conditions are collected. These data can come from laboratory experimental results or existing research reports. Then, through data analysis methods, such as correlation analysis, find out the variables with strong correlation with the activity of potassium-solubilizing bacteria and the release of potassium in the soil. In the specific implementation process, statistical analysis software, such as SPSS or Excel, can be used for data processing and correlation analysis.

[0043] A single factor gradient experiment refers to an experiment in which only one influencing variable is changed while other conditions remain unchanged to observe the effect of the variable on the activity of potassium-solubilizing bacteria and the release of potassium in the soil. The experimental data set is a data set obtained through a single factor gradient experiment, including the initial screening influencing variables, the potassium ion concentration in the soil solution, and the corresponding potassium release. According to the initial screening influencing variables, a series of single factor gradient experiments are designed. Exemplarily, typical soil samples are first selected before the experiment, including acidic, neutral and alkaline soils. According to these typical soil samples, a single factor gradient experiment is set up, and the soil characteristics are changed one by one (such as humidity changes from 40% to 80%, pH from 4 to 8), the potassium ion concentration in the soil solution is measured by a flame photometer, and the community composition and activity changes of potassium-solubilizing bacteria are analyzed by high-throughput sequencing technology to obtain experimental data sets. These experimental data sets contain data on the activity of potassium-solubilizing bacteria and the release of potassium in the soil under different conditions, providing an experimental basis for establishing a quantitative relationship.

[0044] Regression analysis was performed on the experimental data set to establish a quantitative relationship between the initial screening influencing variables and the potassium release efficiency. Principal component analysis was performed based on the quantitative relationship between the initial screening influencing variables and the potassium release efficiency, and the influencing variables that had a significant impact on the amount of potassium released from the soil were screened out and determined as influencing factors, and their corresponding quantitative relationships were determined as influence function relationships. Exemplarily, these influencing factors include soil temperature, soil moisture, soil pH, types and contents of insoluble potassium minerals, soil organic matter content, etc. The influencing factors and their influence function relationships provide a theoretical basis for subsequent soil potassium activation methods.

[0045] Further, step S13 includes:

[0046] Step S131: setting a second-order interaction experiment, changing the combination of two or more primary screening influencing variables, and obtaining second-order experimental data to add to the experimental data set.

[0047] Step S132: performing first-order direct influence intensity fitting based on the single factor data in the experimental data set to obtain a first-order influence relationship, and performing second-order synergistic effect analysis based on the second-order experimental data in the experimental data set to obtain a second-order influence relationship.

[0048] Step S133: combining the first-order influence relationship and the second-order influence relationship to establish the quantitative relationship, including the quantitative relationship of all the initial screening influence variables and their interaction terms.

[0049] Specifically, a second-order interaction experiment refers to an experimental design that simultaneously changes the values ​​of two or more primary screening influencing variables to explore the effects of the interactions between these variables on the potassium release efficiency. The second-order experimental data obtained through the second-order interaction experiment reflects the experimental results when multiple influencing factors interact, such as the potassium ion concentration in the soil solution and the corresponding potassium release under different temperature and humidity combinations.

[0050] According to the previously determined influencing variables, various combinations were designed for second-order interaction experiments. For example, different temperature-humidity combinations, temperature-pH combinations, humidity-pH combinations, etc. were set for experiments. Under each combination, relevant data such as the potassium ion concentration in the soil solution and the corresponding potassium release were measured, and these data were added to the previous experimental data set. Through the second-order interaction experiment, experimental data under multi-factor interaction were obtained, enriching the experimental data set, so that subsequent analysis can take into account the interaction between the influencing factors, thereby more comprehensively reflecting the actual situation of potassium-solubilizing bacteria activity and soil potassium release.

[0051] The first-order direct impact intensity fitting is based on the data of a single preliminary screening influencing variable in the experimental data set, and is a process of determining the degree of direct impact of each preliminary screening influencing variable on the potassium release efficiency by mathematical methods (such as fitting curves). The first-order impact relationship is the relationship between a single preliminary screening influencing variable and the potassium release efficiency obtained by first-order direct impact intensity fitting. For each individual preliminary screening influencing variable experimental data in the experimental data set, a mathematical model (such as a linear model, a nonlinear model, etc.) is used for fitting. For example, for the primary screening influencing variable of soil temperature, assuming that there may be a linear relationship between potassium release efficiency (E) and soil temperature (T), the relationship E=aT+b is fitted by the least squares method and other methods, where a and b are the parameters obtained by fitting, and this relationship is the first-order impact relationship.

[0052] The second-order synergistic effect analysis is a process of analyzing the synergistic (promotional) or antagonistic (inhibitory) effects on potassium release efficiency when two or more primary screening influencing variables interact with each other for the second-order experimental data. The second-order influence relationship is the relationship between the interaction between multiple primary screening influencing variables and the potassium release efficiency obtained through the second-order synergistic effect analysis. For the second-order experimental data, the combination of primary screening influencing variables to be analyzed is first determined. Then, by comparing the potassium release efficiency under different combinations with the expected value under the influence of a single factor, it is determined whether there is a synergistic or antagonistic effect. For example, if the increase in potassium release efficiency is ΔE1 when the soil temperature alone increases, the increase in potassium release efficiency is ΔE2 when the soil moisture alone increases, and the increase in potassium release efficiency is ΔE3 when the soil temperature and moisture increase simultaneously, if ΔE3>(ΔE1+ΔE2), it means that there is a synergistic effect; if ΔE3<(ΔE1+ΔE2), it means that there is an antagonistic effect. By determining the second-order influence relationship. Through the first-order direct impact intensity fitting and the second-order synergistic effect analysis, the direct impact of a single primary screening influencing variable on potassium release efficiency and the impact of the interaction between multiple primary screening influencing variables can be clearly distinguished, laying the foundation for establishing a more accurate quantitative relationship.

[0053] Comprehensively consider the individual effects of all the initial screening influencing variables (first-order influence relationship) and their interactions (second-order influence relationship), integrate the first-order influence relationship and the second-order influence relationship into a mathematical expression, and establish the mathematical relationship between the initial screening influencing variables and their interaction terms and the potassium release efficiency, that is, the quantitative relationship. In actual operation, statistical analysis software such as SPSS or R language can be used for multiple regression analysis and coefficient determination.

[0054] The quantitative relationship established in the above steps comprehensively considers the impact of all initially screened influencing variables and their interactions on potassium release efficiency, and can more accurately describe the intrinsic laws of potassium-solubilizing bacteria activity and soil potassium release, providing a more reliable theoretical basis for the subsequent formulation of potassium-solubilizing bacteria application plans based on soil conditions.

[0055] Further, step S2 includes:

[0056] Step S21: constructing an influence matrix based on the influence factors, wherein rows represent different influence factors and columns also represent the same influence factors as the rows.

[0057] Step S22: fill the diagonal elements of the influence matrix according to the first-order influence relationship, and fill the non-diagonal elements of the influence matrix according to the second-order influence relationship, wherein the matrix elements reflect the degree and direction of influence of the influencing factors on the activity of potassium-solubilizing bacteria, with numbers representing the degree of influence and symbols representing the direction of influence.

[0058] Specifically, an influence matrix is ​​constructed based on the influencing factors determined above, and this matrix is ​​used to describe the relationship between the influencing factors. In the influence matrix, each row represents a different influencing factor that affects the activity of potassium-solubilizing bacteria, and each column also represents a different influencing factor that affects the activity of potassium-solubilizing bacteria. The number of rows and columns is equal to the number of influencing factors. The influence matrix provides a structural framework for the subsequent description of the relationship between the influencing factors, so that the degree and direction of influence between the influencing factors can be uniformly represented in the form of matrix elements.

[0059] The influence matrix is ​​filled with elements according to the first-order influence relationship and the second-order influence relationship determined above. The elements in the matrix reflect the degree and direction of influence of the influencing factors on the activity of potassium-solubilizing bacteria. The size of the number indicates the strength of the influence of one influencing factor on another influencing factor (the diagonal element is itself), and the symbol indicates the direction of this influence. Wherein the positive sign indicates promotion, and the negative sign indicates inhibition. According to the first-order influence relationship, the degree and direction of influence of each influencing factor on the activity of potassium-solubilizing bacteria are filled into the diagonal elements of the influence matrix. For example, if the first-order influence relationship of soil temperature T on the activity of potassium-solubilizing bacteria is a positive influence and the degree of influence is quantified as 0.3, then the elements corresponding to the row and column (i.e., the diagonal position) of soil temperature T in the influence matrix are filled with +0.3. According to the second-order influence relationship, the degree and direction of mutual influence between different influencing factors are filled into the non-diagonal elements of the influence matrix. In the matrix elements, for example, if the second-order influence relationship between soil temperature T and soil moisture H is that soil temperature has a negative influence on soil moisture and the degree of influence is quantified as -0.2, then the elements in the soil temperature T row and soil moisture H column in the influence matrix are filled with -0.2.

[0060] The filled influence matrix comprehensively represents the relationship between the influencing factors, including the influence of a single influencing factor itself and the mutual influence between different influencing factors. This matrix representation is helpful to further analyze the influencing mechanism of potassium-solubilizing bacteria activity.

[0061] Further, step S4 includes:

[0062] Step S41: extracting the influencing factor characteristics according to the soil insoluble potassium content, soil moisture, soil acidity and alkalinity, and soil organic matter, and constructing a soil information vector.

[0063] Step S42: taking the soil information vector as a column vector and performing a dot product operation with the influence matrix to obtain a matrix operation result.

[0064] Step S43: Identify the constraint conditions and optimization direction according to the matrix operation result.

[0065] Specifically, according to the aforementioned determined influencing factors, the corresponding influencing factor characteristics are extracted from the target soil information. For example, the aforementioned determined influencing factors include soil temperature, soil moisture, soil pH value, insoluble potassium mineral types and content, and soil organic matter content. The specific temperature value, humidity value, pH value, insoluble potassium mineral types and content, and soil organic matter content are extracted from the insoluble potassium components, soil moisture, soil acidity and alkalinity, and soil organic matter contained in the target soil information. These characteristic values ​​are combined in the order of columns in the influence matrix to construct a soil information vector.

[0066] The soil information vector is used as a column vector to perform a dot product operation with the influence matrix to obtain the matrix operation result. The dot product operation integrates the relationship between soil information and influencing factors. The obtained matrix operation result can reflect the comprehensive influence of various influencing factors on the activity of potassium-solubilizing bacteria after interaction under the current soil characteristics, providing a basis for the subsequent identification of constraint conditions and optimization directions.

[0067] The optimization direction refers to the soil characteristics or application schemes that should be adjusted when applying potassium-solubilizing bacteria to maximize the efficiency of potassium release. Analyze each element in the matrix operation result to identify which soil characteristics (such as moisture, pH value, organic matter, etc.) are within a certain range and may become limiting factors when applying potassium-solubilizing bacteria. If the value of an element is negative and the absolute value is large, it means that the corresponding influencing factor has a greater inhibitory effect on the activity of potassium-solubilizing bacteria. This influencing factor and its related soil characteristics are constraints. If the value of an element is positive and there is a large room for improvement (for example, compared with the ideal value), then improving the activity of potassium-solubilizing bacteria by changing the soil characteristics related to the influencing factor corresponding to the element is the optimization direction. Exemplarily, the analysis can be performed based on previous research experience or experimental data pre-set thresholds (such as thresholds for judging positive and negative effects, ranges of ideal values, etc.). And use data visualization tools (such as Matplotlib) to display the matrix operation results in a graphical way to more intuitively identify constraints and optimization directions. By analyzing the results of matrix operations, the constraints and optimization directions can be accurately identified, thus providing clear guidance for improving soil conditions to enhance the activity of potassium-solubilizing bacteria and the efficiency of soil potassium release.

[0068] Further, such as Figure 3 As shown, step S5 includes:

[0069] Step S51: obtaining a suboptimal factor according to the matrix operation result, wherein the suboptimal factor is an influencing factor with a positive influence and an influence amount ranking that satisfies a preset ranking.

[0070] Step S52: performing factor gain optimization according to the suboptimal factor to obtain a gain strategy.

[0071] Step S53: performing adjustment and optimization according to the constraint conditions and optimization direction to obtain a compensation strategy.

[0072] Step S54: resetting the soil information vector based on the gain strategy and the compensation strategy, re-performing matrix operations, predicting the activity of the optimized potassium-solubilizing bacteria, iteratively adjusting the strategies and predicting until the activation target is met, and obtaining the potassium-solubilizing bacteria application plan.

[0073] Specifically, first analyze the elements in the matrix operation results to find out the positive elements. The corresponding influencing factors of these elements have a positive effect on the activity of potassium-solubilizing bacteria. Then sort the corresponding influencing factors of these positive elements according to their sizes, and determine the suboptimal factors according to the preset ranking. For example, if the preset ranking is the top 3, then the influencing factors corresponding to the top 3 positive influences in the sorting are the suboptimal factors.

[0074] The gain strategy refers to a strategy for maximizing the activity of potassium-solubilizing bacteria by adjusting the level of suboptimal factors. After determining the suboptimal factors, the gain strategy is formulated according to the characteristics of the factors and the actual soil environment, with reference to soil science-related research results, agricultural production practice experience, and soil testing data. For example, if the suboptimal factor is soil organic matter, considering the source and decomposition process of soil organic matter, strategies such as increasing the application of organic fertilizers and improving soil aeration to promote the decomposition of organic matter can be formulated. For the case where soil moisture is a suboptimal factor, the texture and drainage of the soil can be analyzed. If the soil has good drainage, the amount of irrigation can be appropriately increased; if the soil has poor water retention, water-retaining materials can be added, etc., to form a specific gain strategy. By formulating a gain strategy, suboptimal factors can be optimized, and the positive effect of suboptimal factors on the activity of potassium-solubilizing bacteria can be increased to improve the activity of potassium-solubilizing bacteria and the efficiency of soil potassium release.

[0075] The compensation strategy is a specific strategy for adjusting soil conditions in order to overcome constraints and move toward optimization. Compensation strategies are formulated based on the identified constraints and optimization directions. If the constraint is the low content of insoluble potassium in the soil, and the optimization direction is to increase the effective potassium content in the soil, strategies such as using potassium ore powder to improve the soil or inoculating more potassium-solubilizing bacteria can be considered. When soil moisture is a constraint (such as too low humidity) and the optimization direction is to increase humidity, compensation strategies can be formulated based on the water holding capacity of the soil and local climatic conditions, such as using drip irrigation technology, covering the ground with film to reduce water evaporation, etc. By formulating compensation strategies, constraints can be effectively addressed, and soil conditions can be pushed toward a direction that is conducive to increasing the activity of potassium-solubilizing bacteria and releasing soil potassium.

[0076] The soil information vector is reset according to the gain strategy and the compensation strategy. For example, if the gain strategy is to increase soil organic matter and the compensation strategy is to adjust the soil pH value, then the elements corresponding to the soil organic matter content and pH value are adjusted accordingly in the soil information vector. Then, the reset soil information vector is used to re-perform matrix operations to predict the activity of the optimized potassium-solubilizing bacteria. The strategy adjustment and prediction process are iteratively performed according to the prediction results. The potassium-solubilizing bacteria activity is recalculated after each adjustment of the gain strategy and the compensation strategy until the activation target is met (such as the potassium-solubilizing bacteria activity reaches a predetermined value or the soil potassium release efficiency reaches the expected level). Finally, the application scheme of potassium-solubilizing bacteria is determined according to the strategy adjustment during the iteration process, including the type, application amount, application time, application frequency, etc. of potassium-solubilizing bacteria. For example, if it is found through iteration that the potassium-solubilizing bacteria activity is significantly improved after increasing soil organic matter and adjusting the soil pH value to a certain range, then the application amount and application time of potassium-solubilizing bacteria can be determined according to the increase in organic matter in the soil and the adjustment of pH value.

[0077] Through iterative strategy adjustment and prediction, a potassium-solubilizing bacteria application plan that meets the activation target can be obtained. This plan can comprehensively consider the optimization of various influencing factors according to the actual soil conditions to achieve the purpose of improving the activity of potassium-solubilizing bacteria and the efficiency of soil potassium release.

[0078] Further, step S54 includes:

[0079] Step S541: Evaluate the gain cost and potential benefit of the gain strategy to establish a gain evaluation relationship.

[0080] Step S542: performing compensation imbalance loss evaluation and potential benefit evaluation on the compensation strategy to obtain a compensation evaluation relationship.

[0081] Step S543: Based on the gain evaluation relationship and the compensation evaluation relationship, a maximum search is performed on the combined evaluation results of the gain strategy and the compensation strategy to obtain the best combination strategy, and the soil information vector is reset using the best combination strategy.

[0082] Specifically, in the iterative process, it is necessary to evaluate the gain strategy and compensation strategy after each adjustment to determine the specific adjustment operation for the next step. For the gain strategy, first determine the gain cost, that is, the various resources required to implement the gain strategy, including manpower, material resources, etc. Then, based on previous experimental data or empirical models, estimate the degree of improvement in the activity of potassium-solubilizing bacteria or the improvement in the efficiency of potassium release in the soil after the implementation of the gain strategy, that is, the potential benefit. In the actual operation process, cost calculation can use accounting methods to count various expenses. Potential benefit estimates can refer to experimental research results in the field of soil science, existing mathematical models, etc. For example, when the gain strategy is to improve soil aeration to increase the decomposition of soil organic matter, it is necessary to consider the equipment rental costs for tilling the soil (if any), the labor costs of manual tillage, etc. Quantify these costs, convert equipment rental costs and labor costs into monetary units, and obtain the total gain cost C g By consulting relevant research literature, we know that increasing a certain amount of soil organic matter can increase the activity of potassium-dissolving bacteria by x%. Based on the total amount of potassium in the soil, we calculate the increase in potassium release that may be brought about by this part of activity increase, ΔK, which is the potential benefit B g .

[0083] Based on the gain cost and potential benefit evaluation, a gain evaluation relationship is constructed using data analysis and modeling software to describe the relationship between the gain cost and potential benefit of the gain strategy. The gain evaluation relationship can be a simple cost-benefit ratio or a functional relationship including multiple influencing factors. For example, based on the gain cost C g and potential benefits B g Construct a simple gain evaluation relationship, such as the cost-benefit ratio R g =B g / C g , or construct a more complex functional relationship according to the actual situation, such as R g =f(C g , B g , other factors), where other factors may include soil type, climatic conditions and other influencing factors.

[0084] For the compensation strategy, the first step is to evaluate the compensation imbalance loss, that is, to evaluate the negative impact of other soil characteristics caused by adjusting a certain soil characteristic during the implementation of the compensation strategy. The compensation imbalance loss evaluation can use soil chemical analysis methods to detect changes in soil element effectiveness through experimental analysis, and at the same time refer to relevant knowledge such as soil ecology to quantify the loss. For example, through soil testing, it was found that the effectiveness of iron in the soil decreased after lime application. According to factors such as the importance of iron in the soil to plant growth and the absorption of iron by plants, the losses in crop yield or soil ecological function caused by the decrease in iron effectiveness were calculated. Then, the potential benefit evaluation of the compensation strategy is carried out, which is similar to the potential benefit evaluation in the gain strategy. It is to evaluate the benefits of the expected improvement in potassium-solubilizing bacteria activity or the improvement in soil potassium release efficiency after the implementation of the compensation strategy. A compensation evaluation relationship is constructed based on the compensation imbalance loss evaluation and potential benefit evaluation, which is used to describe the relationship between the compensation imbalance loss and potential benefit of the compensation strategy.

[0085] List all possible combinations of gain strategies and compensation strategies. Assuming there are m gain strategies and n compensation strategies, there will be a total of m×n combinations. For each combination, according to the established gain evaluation relationship and compensation evaluation relationship, the overall evaluation result of each combination of gain strategy and compensation strategy is obtained. For example, the gain evaluation relationship and the compensation evaluation relationship can be weighted and summed to obtain a value that reflects the comprehensive benefits of the combination strategy. Suppose the gain evaluation relationship R g The weight is ω g , compensation evaluation relationship R c The weight is ω c , then the combined evaluation result R = ω g R g +ω c R c Among them, ω g ,ω c It can be determined according to the agricultural production goals (whether to focus more on cost control or yield improvement, etc.). Find the combination corresponding to the maximum value in all combination evaluation results, and this combination is the best combination strategy. Adjust the corresponding elements in the soil information vector according to the best combination strategy. For example, if the best combination strategy includes increasing soil organic matter and adjusting soil pH, then in the soil information vector, the elements corresponding to soil organic matter content and pH value are adjusted according to the requirements of the best combination strategy.

[0086] By searching for the best combination strategy and resetting the soil information vector, it is possible to optimize soil conditions based on multiple factors and accurately predict the activity of optimized potassium-solubilizing bacteria, providing more reasonable basic data for ultimately obtaining the potassium-solubilizing bacteria application plan.

[0087] In summary, the method for efficiently activating soil potassium under the action of potassium-dissolving bacteria provided in the embodiments of the present application has the following technical effects:

[0088] The present application embodiment accurately formulates the application scheme of potassium-solubilizing bacteria by comprehensively analyzing the multiple factors of the soil and the mechanism of action of potassium-solubilizing bacteria. First, based on the in-depth excavation and quantification of the soil influencing factors under the action of potassium-solubilizing bacteria, an influence matrix is ​​constructed to reveal the relationship between soil characteristics and potassium-solubilizing effects. Then, in combination with the specific information of the target soil, the application scheme of potassium-solubilizing bacteria is optimized through constraint analysis and compensation analysis to ensure the efficient application of potassium-solubilizing bacteria under different soil conditions. Overall, the method comprehensively considers the various factors and their mutual relationships in the potassium-solubilizing process of potassium-solubilizing bacteria, and accurately adapts to the characteristics of different soils, which can significantly improve the potassium-solubilizing efficiency of potassium-solubilizing bacteria in the soil, thereby effectively activating potassium in the soil, providing more absorbable potassium elements for crops, and improving the potassium utilization rate of crops. It also helps to optimize the soil ecological environment and realize the sustainable management of soil potassium.

[0089] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those 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 application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria, characterized in that: include: Mining the influencing factors of soil potassium dissolving by potassium dissolving bacteria, and obtaining the relationship between the influencing factors and their influencing functions; Sorting out the relationships according to the influencing factors and their influencing function relationships, and constructing an influencing matrix; Acquiring target soil information, wherein the target soil information includes soil insoluble potassium content, soil moisture, soil acidity and alkalinity, and soil organic matter; According to the soil insoluble potassium content, soil moisture, soil acidity and alkalinity, and soil organic matter, the influence matrix is ​​analyzed to obtain constraint conditions; According to the constraint conditions and the influence function relationship, a compensation analysis is performed with the activation target maximized to obtain a potassium-solubilizing bacteria application plan, which is used for the implementation design of soil potassium activation.

2. The method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria as claimed in claim 1, characterized in that: The method for obtaining potassium-dissolving bacteria comprises: Analyze the full-cycle potassium requirements of target soil crops based on the influence relationship of soil potassium; According to the full-cycle potassium demand, a time-series potassium activation target is established; According to the time sequence potassium activation target, a cycle activation target maximization scheme search is performed to obtain each cycle application scheme, and each cycle application scheme is connected in time sequence according to the time sequence relationship to obtain the potassium-solubilizing bacteria application scheme.

3. The method for efficiently activating soil potassium under the action of potassium-solubilizing bacteria as claimed in claim 1, characterized in that: The mining of soil potassium-solubilizing influencing factors of potassium-solubilizing bacteria to obtain influencing factors and their influencing function relationships includes: Acquire reaction record data of potassium-solubilizing bacteria, and preliminarily screen variables related to potassium-solubilizing bacteria activity and soil potassium release according to the reaction record data to obtain preliminarily screened influencing variables, wherein the preliminarily screened variable conditions are those that are strongly correlated with potassium-solubilizing bacteria activity and are controllable under actual field conditions; Based on the primary screening influencing variables, a single factor gradient experiment is performed in the soil sample to change the soil characteristics one by one to obtain an experimental data set, wherein the experimental data set includes the primary screening influencing variables, the potassium ion concentration in the soil solution and the corresponding potassium release amount; According to the experimental data set, a quantitative relationship between the primary screening influencing variables and the potassium release efficiency is established, and the influencing factors and their influencing function relationships are determined according to the quantitative relationship.

4. The method for efficiently activating soil potassium under the action of potassium-dissolving bacteria as claimed in claim 3, characterized in that: Based on the experimental data set, a quantitative relationship between the initial screening influencing variables and the potassium release efficiency was established, including: Setting up a second-order interaction experiment, changing the combination of two or more primary screening influencing variables, and obtaining second-order experimental data to add to the experimental data set; Perform first-order direct impact intensity fitting based on the single factor data in the experimental data set to obtain a first-order impact relationship, and perform second-order synergistic effect analysis based on the second-order experimental data in the experimental data set to obtain a second-order impact relationship; The quantitative relationship is established by combining the first-order influence relationship and the second-order influence relationship, including the quantitative relationship of all the initial screening influencing variables and their interaction terms.

5. The method for efficiently activating soil potassium under the action of potassium-dissolving bacteria as claimed in claim 4, characterized in that: According to the relationship between the impact factors and their impact functions, the impact matrix is ​​constructed, including: Constructing an impact matrix based on the impact factors, wherein rows represent different impact factors and columns also represent the same impact factors as the rows; The diagonal elements of the influence matrix are filled according to the first-order influence relationship, and the non-diagonal elements of the influence matrix are filled according to the second-order influence relationship, wherein the matrix elements reflect the degree and direction of influence of the influencing factors on the activity of potassium-solubilizing bacteria, the numbers represent the degree of influence, and the symbols represent the direction of influence.

6. The method for efficiently activating soil potassium under the action of potassium-dissolving bacteria as claimed in claim 5, characterized in that: According to the soil insoluble potassium content, soil moisture, soil acidity and alkalinity, and soil organic matter, the influence matrix is ​​analyzed to obtain constraint conditions, including: Extract the influencing factor characteristics according to the soil insoluble potassium component, soil moisture, soil acidity and alkalinity, and soil organic matter, and construct a soil information vector; Performing a dot product operation on the soil information vector as a column vector and the influence matrix to obtain a matrix operation result; The constraint conditions and optimization direction are identified according to the matrix operation results.

7. The method for efficiently activating soil potassium under the action of potassium-dissolving bacteria as claimed in claim 6, characterized in that: According to the constraint conditions and the influence function relationship, compensation analysis is performed with the activation target maximized to obtain a potassium-solubilizing bacteria application plan, including: According to the matrix operation result, a suboptimal factor is obtained, where the suboptimal factor is an influencing factor with positive influence and the influence quantity ranking satisfies a preset ranking; Perform factor gain optimization according to the suboptimal factors to obtain a gain strategy; Perform adjustment and optimization according to the constraint conditions and optimization direction to obtain a compensation strategy; Based on the gain strategy and the compensation strategy, the soil information vector is reset, matrix operations are re-performed, the activity of the optimized potassium-solubilizing bacteria is predicted, and strategy adjustments and predictions are iteratively performed until the activation target is met, thereby obtaining the potassium-solubilizing bacteria application plan.

8. The method for efficiently activating soil potassium under the action of potassium-dissolving bacteria as claimed in claim 7, characterized in that: Resetting the soil information vector based on the gain strategy and the compensation strategy includes: Evaluate the gain cost and potential benefit of the gain strategy and construct a gain evaluation relationship; Conducting compensation imbalance loss evaluation and potential benefit evaluation on the compensation strategy to obtain a compensation evaluation relationship; Based on the gain evaluation relationship and the compensation evaluation relationship, a maximum search is performed on the combined evaluation results of the gain strategy and the compensation strategy to obtain the best combination strategy, and the soil information vector is reset using the best combination strategy.

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