Microbial compound fertilizer application method for desert grassland

Abstract

This invention discloses a method for applying microbial compound fertilizer to desert grasslands, belonging to the field of agricultural fertilization technology. This method addresses the problem of unstable application effects of existing microbial fertilizers in harsh environments such as desert grasslands by employing the following approach: First, soil samples are collected for physicochemical analysis. Based on the analysis results, suitable microbial strains are selected and prepared into a compound inoculum. The inoculum is then mixed with bentonite carrier to form a microbial compound fertilizer. The fertilization depth and dosage are determined using a graded adaptation process based on specific soil obstacle indicators (such as total petroleum hydrocarbon content). After fertilization, soil moisture, temperature, and pH are initially regulated. Finally, a dynamic field management strategy based on fertilization effect feedback is established. By regularly monitoring changes in key soil indicators, the appropriate target values ​​for moisture and temperature are dynamically calculated and adjusted. This continuously optimizes the soil microenvironment based on the effectiveness of the microbial fertilizer, achieving precise fertilization and soil improvement.

Description

Technical Field

[0001] This invention relates to the field of agricultural fertilization technology. More specifically, this invention relates to a method for applying microbial compound fertilizer to desert grasslands. Background Technology

[0002] Soil is the foundation of agricultural production and ecological security; its quality directly affects pasture growth, vegetation restoration, and sustainable agricultural development. Utilizing beneficial microorganisms to improve soil and enhance soil fertility is an important direction for modern and ecological agriculture. Microbial fertilizers, through the life activities of microorganisms, can promote nutrient transformation and cycling, improve soil physicochemical properties, and enhance nutrient availability. However, the practical application of microbial fertilizers in harsh environments such as polluted or degraded desert grasslands still faces several limitations. These limitations are constrained by multiple factors, including drought, drastic temperature variations, soil infertility, and the potential presence of pollutants, affecting the stability of their fertilizer efficacy and their potential for widespread application.

[0003] First, the colonization, activity, and fertilization effectiveness of microorganisms in soil are highly dependent on soil environmental conditions, such as temperature, humidity, and pH. Desert grasslands suffer from harsh ecological environments and drastic environmental fluctuations; changes in external conditions significantly impact the growth and functional expression of microbial communities, leading to unstable and inefficient growth-promoting and improvement effects. Current technologies typically involve one-time adjustments to environmental parameters after fertilizer application or rely solely on natural conditions, lacking a mechanism for real-time feedback and adjustment of field management measures based on soil moisture, soil temperature, and fertilizer effectiveness. Transforming dynamic monitoring data of soil environmental parameters into key inputs for precise water and fertilizer management decisions is the core challenge for achieving stable improvements in fertilization effects in desert grasslands.

[0004] Secondly, when fertilizing desert grasslands with different levels of obstruction (such as specific organic matter or heavy metals) or degradation, the soil's background properties, obstruction concentrations, and texture vary significantly. Existing technologies often employ single microbial fertilizer formulations and fixed application methods, lacking a tiered adaptation process tailored to soil characteristics, obstruction levels, and improvement goals. This can lead to excessive microbial fertilizer application in mildly obstructed soils, resulting in resource waste, while in severely obstructed soils, insufficient concentration, uneven distribution, or inadequate contact of the microbial agent can lead to substandard improvement results. Due to the spatial heterogeneity of desert grasslands and the diversity of obstruction types, developing a set of refined fertilization process standards that can flexibly adapt to different soil conditions is a challenge for the widespread application of this technology.

[0005] Third, in the construction and application of compound microbial fertilizers, the pursuit of multifunctional improvement is often achieved by simply mixing multiple functional strains, but the potential antagonistic effects between strains are overlooked. Applying incompatible strains simultaneously to the soil not only fails to create synergistic effects but also leads to overall functional decline due to competitive inhibition, affecting the expected fertilizer effect. Current technology lacks an effective method for rapidly assessing strain compatibility before fertilizer preparation and application, resulting in uncertainty in the effectiveness of compound microbial fertilizers in practical applications. How to efficiently screen strain combinations that can stably coexist and synergistically act in desert grassland soils, and accordingly prepare and apply high-efficiency compound microbial fertilizers, is a key issue in improving the reliability of fertilization effects.

[0006] These problems limit the reliability, environmental adaptability, and economic viability of microbial fertilizers in soil improvement of desert grasslands (including grasslands with pollution or degradation barriers). Therefore, it is necessary to develop a more refined and systematic method for applying microbial compound fertilizers and corresponding field management strategies to achieve rapid improvement and ecological restoration of such grasslands. Summary of the Invention

[0007] To achieve these objectives and other advantages of the present invention, the present invention provides a method for applying microbial compound fertilizer to desert grasslands, comprising the following steps:

[0008] Step 1: Collect soil samples from polluted desert grassland for physicochemical analysis and determine key indicators affecting soil quality and microbial activity, including total petroleum hydrocarbon content, heavy metal content, microbial community diversity index, soil moisture, soil temperature, and soil pH.

[0009] Step 2: Based on the physicochemical analysis results, select suitable functional and nutritional microbial strains. Cultivate the selected strains under aseptic conditions to prepare a compound microbial agent. The concentration range of each microbial strain in the compound microbial agent is 1×10⁻⁶. 6 ~1×10 8 CFU / g;

[0010] Step 3: Mix the compound microbial agent with the carrier bentonite to obtain the microbial compound fertilizer; based on the physicochemical analysis results of Step 1, determine the fertilization parameters using a graded adaptation process, and apply the microbial compound fertilizer to the polluted desert grassland soil;

[0011] Step 4: After completing the application, conduct preliminary regulation of the soil environmental conditions of the desert grassland, maintaining soil moisture in the range of 15-32% field capacity, soil temperature in the range of 15-35℃, and soil pH in the range of 6.5-7.0.

[0012] Preferably, in step two, suitable functional microbial strains and nutritional microbial strains are selected based on the physicochemical analysis results. The specific selection method is as follows:

[0013] For the total petroleum hydrocarbon content index in soil, at least one functional microbial strain is selected from Pseudomonas, Nocardia, Sphingomonas and Bacillus.

[0014] For heavy metal content indicators, at least one functional microbial strain should be selected from Bacillus, Aspergillus, Yeast and Penicillium.

[0015] To determine the microbial community diversity index, at least one vegetative microbial strain was selected from Aspergillus niger, Bacillus mycosis fungoides, Bacillus subtilis, and nitrogen-fixing bacteria.

[0016] The ratio of viable cells between the nutritional microbial strains and the total number of selected functional microbial strains is 1:1 to 1:5.

[0017] Preferably, the selection of functional microbial strains based on the total petroleum hydrocarbon content in the soil determined in step one specifically involves the following steps: when the total petroleum hydrocarbon content in the soil is below 200 mg / kg, Bacillus subtilis is selected; when the total petroleum hydrocarbon content in the soil is in the range of 200-500 mg / kg, Pseudomonas aeruginosa and Bacillus subtilis with a viable count ratio of 1:1 to 1:3 are selected; when the total petroleum hydrocarbon content in the soil is above 500 mg / kg, Pseudomonas aeruginosa, Nocardia, and Sphingosine monoclonal antibodies with a viable count ratio of 1:0.5:0.5 to 1:1:1 are selected.

[0018] The selection of functional microbial strains based on the heavy metal content index determined in step one is specifically as follows: when the heavy metal content index is below 20 mg / kg, Saccharomyces cerevisiae is selected; when the heavy metal content index is in the range of 20~50 mg / kg, Aspergillus niger and Bacillus mycoides with a live cell count ratio of 1:1~1:2 are selected; when the heavy metal content index is above 50 mg / kg, Aspergillus niger, Saccharomyces cerevisiae, and Penicillium spp. with a live cell count ratio of 1:0.5:0.5~1:1:1 are selected.

[0019] The selection of nutrient microbial strains based on the microbial community diversity index determined in step one is specifically as follows: when the microbial community diversity index is in the range of 0 to 1.5, Aspergillus niger and Azotobacter brasiliensis with a viable count ratio of 1:1 to 1:3 are selected; when the microbial community diversity index is in the range of 1.5 to 3.0, Bacillus mycoides and Bacillus subtilis with a viable count ratio of 1:1 to 1:2 are selected; when the microbial community diversity index is higher than 3.0, Aspergillus niger, Bacillus mycoides and Bacillus subtilis with a viable count ratio of 1:0.5:0.5 to 1:1:1 are selected.

[0020] Preferably, the graded adaptation process in step three is as follows:

[0021] Based on the total petroleum hydrocarbon content in the soil measured in step one, different proportions of microbial agents, application depths, and application amounts are adapted.

[0022] When the total petroleum hydrocarbon content in the soil exceeds 500 mg / kg, the first appropriate level should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:1, applied at a depth of 15-20 cm, and at a rate of 300-400 kg / hm². 2 ;

[0023] When the total petroleum hydrocarbon content in the soil is 200-500 mg / kg, the second appropriate level should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:2, an application depth of 12-18 cm, and an application rate of 250-350 kg / hm². 2 ;

[0024] When the total petroleum hydrocarbon content in the soil is below 200 mg / kg, the third-adaptation grade should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:3, an application depth of 10-15 cm, and an application rate of 200-300 kg / hm². 2 .

[0025] Preferably, the heavy metal pollution level is classified according to the heavy metal content index measured in step one, specifically as follows:

[0026] Level 1 is above 50 mg / kg, Level 2 is between 20 and 50 mg / kg, and Level 3 is below 20 mg / kg.

[0027] The number corresponding to the heavy metal pollution level is compared with the number corresponding to the level based on the total petroleum hydrocarbon content index in the soil. The level corresponding to the larger number is used for mixing and application. When the two values ​​are the same, the level is directly used for mixing and application.

[0028] Preferably, it also includes step five, which is: establishing a dynamic field management strategy based on feedback from key soil indicators. The deviation of the changes in key soil indicators in the previous monitoring period from the expected target is used as the core input. The preliminary control environmental parameters in step four are used as the benchmark. The appropriate soil environmental parameter target for the current period is dynamically calculated through the pre-established effect coefficient. Corresponding field management measures are then implemented accordingly, thereby forming a closed-loop continuous management process that optimizes the soil microenvironment, improves fertilizer utilization efficiency, and promotes grassland ecosystem restoration.

[0029] Preferably, the specific process of the dynamic field management strategy is as follows:

[0030] (a) Measure the concentration of the target substance in the grassland soil weekly, while continuously monitoring soil moisture and soil temperature;

[0031] (b) Calculate the actual rate of change V of the target substance concentration in the current stage. current ;

[0032] (c) V current Compared with the preset target rate of change V target The comparison was made using the arithmetic mean S of the initial soil moisture control range from step four. cen The arithmetic mean T of the initial range of soil temperature regulation cen Based on the calculation, the target range S for soil moisture regulation this week is dynamically calculated according to the following formula. val and soil temperature regulation target range T val :

[0033] S val =S cen + k1×(V target -V current );

[0034] T val =T cen + k2×(V target -V current );

[0035] Where k1 and k2 are adjustment coefficients pre-calibrated according to the desert grassland soil type;

[0036] The calculated S val Limited to 15-32% of field capacity, T val Limited to the range of 15~35℃: If S val If the water content is below 15% of field capacity, then use 15% of field capacity; if the water content is above 32% of field capacity, then use 32% of field capacity. If T... val If the temperature is below the lower limit of soil temperature by 15℃, then take 15℃; if the temperature is above the upper limit of soil temperature by 35℃, then take 35℃.

[0037] The target range for soil moisture regulation this week has been finalized. target For [S] val -ΔS, S val +ΔS], target range for soil temperature regulation T target For [T] val -ΔT, T val +ΔT], where ΔS and ΔT are preset absolute values ​​of allowable fluctuation deviation, ΔS represents the allowable percentage fluctuation relative to field capacity, and is set to 5%; ΔT is set to 2℃;

[0038] (d) Maintain soil moisture at S through irrigation, drainage, or mulching measures. target Within the range, maintain the soil temperature at T target Within the range;

[0039] (e) Repeat steps a through d weekly, according to the latest V. current Value dynamically adjusted S target and T target This leads to feedback-based continuous management until key soil indicators reach the expected targets.

[0040] The target substance is the substance corresponding to the total petroleum hydrocarbon content index in the soil measured in step one, and / or the substance corresponding to the heavy metal content index.

[0041] Preferably, the adjustment coefficients k1 and k2 are empirical values ​​pre-calibrated based on the soil texture type of desert grassland.

[0042] For sandy soils, the calibration value of k1 ranges from 0.8 to 1.5, and the calibration value of k2 ranges from 0.3 to 0.8.

[0043] For loamy soils, the calibration value of k1 ranges from 1.0 to 2.0, and the calibration value of k2 ranges from 0.5 to 1.2.

[0044] For clay soils, the calibration value of k1 ranges from 1.2 to 2.5, and the calibration value of k2 ranges from 0.8 to 1.5.

[0045] The present invention provides at least the following beneficial effects: The microbial compound fertilizer application method for desert grasslands described herein, by establishing a dynamic field management strategy based on feedback from key soil indicators, optimizes soil environmental parameters according to real-time monitoring data of changes in key soil indicators, significantly improving the stability and efficiency of the fertilization improvement process and reducing the impact of environmental fluctuations on traditional fertilization methods. By selecting suitable functional and nutrient microbial strains for specific soil organic matter, heavy metal ion types, and soil environmental characteristics, the compound microbial agent ensures comprehensive and targeted soil improvement and nutrient transformation functions, effectively addressing the fertilization needs of soils with barrier substances. Cross-stripe culture is used for strain compatibility testing to pre-exclude antagonistic strain combinations, ensuring that all strains in the compound microbial agent can work synergistically, improving the actual application effect and reliability of the microbial fertilizer. A graded adaptation process is implemented based on the total petroleum hydrocarbon content in the soil, matching different microbial fertilizer ratios, application depths, and dosages for different content levels, achieving precise allocation of fertilizer resources and improving economic efficiency while ensuring improvement effects. By linking the heavy metal impact level with the organic matter content level and using the higher level as the basis for fertilization techniques, a sufficiently strong improvement strategy was ensured in complex obstacle soils, avoiding the risk of insufficient improvement due to different obstacle types. Weekly soil moisture and temperature guidelines were dynamically adjusted using calculation formulas, and target values ​​were constrained within a range suitable for microbial activity. This achieved precise management based on changes in key soil indicators while ensuring the safety and reliability of field management measures, preventing the risk of soil environmental imbalance.

[0046] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can implement it based on the description.

[0048] It should be understood that terms such as "having," "including," and "comprising" as used herein do not exclude the presence or addition of one or more other elements or combinations thereof. All soil moisture control targets expressed as percentages herein refer to relative humidity relative to the baseline value of the soil's "field capacity" for a specific plot. Field capacity was determined using the pressure membrane method. This method ensures the comparability and accuracy of moisture management for soils of different textures.

[0049] This invention provides a method for applying microbial compound fertilizer to desert grasslands, comprising the following steps:

[0050] Step 1: Collect soil samples from polluted desert grassland for physicochemical analysis and determine key indicators affecting soil quality and microbial activity, including total petroleum hydrocarbon content, heavy metal content, microbial community diversity index, soil moisture, soil temperature, and soil pH.

[0051] Step 2: Based on the physicochemical analysis results, select suitable functional and nutritional microbial strains. Cultivate the selected strains under aseptic conditions to prepare a compound microbial agent. The concentration range of each microbial strain in the compound microbial agent is 1×10⁻⁶. 6 ~1×10 8 CFU / g;

[0052] Step 3: Mix the compound microbial agent with the carrier bentonite to obtain the microbial compound fertilizer; based on the physicochemical analysis results of Step 1, determine the fertilization parameters using a graded adaptation process, and apply the microbial compound fertilizer to the polluted desert grassland soil;

[0053] Step 4: After completing the application, conduct preliminary regulation of the soil environmental conditions of the desert grassland, maintaining soil moisture in the range of 15-32% field capacity, soil temperature in the range of 15-35℃, and soil pH in the range of 6.5-7.0.

[0054] In the above technical solution, during the soil sample collection and physicochemical analysis stage, stainless steel soil samplers can be used to collect samples from a depth of 20 cm below the surface, and the samples should be sealed in polyethylene bags for transportation. Laboratory analysis can utilize atomic absorption spectrometry to determine heavy metal content, gas chromatography-mass spectrometry to analyze total petroleum hydrocarbon content, high-throughput sequencing to analyze microbial community diversity indices, digital soil moisture meters and thermometers to measure on-site temperature and humidity, and pH meters to measure soil acidity and alkalinity. Sample pretreatment should be completed within 24 hours of collection, and the analysis process should follow relevant national standard methods for agricultural soil surveys.

[0055] The field water holding capacity was determined using a pressure membrane apparatus method. The specific process is as follows: Representative soil samples were collected from a depth of 15-20 cm below the surface of the test site. After air drying, the samples were ground through a 2 mm sieve to obtain disturbed soil samples. A certain mass of the sieved soil sample was saturated with moisture and then evenly placed into the sample cup of the pressure membrane apparatus and placed inside the pressure chamber of the instrument. A standard suction force of 0.33 bar was applied to allow the soil sample to fully equilibrate under this pressure. After equilibration, the soil sample was removed, weighed immediately, and then dried at 105℃ to constant weight. The soil moisture content calculated based on the equilibrated wet soil mass and the dried soil mass is the field water holding capacity of the soil. The purpose is to establish a comparable humidity benchmark value for the specific implementation site. All subsequent humidity control targets expressed as percentages are relative management based on this benchmark value. For example, if the field water holding capacity of a certain plot is 15%, then the control target "30% field water holding capacity" corresponds to an actual soil moisture content of 4.5%. This method anchors the general humidity percentage control to the specific background properties of the soil, achieving standardization and precision in humidity management on different desert grasslands.

[0056] For the preparation of compound microbial agents, a commonly used laboratory biochemical incubator can be used for bacterial culture, and an autoclave is used to sterilize the culture medium and vessels. Commercially available agricultural microbial strains can be selected for functional and nutrient strains, based on the total petroleum hydrocarbon content, heavy metal content, and soil environmental characteristics. Beef extract peptone medium is used for bacterial culture, and PDA medium is used for fungal culture. The culture temperature is maintained at 28°C, the shaker speed is set to 150 r / min, and the culture time is controlled between 48 and 72 h according to the growth characteristics of the strains. During the preparation of the agent, the concentration and purity of the bacterial cells need to be monitored regularly under a microscope. When the concentration of each microbial strain reaches 1×10⁻⁶... 6 ~1×10 8 Harvest at CFU / g.

[0057] For the application of microbial compound fertilizer, agricultural rotary tillers can be used for soil tillage, and granular fertilizer spreaders can be used for fertilization. Agricultural-grade calcium-based bentonite can be used as the carrier material, with a particle size controlled at around 200 mesh. When mixing with the microbial agent, a double-helix conical mixer should be used to ensure uniform mixing. The fertilization depth can be adjusted according to soil moisture conditions; a shallower depth is chosen for clay soils, and a deeper depth for sandy soils. The fertilizer application rate per unit area is determined based on the graded and adapted process. In specific operations, parameters such as soil bulk density can be considered, and the actual fertilizer application rate per row can be calculated and controlled by adjusting the spreader's opening size and travel speed to achieve precise fertilizer delivery per unit area.

[0058] This method can maintain the activity of soil microorganisms in desert grasslands at an optimal level, promote the transformation of soil obstacles and soil improvement, enhance soil fertility and ecological function, ensure the stability of fertilization management, adapt to the improvement needs of grassland soils with different levels of obstacles, and provide technical support for the standardized and precise application of microbial fertilizers in desertified grasslands.

[0059] In other technical solutions, step two involves selecting suitable functional and nutritional microbial strains based on physicochemical analysis results. The specific selection method is as follows:

[0060] For the total petroleum hydrocarbon content index in soil, at least one functional microbial strain is selected from Pseudomonas, Nocardia, Sphingomonas and Bacillus.

[0061] For heavy metal content indicators, at least one functional microbial strain should be selected from Bacillus, Aspergillus, Yeast and Penicillium.

[0062] To determine the microbial community diversity index, at least one vegetative microbial strain was selected from Aspergillus niger, Bacillus mycosis fungoides, Bacillus subtilis, and nitrogen-fixing bacteria.

[0063] The ratio of viable cells between the nutritional microbial strains and the total number of selected functional microbial strains is 1:1 to 1:5.

[0064] In the above technical solutions, the selection of bacterial strains can be tailored to the specific characteristics of soil obstacles. For substances corresponding to the total petroleum hydrocarbon content index in the soil, at least one of the following can be selected: commercially available *Pseudomonas* powder, *Nocardia* freeze-dried powder, *Sphingomonas* slant culture, or *Bacillus* liquid culture. For substances corresponding to the heavy metal content index, at least one of the following can be selected: *Bacillus* preparation, *Aspergillus* spore suspension, active dry yeast powder, or frozen *Penicillium* strains. For improving soil physicochemical properties and nutrient conditions, at least one of the following can be selected: *Aspergillus niger* microbial preparation, *Bacillus mycoides* culture, *Bacillus subtilis* powder, or nitrogen-fixing bacteria liquid inoculant.

[0065] Laboratory procedures can be performed in a sterile workbench, using disposable plastic petri dishes and test tubes as containers. Strain activation can be achieved using a constant-temperature shaker at 28°C and a shaker speed of 150 r / min. Strain expansion can be carried out in glass Erlenmeyer flasks, with the liquid volume controlled between 20% and 30% of the container's volume. Bacterial concentration can be determined by measuring the OD600 value using a UV spectrophotometer and identifying viable cells using the plate count method.

[0066] The bacterial strain compounding process can utilize sterile centrifuge tubes as mixing containers, with micropipettes used to precisely control the amount of bacterial solution added. The ratio of viable cells between nutrient microbial strains and functional microbial strains is controlled within the range of 1:1 to 1:5, and the specific ratio can be adjusted. The compound microbial agent can be prepared into bacterial powder using a vacuum freeze-drying process, and stored in a sealed container at 4°C, maintaining a viable cell survival rate of over 80% for 12 months; alternatively, it can be mixed with sterilized bentonite carrier and granulated to form granules, which can be stored under cool, dry conditions.

[0067] This method ensures that the strains in the compound microbial agent maintain a suitable ratio, which is conducive to exerting synergistic effects, improving the transformation of soil barrier substances and soil improvement efficiency, while avoiding competitive inhibition between strains due to imbalance in ratio, thus maintaining the activity and adaptability of the microbial agent in the target grassland soil environment.

[0068] In other technical solutions, the selection of functional microbial strains based on the total petroleum hydrocarbon content in the soil determined in step one specifically involves: when the total petroleum hydrocarbon content in the soil is below 200 mg / kg, Bacillus subtilis is selected; when the total petroleum hydrocarbon content in the soil is in the range of 200-500 mg / kg, Pseudomonas aeruginosa and Bacillus subtilis with a viable count ratio of 1:1 to 1:3 are selected; when the total petroleum hydrocarbon content in the soil is above 500 mg / kg, Pseudomonas aeruginosa, Nocardia, and Sphingosine monoclonal antibodies with a viable count ratio of 1:0.5:0.5 to 1:1:1 are selected.

[0069] The selection of functional microbial strains based on the heavy metal content index determined in step one is specifically as follows: when the heavy metal content index is below 20 mg / kg, Saccharomyces cerevisiae is selected; when the heavy metal content index is in the range of 20~50 mg / kg, Aspergillus niger and Bacillus mycoides with a live cell ratio of 1:1~1:2 are selected; when the heavy metal content index is above 50 mg / kg, Aspergillus niger, Saccharomyces cerevisiae and Penicillium spp. with a live cell ratio of 1:0.5:0.5~1:1:1 are selected.

[0070] The selection of nutrient microbial strains based on the microbial community diversity index determined in step one is specifically as follows: when the microbial community diversity index is in the range of 0 to 1.5, Aspergillus niger and Azotobacter brasiliensis with a viable count ratio of 1:1 to 1:3 are selected; when the microbial community diversity index is in the range of 1.5 to 3.0, Bacillus mycoides and Bacillus subtilis with a viable count ratio of 1:1 to 1:2 are selected; when the microbial community diversity index is higher than 3.0, Aspergillus niger, Bacillus mycoides and Bacillus subtilis with a viable count ratio of 1:0.5:0.5 to 1:1:1 are selected.

[0071] In the above technical solutions, to meet the needs of grassland management and rapid decision-making, simpler and more operational alternative measurement methods can be adopted. These methods are often used as rapid screening or semi-quantitative means in grassland ecological monitoring.

[0072] The determination of total petroleum hydrocarbon content in soil is as follows: Weigh 5.0 g of air-dried soil sample (mass denoted as m), add 10 mL of n-hexane as the extractant, shake and extract for 10 min, then let stand. Take the supernatant and measure its absorbance (A) at 254 nm using a UV-Vis spectrophotometer. Using a standard curve pre-plotted with gradient concentration standard solutions, the concentration of total petroleum hydrocarbons (C, in mg / L) in the extract can be calculated based on the A value. Then, the approximate content of total petroleum hydrocarbons in the soil can be calculated using the formula "soil content (mg / kg = (C × V) / m)" (where V is the volume of the extract, 10 mL).

[0073] For the determination of heavy metal content, a portable X-ray fluorescence spectrometer can be used for rapid on-site screening. After pressing the soil sample into a sample cup and leveling the surface, the instrument is calibrated using its built-in standard slides. The instrument probe is then placed firmly against the sample surface for approximately 60 seconds to directly read the approximate content (in mg / kg) of target elements such as lead (Pb), cadmium (Cd), and chromium (Cr). The sum of the measured values ​​for each target element is then used as the heavy metal content indicator (in mg / kg) for subsequent decision-making.

[0074] The Biolog ecological microplate method can be used to assess the diversity index of microbial communities. Soil suspensions are prepared and inoculated onto Biolog ECO microplates. After incubation at 25°C for 72 hours, the absorbance (OD) of each carbon source well at 590 nm is read using a microplate reader. i First, adjust the OD of each hole. i The average absorbance of the three control wells was subtracted from the original value to obtain the correction value. Then, the correction values ​​of all carbon source wells were summed, and the proportion of each well's correction value to the total was calculated to obtain the normalized relative metabolic activity value (n). i Finally, based on the McIntosh exponent formula... The calculation is performed, where S represents the total number of effective carbon source pores (e.g., 31 in a Biolog ECO plate); the resulting U value is a quantitative indicator reflecting the diversity of the metabolic function of the soil microbial community, which is used for subsequent management decisions.

[0075] This strain adaptation scheme aims to construct a standardized logic for the screening and compounding of microbial strains directly linked to key quantitative indicators of soil. Its core objective is to achieve precise formulation of microbial fertilizers, thereby improving the targeting and effectiveness of fertilization in harsh or degraded environments such as desert grasslands. The selection logic for the total petroleum hydrocarbon content indicator in soil is based on the differences in the conversion capacity and adaptability of different microorganisms to organic matter. When this indicator is low, it indicates a light soil organic load, with the main need being to improve soil structure and basic fertility; therefore, Bacillus subtilis, which has both growth-promoting and improvement functions, is selected. When the indicator rises to a moderate level, it means a stronger organic matter conversion capacity is needed; therefore, the highly efficient degrading bacterium Pseudomonas aeruginosa is introduced and compounded with Bacillus subtilis in a specific ratio to synergistically exert degradation and growth-promoting functions. When the indicator enters a high-level range, it indicates significant organic matter conversion pressure; therefore, a multifunctional composite system composed of Pseudomonas aeruginosa, Nocardia, and Sphingosine monocytogenes is adopted to address the complex situation by broadening the degradation spectrum and enhancing metabolic complementarity. The selection of strains for heavy metal content indicators is based on the different microbial tolerance, adsorption, or transformation mechanisms of heavy metal ions. For low concentrations, *Saccharomyces cerevisiae*, which is adaptable to various environments and has a certain adsorption capacity, is selected. For medium concentrations, a combination of *Aspergillus niger* and *Bacillus mycoides*, which can passivate heavy metals through adsorption and precipitation, is used. For high concentrations, *Aspergillus niger*, *Saccharomyces cerevisiae*, and *Penicillium* are used in combination, aiming to enhance the stabilization effect on heavy metals through the synergistic effect of multiple mechanisms. For the selection of nutrient-rich microbial strains based on the microbial community diversity index, the principle is to differentiate functional bacteria to optimize soil biological functions according to the existing microecological conditions of the soil. When the diversity index is low, indicating a fragile soil microecology, *Aspergillus niger* and *Brasilaria sinensis* are selected to simultaneously improve soil structure and supplement nitrogen sources to initiate ecological restoration. When the diversity index is moderate, the soil has a certain biological foundation, and the focus shifts to promoting nutrient cycling and inhibiting potential diseases; therefore, a combination of *Bacillus mycoides* and *Bacillus subtilis* is selected. When the diversity index is high, the soil microecology is relatively healthy. The goal is to further optimize and stabilize community functions. Therefore, a multi-species combination of Aspergillus niger, Bacillus mycosis fungoides, and Bacillus subtilis is selected to provide more comprehensive auxiliary functions such as growth promotion, phosphorus solubilization, and enzyme production. The overall scheme forms a set of decision-making and operable precision fertilization formulation methods by quantitatively correlating specific detection thresholds, strain functions, and viable bacteria ratios. It aims to overcome the shortcomings of traditional microbial fertilizer formulations, such as single formulations and poor environmental adaptability, thereby improving the reliability, stability, and final effect of microbial fertilizer application under specific soil obstacle conditions.

[0076] In other technical solutions, the graded adaptation process in step three is specifically as follows:

[0077] Based on the total petroleum hydrocarbon content in the soil measured in step one, different proportions of microbial agents, application depths, and application amounts are adapted.

[0078] When the total petroleum hydrocarbon content in the soil exceeds 500 mg / kg, the first appropriate level should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:1, applied at a depth of 15-20 cm, and at a rate of 300-400 kg / hm². 2 ;

[0079] When the total petroleum hydrocarbon content in the soil is 200-500 mg / kg, the second appropriate level should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:2, an application depth of 12-18 cm, and an application rate of 250-350 kg / hm². 2 ;

[0080] When the total petroleum hydrocarbon content in the soil is below 200 mg / kg, the third-adaptation grade should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:3, an application depth of 10-15 cm, and an application rate of 200-300 kg / hm². 2 .

[0081] The above technical solution achieves precise and differentiated application of microbial compound fertilizer by quantitatively correlating the total petroleum hydrocarbon content in the soil with a set of predefined fertilization parameters. When the index exceeds 500 mg / kg, it indicates that the soil has a heavy organic load; therefore, the first adaptation level is adopted, using a higher inoculant ratio (1:1) and a larger application rate (300-400 kg / hm²). 2 To ensure sufficient functional microorganisms are added, a deeper application depth (15-20 cm) is used to allow the microbial agent to reach soil layers where pollutants may accumulate, thereby enhancing contact and degradation efficiency. When the index is in the moderate range of 200-500 mg / kg, the second adaptation level is used, and the ratio of microbial agent (1:2) and the application rate (250-350 kg / hm²) are appropriately reduced. 2 While ensuring effectiveness, the principle of economy should be reflected; the application depth should be adjusted to 12-18 cm to match the more common distribution layers of pollutants. When the index is below 200 mg / kg, it indicates a relatively light organic load, and the main goal is soil improvement and fertility cultivation. Therefore, the third adaptation level is adopted to further reduce the input of microbial agents (ratio 1:3, dosage 200-300 kg / hm). 2 The fertilizer is applied at a relatively shallow depth (10-15 cm) to concentrate microorganisms near the plant root zone, thereby promoting the restoration and enhancement of soil ecological functions. The entire design aims to overcome the shortcomings of traditional single fertilization models by establishing a clear mapping relationship between "detection indicators - adaptation level - operating parameters," and to achieve precise resource allocation and efficient improvement for soils with different levels of pollution or obstacles.

[0082] In other technical solutions, the heavy metal pollution level is classified based on the heavy metal content index measured in step one, specifically as follows:

[0083] Level 1 is above 50 mg / kg, Level 2 is between 20 and 50 mg / kg, and Level 3 is below 20 mg / kg.

[0084] The number corresponding to the heavy metal pollution level is compared with the number corresponding to the level based on the total petroleum hydrocarbon content index in the soil. The level corresponding to the larger number is used for mixing and application. When the two values ​​are the same, the level is directly used for mixing and application.

[0085] In the aforementioned technical solution, when both convertible organic matter and heavy metals are present in the soil, a clear decision-making rule is needed to determine the intensity of the dominant management strategy. By quantifying heavy metal content into three levels with clearly defined thresholds and adopting the same numbering logic as the parallel level system for organic matter, a directly comparable framework is established. The principle of selecting the level corresponding to the "largest number" essentially employs the "maximum factor" or "weakest link" principle, aiming to ensure that the final fertilization process intensity responds to the most prominent or highest-risk obstacle type in the soil. For example, if organic matter is at level two and heavy metals at level one, then process parameters for level one are used, ensuring sufficient treatment intensity under combined conditions and avoiding underestimation of the impact of any obstacle factor, which could lead to substandard overall improvement. This comparison mechanism is logically clear, easy to operate, requires no complex calculations, and is conducive to rapid application in field practice. This allows the application strategy of microbial compound fertilizer to have a clear decision-making basis, necessary intervention intensity, and overall reliability when facing common and variable combined obstacles in desert grasslands, ultimately serving to improve the accuracy and effectiveness of fertilization management.

[0086] Other technical solutions also include step five, which specifically involves: establishing a dynamic field management strategy based on feedback from key soil indicators. The deviation of the changes in key soil indicators in the previous monitoring period from the expected target is used as the core input. The preliminary environmental parameters of step four are used as a benchmark. The appropriate soil environmental parameter target for the current period is dynamically calculated through the pre-established effect coefficients. Corresponding field management measures are then implemented accordingly, thereby forming a closed-loop continuous management process that optimizes the soil microenvironment, improves fertilizer utilization efficiency, and promotes grassland ecosystem restoration.

[0087] In the above technical solution, the dynamic management of grassland soil environment can utilize outdoor IoT sensors to monitor soil temperature and humidity in real time. An intelligent control unit operates the management algorithm, regulating soil moisture through irrigation systems and adjusting soil temperature through agronomic measures such as mulching or shade netting. The system collects environmental data and management parameters on a 7-day cycle, and the management algorithm calculates based on preset effect coefficients. During system operation, parameter protection ranges are set; when calculated values ​​exceed preset thresholds, protection values ​​are automatically applied to ensure the grassland soil microenvironment remains within a suitable range for microbial activity. The system can be equipped with a data storage module to record all management processes and soil improvement effect data; the recorded data can be used to evaluate the improvement effect.

[0088] In other technical solutions, the specific process of the dynamic field management strategy is as follows:

[0089] (a) Measure the concentration of the target substance in the grassland soil weekly, while continuously monitoring soil moisture and soil temperature;

[0090] (b) Calculate the actual rate of change V of the target substance concentration in the current stage. current ;

[0091] (c) V current Compared with the preset target rate of change V target The comparison was made using the arithmetic mean S of the initial soil moisture control range from step four. cen The arithmetic mean T of the initial range of soil temperature regulation cen Based on the calculation, the target range S for soil moisture regulation this week is dynamically calculated according to the following formula. val and soil temperature regulation target range T val :

[0092] S val =S cen + k1×(V target -V current );

[0093] T val =T cen + k2×(V target -V current );

[0094] Where k1 and k2 are adjustment coefficients pre-calibrated according to the desert grassland soil type;

[0095] The calculated S val Limited to 15-32% of field capacity, T val Limited to the range of 15~35℃: If S valIf the water content is below 15% of field capacity, then use 15% of field capacity; if the water content is above 32% of field capacity, then use 32% of field capacity. If T... val If the temperature is below the lower limit of soil temperature by 15℃, then take 15℃; if the temperature is above the upper limit of soil temperature by 35℃, then take 35℃.

[0096] The target range for soil moisture regulation this week has been finalized. target For [S] val -ΔS, S val +ΔS], target range for soil temperature regulation T target For [T] val -ΔT, T val +ΔT], where ΔS and ΔT are preset absolute values ​​of allowable fluctuation deviation, ΔS represents the allowable percentage fluctuation relative to field capacity, and is set to 5%; ΔT is set to 2℃;

[0097] (d) Maintain soil moisture at S through irrigation, drainage, or mulching measures. target Within the range, maintain the soil temperature at T target Within the range;

[0098] (e) Repeat steps a through d weekly, according to the latest V. current Value dynamically adjusted S target and T target This leads to feedback-based continuous management until key soil indicators reach the expected targets.

[0099] The target substance is the substance corresponding to the total petroleum hydrocarbon content index in the soil measured in step one, and / or the substance corresponding to the heavy metal content index.

[0100] The aforementioned technical solution, by establishing a closed-loop control system based on effect feedback, achieves precise and adaptive regulation of the soil microenvironment after the application of microbial fertilizers, thereby maximizing the stability and efficiency of fertilizer effectiveness. Its core logic lies in using the "rate of change of key soil indicators (target substances)"—a core dynamic parameter directly reflecting the metabolic activity of the microbial community and the effect of fertilizer application—as a key input signal for real-time regulation of the soil hydrothermal environment.

[0101] The system monitors changes in the concentration of target substances on a weekly basis, calculates the actual rate of change, and compares it with a pre-set scientifically determined target rate. The difference between the two is considered a quantitative indicator of the deviation between current environmental conditions and the ideal fertilizer efficiency rate. This deviation is converted into a correction amount for the arithmetic mean of soil moisture targets and the arithmetic mean of temperature targets through pre-calibrated adjustment coefficients related to soil type and target substance characteristics.

[0102] The calculation formula embodies the direct negative feedback control concept: if the actual rate is lower than the target, the calculated target environmental parameters tend to be adjusted in a direction more favorable to microbial activity (such as appropriately increasing humidity or temperature) to stimulate metabolism and catch up with the target; conversely, fine-tuning occurs in the opposite direction. To prevent over-regulation leading to environmental dysbiosis, the calculated target values ​​are strictly constrained within a preset safety boundary suitable for general microbial activity (such as humidity 15-32% field capacity, temperature 15-35℃). Finally, by setting reasonable allowable fluctuation deviations (ΔS, ΔT), a control target range centered on the calculated value is formed ([S... val -ΔS, S val +ΔS];[T val -ΔT, T val +ΔT]), to guide specific field management measures such as irrigation, drainage, and mulching.

[0103] By repeatedly executing the cycle of "monitoring-calculation-comparison-adjustment," this strategy transforms soil moisture and temperature from fixed values ​​into variables that can be dynamically optimized based on feedback from the actual effects of fertilizer application. This achieves a shift in management models from "static environmental maintenance" to "dynamic effect synergy," enabling field management measures to proactively adapt to fluctuations in the external environment and dynamic changes in internal soil processes. It provides continuously optimized auxiliary environmental support for core fertilization measures, thereby improving the overall utilization efficiency and reliability of microbial fertilizer resources under complex field conditions.

[0104] In other technical solutions, the adjustment coefficients k1 and k2 are empirical values ​​pre-calibrated based on the soil texture type of desert grassland.

[0105] For sandy soils, the calibration value of k1 ranges from 0.8 to 1.5, and the calibration value of k2 ranges from 0.3 to 0.8.

[0106] For loamy soils, the calibration value of k1 ranges from 1.0 to 2.0, and the calibration value of k2 ranges from 0.5 to 1.2.

[0107] For clay soils, the calibration value of k1 ranges from 1.2 to 2.5, and the calibration value of k2 ranges from 0.8 to 1.5.

[0108] In the aforementioned technical solution, by pre-calibrating adjustment coefficients directly related to soil texture, the dynamic management model gains a physical basis and adaptability. Soil texture, as a stable baseline attribute, profoundly influences water infiltration, retention, conduction, as well as heat capacity and thermal conductivity, thus determining the required management intensity (such as irrigation volume and cover degree) and the resulting micro-ecological effects to change unit soil moisture or temperature. For example, sandy soils have weak water retention capacity and low heat capacity, and their moisture and temperature responses to environmental control measures are typically more sensitive and rapid; while clay soils are the opposite. Therefore, applying the same environmental control correction to soils with different textures will result in significantly different actual micro-environmental changes and subsequent promotional effects on microbial activity and target substance transformation processes. By calibrating different ranges of k1 and k2 values ​​for sandy, loamy, and clay soils respectively, this difference in physical and ecological response is essentially quantified and integrated into the management algorithm. This allows the theoretical control targets calculated by the model to more accurately reflect the actual operational intensity required for different soils to achieve the expected control effects, thereby improving the accuracy and universality of the entire dynamic feedback management.

[0109] The pre-calibrated methods were implemented through controlled experiments. First, representative sandy, loamy, and clayey desert grassland soil samples were prepared or collected. In a controlled environment chamber, a series of soil moisture gradients (e.g., covering 15% to 32% of field capacity) and soil temperature gradients (e.g., covering 15°C to 35°C) were established for each soil texture, constituting multiple temperature and humidity combinations. In each treatment, a standardized microbial compound fertilizer was applied, and the changes in one or more key indicators characterizing microbial activity or soil processes (such as soil respiration rate, specific enzyme activity, or target substance concentration) over time were continuously monitored, and their stable rates of change were calculated. Subsequently, regression analysis was performed using the actual controlled quantities in the experiment (e.g., the difference between soil moisture and the set baseline, the difference between soil temperature and the set baseline) as independent variables and the measured rates of change of key soil indicators as dependent variables. For each soil texture, empirical slopes (reflected as the baseline k1) and temperature changes (reflected as the baseline k2) between moisture changes and indicator rates of change were obtained through fitting analysis. After repeated experiments and data collection from soil samples from different sources, statistical analysis can be performed to derive reliable ranges for k1 and k2 values ​​for each soil texture. These ranges are then incorporated into the parameter library of the management model. In practical applications, the corresponding coefficient range can be retrieved based on the soil texture type determined in the field to initiate a feedback-based dynamic management process.

[0110] Example 1

[0111] In a desertified grassland, the soil exhibited problems such as impaired organic matter transformation and high heavy metal content. The method described in this invention was used for fertilization improvement. The goal was to improve key soil indicators to levels suitable for pasture growth through precise microbial fertilization management. The specific implementation process is as follows:

[0112] (1) Soil diagnosis and customized microbial fertilizer

[0113] Soil samples were collected from a depth of 0-20 cm using a grid method for physicochemical analysis. The results were as follows: total petroleum hydrocarbon content in the soil was 650±25 mg / kg; heavy metal content (calculated as the sum of cadmium (Cd) and lead (Pb)) was (36.2±2.1) + (45.5±3.3) = 81.7±5.4 mg / kg; microbial community diversity index (Shannon index H') was 1.2±0.3; soil moisture was 8% field capacity (the actual measured field capacity of this plot was 15%); soil temperature was 22±3 ℃; soil pH was 6.7; and the soil texture was sandy loam.

[0114] Strains were selected based on the above test results:

[0115] To target the total petroleum hydrocarbon content in soil (650 mg / kg > 500 mg / kg), Pseudomonas aeruginosa, Nocardia, and Sphingosine monocytogenes were selected, and the ratio of viable bacteria among the three was controlled to be 1:0.8:0.8.

[0116] For heavy metal content indicators (81.7 mg / kg > 50 mg / kg), Aspergillus niger, Saccharomyces cerevisiae and Penicillium were selected, and the ratio of viable bacteria among the three was controlled to be 1:1:1.

[0117] For the microbial community diversity index (1.2, in the range of 0 to 1.5), Aspergillus niger and Azotobacter brasiliensis were selected as nutrient microbial strains, and the ratio of their viable counts was controlled at 1:2.

[0118] All selected strains underwent cross-strike compatibility testing. After 72 hours of incubation, good growth was observed at the interface between strains, with no inhibition bands, indicating a compatible combination. The above strains were then cultured to concentrations ≥1×10⁻⁶. 8 CFU / mL, mixed according to the above-determined ratio, to prepare a compound microbial agent.

[0119] (2) Graded and appropriate fertilization and preliminary regulation

[0120] First, determine the appropriate fertilization level. The total petroleum hydrocarbon content in the soil (650 mg / kg) corresponds to the first appropriate level (>500 mg / kg). The heavy metal content (81.7 mg / kg) exceeds 50 mg / kg and is classified as the first level. The level corresponding to the larger of the two (both being in the first level) is selected; therefore, the first appropriate level is ultimately adopted for fertilization.

[0121] Fertilization should be carried out according to the first adaptation level parameters: Mix compound microbial inoculant with 200-mesh calcium-based bentonite at a weight ratio of 1:1 to prepare granular microbial compound fertilizer with a total viable count ≥ 5.0 × 10⁻⁶. 7 CFU / g. A deep tillage fertilizer applicator was used, with the fertilization depth controlled at 18 cm and the application rate at 350 kg / hm². 2 .

[0122] Immediately after fertilization, preliminary soil environmental control was carried out. Soil moisture was regulated and maintained at 30% field capacity (corresponding to approximately 4.5% actual volumetric water content) using a drip irrigation system. Transparent mulch was used to stabilize the soil temperature in the 0-20 cm layer within the range of 24-26 ℃. A small amount of calcium carbonate was applied to stabilize the soil pH at 6.8-7.0.

[0123] (3) Implementation of dynamic feedback management

[0124] Deploy monitoring system: every 100 m 2 Install a soil temperature and humidity sensor (probe buried 20 cm deep); every 200 m 2 Establish a soil sampling point to collect soil samples weekly to determine the concentration of key indicators.

[0125] Target setting: The expected weekly rate of change Vtarget for key soil indicators (in this embodiment, the comprehensive improvement of total petroleum hydrocarbon content and heavy metal content in soil) is set at a decrease of 10.5 mg / kg per week.

[0126] Parameter determination: Adjustment coefficients k1 and k2 are determined based on soil texture type. In this embodiment, the soil texture is sandy loam. Referring to the coefficient range for sandy soil and based on the calibration results of preliminary experiments on similar contaminated soils, k1 = 1.2 (range 0.8 to 1.5) and k2 = 0.5 (range 0.3 to 0.8) were selected. The target rate of change Vtarget is set to a decrease of 7.5 mg / kg per week. This target value is determined comprehensively based on the preliminary assessment results of the microbial remediation potential under local climatic conditions and the project cycle requirements.

[0127] Management process execution: Taking the first week of management as an example.

[0128] First, based on the weekly sampling and measurement results, the actual weekly rate of change (Vcurrent) of key soil indicators was calculated to be 5.8 mg / kg.

[0129] Secondly, using the arithmetic mean of soil moisture (30% field capacity) and the arithmetic mean of soil temperature (25℃) obtained from the initial adjustment in step (2) as the calculation benchmark, the calculation is performed according to the formula:

[0130] The calculated target value for soil moisture, Sval, is 30 + 1.2 × (7.5 - 5.8) = 32.0.

[0131] The calculated target soil temperature Tval = 25 + 0.5 × (7.5 - 5.8) = 25.9;

[0132] Subsequently, the calculated values ​​were constrained: Sval (32.0) is within the range of 15% to 32% field capacity, so 32.0 is taken; Tval (26.3) is within the range of 15 to 35℃, so 26.3 is taken.

[0133] Ultimately, the target range for this week's regulation was determined as follows: Soil moisture Starget = [Sval - 5%, Sval + 5%] = [27%, 37%] field capacity; Soil temperature Ttarget = [Tval - 2℃, Tval + 2℃] = [23.9℃, 27.9℃]. Soil moisture and temperature will be maintained within this target range by automatically controlling the drip irrigation system and adjusting the surface mulch.

[0134] Subsequently, the above monitoring, calculation and control cycle is repeated weekly. Based on the latest monitored Vcurrent value, the Starget and Ttarget for the next cycle are dynamically adjusted to form a closed-loop, effect-based continuous management process until the key soil indicators reach the expected improvement target.

[0135] Comparative Example 1 (Traditional strain selection)

[0136] The same target plot as in Example 1 was used. The selection of the microbial agent was not based on soil diagnostic indicators, but rather on a commercially available general-purpose compound microbial agent (mainly composed of Bacillus subtilis, Bacillus licheniformis, and Saccharomyces cerevisiae). The fertilization process, application rate, depth, and post-fertilization environmental control and dynamic field management strategies were all implemented strictly according to the steps in Example 1.

[0137] Comparative Example 2 (Traditional Fertilization Process)

[0138] The same target plot and the same compound microbial agent (i.e., the targeted agent prepared according to step (1) of Example 1) were used as in Example 1. During fertilization, a graded adaptation process was not adopted; instead, the recommended dosage of local conventional microbial fertilizers was uniformly applied at 250 kg / hm². 2 The application rate and depth of the fertilizer were determined. The initial environmental control (humidity, temperature, pH) and dynamic field management strategies after fertilization were carried out in strict accordance with the steps in Example 1.

[0139] Comparative Example 3 (Static Environment Management)

[0140] The same target plot, compound microbial inoculant, and fertilization process (including dosage and depth) as in Example 1 were used. After fertilization, a one-time preliminary adjustment of the soil environment was performed, adjusting soil moisture to 30% field capacity, temperature to 25℃, and pH to 6.8-7.0. Thereafter, no further active dynamic adjustments based on monitoring feedback were made; soil temperature and humidity only changed with the natural environment.

[0141] For Example 1 and Comparative Examples 1, 2, and 3 above, soil samples from the 0-20 cm layer were collected weekly at fixed points to determine the total petroleum hydrocarbon content (as TPH, mg / kg), heavy metal content (as total Cd+Pb, mg / kg), and microbial community diversity index (Shannon index H'). Simultaneously, IoT sensors were used to continuously monitor and record the daily fluctuations of soil moisture (%field capacity) and soil temperature (°C).

[0142] Improvement efficiency: Measured by the weekly average rate of change of key indicators (TPH and total heavy metals) and the number of weeks required to reach the preset safety threshold.

[0143] Stability: Assessed by the percentage of time (%) during which soil moisture and temperature remain within the target range (Starget, Ttarget).

[0144] Ecological effects: The increase in the microbial community diversity index (H') at the end of the experiment compared to the increase at the beginning of the experiment (ΔH') was used for evaluation.

[0145] Data processing: All data are expressed as mean ± standard deviation. One-way ANOVA combined with Duncan's multiple comparison test was used to analyze the significance of differences in each indicator among different treatment groups at week 10 (significance level α = 0.05).

[0146] The test results are shown in Table 1 below.

[0147] Table 1. Effects of different treatments on soil improvement and microenvironment stability in desert grassland

[0148] (Experiment duration: 10 weeks)

[0149]

[0150] Note: Different letters after the data in the same row in the table indicate significant differences at the p<0.05 level. "Meets the standard" means that the indicator value has dropped below the risk screening value specified in the "Soil Environmental Quality Standard for Risk Control of Soil Pollution in Agricultural Land (Trial)" (GB 15618-2018). The time required to meet the standard is estimated based on the measured weekly average rate of decline and the initial value.

[0151] Test results demonstrate that Example 1 exhibited higher weekly removal rates of TPH and heavy metals than all comparative examples (p<0.05). Compared to Comparative Example 1 (general bacterial agent), it demonstrates the crucial role of precise strain adaptation; compared to Comparative Example 2 (incorrect process), it demonstrates the importance of graded adaptive fertilization processes in improving the remediation efficiency of heavily polluted soils; and compared to Comparative Example 3 (static management), its efficiency was several times higher, verifying the decisive influence of dynamic feedback management on maintaining a highly efficient metabolic environment for microorganisms.

[0152] Regarding microenvironmental stability, Example 1, through a dynamic feedback algorithm, maintained soil moisture and temperature within a suitable range at a higher rate than Comparative Examples 1 and 2, which only used preliminary control targets (p<0.05), demonstrating the adaptive advantage of the dynamic management algorithm. All groups implementing dynamic management (Example 1, Comparative Examples 1 and 2) outperformed Comparative Example 3, which used static management (p<0.01), highlighting the absolute necessity of continuous environmental control in desert environments.

[0153] Regarding soil ecological effects, Example 1 showed the greatest increase in microbial community diversity (ΔH'=1.8), which was higher than other comparative examples (p<0.05). This indicates that the "diagnosis-adaptation-dynamic management" whole-chain solution of this invention is most conducive to the restoration and functional optimization of soil microecology, while optimization of a single link (such as only improving the strain or only improving the fertilization process) or lack of management have limited ecological improvement effects.

[0154] In summary, through systematic comparative experiments and data analysis, the complete technical system proposed in this invention, which includes precise microbial agent adaptation, graded fertilization process, and dynamic environmental management, has been proven to synergistically and significantly improve the soil improvement efficiency of desert grassland, maintain a stable microenvironment for fertilizer effectiveness, and promote the restoration of soil ecological functions. The overall effect is significantly better than any local or traditional improvement scheme, fully verifying the beneficial effects of this invention.

[0155] It should be understood that the above embodiments only demonstrate specific applications under conditions of severe combined pollution. For those skilled in the art, based on the selection logic and grading adaptation rules disclosed in the specification, for soil indicators falling into other ranges (such as organic matter content below 200 mg / kg, or heavy metal content between 20-50 mg / kg, etc.), the corresponding strain combinations, ratios, and fertilization process parameters can be selected, and the same dynamic field management strategy can be applied to achieve the objectives of this invention. This falls within the scope of what those skilled in the art can reasonably infer and implement based on the disclosure of this invention.

[0156] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for applying microbial compound fertilizer to desert grasslands, characterized in that, Includes the following steps: Step 1: Collect soil samples from polluted desert grassland for physicochemical analysis and determine key indicators affecting soil quality and microbial activity, including total petroleum hydrocarbon content, heavy metal content, microbial community diversity index, soil moisture, soil temperature, and soil pH. Step 2: Based on the physicochemical analysis results, select suitable functional and nutritional microbial strains. Cultivate the selected strains under aseptic conditions to prepare a compound microbial agent. The concentration range of each microbial strain in the compound microbial agent is 1×10⁻⁶. 6 ~1×10 8 CFU / g; Step 3: Mix the compound microbial agent with the carrier bentonite to obtain the microbial compound fertilizer; based on the physicochemical analysis results of Step 1, determine the fertilization parameters using a graded adaptation process, and apply the microbial compound fertilizer to the polluted desert grassland soil; The graded adaptation process specifically includes: Based on the total petroleum hydrocarbon content in the soil measured in step one, different proportions of microbial agents, application depths, and application amounts are adapted. When the total petroleum hydrocarbon content in the soil exceeds 500 mg / kg, the first appropriate level should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:1, applied at a depth of 15-20 cm, and at a rate of 300-400 kg / hm². 2 ; When the total petroleum hydrocarbon content in the soil is 200-500 mg / kg, the second appropriate level should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:2, an application depth of 12-18 cm, and an application rate of 250-350 kg / hm². 2 ; When the total petroleum hydrocarbon content in the soil is below 200 mg / kg, the third-adaptation grade should be used, with a weight ratio of compound microbial agent to bentonite carrier of 1:3, an application depth of 10-15 cm, and an application rate of 200-300 kg / hm². 2 ; Step 4: After completing the application, conduct preliminary regulation of the soil environmental conditions of the desert grassland, maintaining soil moisture in the range of 15-32% field capacity, soil temperature in the range of 15-35℃, and soil pH in the range of 6.5-7.

0. Step 5: Establish a dynamic field management strategy based on feedback from key soil indicators. The deviation of the changes in key soil indicators in the previous monitoring period from the expected target is used as the core input. The preliminary environmental parameters of Step 4 are used as the benchmark. The appropriate soil environmental parameter target for the current period is dynamically calculated through the pre-established effect coefficient. Corresponding field management measures are then implemented to form a closed-loop continuous management process that optimizes the soil microenvironment, improves fertilizer utilization efficiency, and promotes grassland ecosystem restoration. The heavy metal pollution level is determined based on the heavy metal content measured in step one, as follows: Level 1 is above 50 mg / kg, Level 2 is between 20 and 50 mg / kg, and Level 3 is below 20 mg / kg. The number corresponding to the heavy metal pollution level is compared with the number corresponding to the level based on the total petroleum hydrocarbon content index in the soil. The level corresponding to the larger number is used for mixing and application. When the two values ​​are the same, the level is directly used for mixing and application. The specific process of the dynamic field management strategy is as follows: (a) Measure the concentration of the target substance in the grassland soil weekly, while continuously monitoring soil moisture and soil temperature; (b) Calculate the actual rate of change V of the target substance concentration in the current stage. current ; (c) V current Compared with the preset target rate of change V target The comparison was made using the arithmetic mean S of the initial soil moisture control range from step four. cen The arithmetic mean T of the initial range of soil temperature regulation cen Based on the calculation, the target range S for soil moisture regulation this week is dynamically calculated according to the following formula. val and soil temperature regulation target range T val : S val =S cen + k1×(V target -V current ); T val =T cen + k2×(V target -V current ); Where k1 and k2 are adjustment coefficients pre-calibrated according to the desert grassland soil type; The calculated S val Limited to 15-32% of field capacity, T val Limited to the range of 15~35℃: If S val If the water content is below 15% of field capacity, then use 15% of field capacity; if the water content is above 32% of field capacity, then use 32% of field capacity. If T... val If the temperature is below the lower limit of soil temperature by 15℃, then take 15℃; if the temperature is above the upper limit of soil temperature by 35℃, then take 35℃. The target range for soil moisture regulation this week has been finalized. target For [S] val -ΔS, S val +ΔS], target range for soil temperature regulation T target For [T] val -ΔT, T val +ΔT], where ΔS and ΔT are preset absolute values ​​of allowable fluctuation deviation, ΔS represents the allowable percentage fluctuation relative to field capacity, and is set to 5%; ΔT is set to 2℃; (d) Maintain soil moisture at S through irrigation, drainage, or mulching measures. target Within the range, maintain the soil temperature at T target Within the range; (e) Repeat steps a through d weekly, according to the latest V. current Value dynamically adjusted S target and T target This leads to feedback-based continuous management until key soil indicators reach the expected targets. The target substance is the substance corresponding to the total petroleum hydrocarbon content index in the soil determined in step one, and / or the substance corresponding to the heavy metal content index. The adjustment coefficients k1 and k2 are empirical values ​​pre-calibrated based on the soil texture type of desert grassland; For sandy soils, the calibration value of k1 ranges from 0.8 to 1.5, and the calibration value of k2 ranges from 0.3 to 0.

8. For loamy soils, the calibration value of k1 ranges from 1.0 to 2.0, and the calibration value of k2 ranges from 0.5 to 1.

2. For clay soils, the calibration value of k1 ranges from 1.2 to 2.5, and the calibration value of k2 ranges from 0.8 to 1.

5.

2. The method for applying microbial compound fertilizer to desert grasslands as described in claim 1, characterized in that, In step two, suitable functional and nutritional microbial strains are selected based on the physicochemical analysis results. The specific selection method is as follows: For the total petroleum hydrocarbon content index in soil, at least one functional microbial strain is selected from Pseudomonas, Nocardia, Sphingomonas and Bacillus. For heavy metal content indicators, at least one functional microbial strain should be selected from Bacillus, Aspergillus, Yeast and Penicillium. To determine the microbial community diversity index, at least one vegetative microbial strain was selected from Aspergillus niger, Bacillus mycosis fungoides, Bacillus subtilis, and nitrogen-fixing bacteria. The ratio of viable cells between the nutritional microbial strains and the total number of selected functional microbial strains is 1:1 to 1:

5.

3. The method for applying microbial compound fertilizer to desert grasslands as described in claim 2, characterized in that, The selection of functional microbial strains based on the total petroleum hydrocarbon content in the soil determined in step one is as follows: when the total petroleum hydrocarbon content in the soil is below 200 mg / kg, Bacillus subtilis is selected; when the total petroleum hydrocarbon content in the soil is in the range of 200-500 mg / kg, Pseudomonas aeruginosa and Bacillus subtilis with a viable count ratio of 1:1 to 1:3 are selected; when the total petroleum hydrocarbon content in the soil is above 500 mg / kg, Pseudomonas aeruginosa, Nocardia, and Sphingosine monocytogenes with a viable count ratio of 1:0.5:0.5 to 1:1:1 are selected. The selection of functional microbial strains based on the heavy metal content index determined in step one is specifically as follows: when the heavy metal content index is below 20 mg / kg, Saccharomyces cerevisiae is selected; when the heavy metal content index is in the range of 20~50 mg / kg, Aspergillus niger and Bacillus mycoides with a live cell count ratio of 1:1~1:2 are selected; when the heavy metal content index is above 50 mg / kg, Aspergillus niger, Saccharomyces cerevisiae, and Penicillium spp. with a live cell count ratio of 1:0.5:0.5~1:1:1 are selected. The selection of nutrient microbial strains based on the microbial community diversity index determined in step one is specifically as follows: when the microbial community diversity index is in the range of 0 to 1.5, Aspergillus niger and Azotobacter brasiliensis with a viable count ratio of 1:1 to 1:3 are selected; when the microbial community diversity index is in the range of 1.5 to 3.0, Bacillus mycoides and Bacillus subtilis with a viable count ratio of 1:1 to 1:2 are selected; when the microbial community diversity index is higher than 3.0, Aspergillus niger, Bacillus mycoides and Bacillus subtilis with a viable count ratio of 1:0.5:0.5 to 1:1:1 are selected.

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