Ecological and high-quality planting methods based on Polygonatum odoratum cultivation

Abstract

This invention relates to the field of agricultural planting technology, specifically to an ecological and high-quality planting method based on Polygonatum odoratum cultivation. The method includes: establishing data collection points at planting sites to capture native environmental data such as historical soil pH values, annual organic matter fluctuations, and seasonal groundwater level fluctuations. This data is processed to generate a soil pH evolution trend map, and combined with soil particle composition calculations to obtain a soil fertility decline model. A correlation analysis between these two models is used to determine the key parameter set for Polygonatum odoratum seed stem pretreatment, which is then verified through a germplasm resource bank to ensure the parameters are within the variety's tolerance range. Simultaneously, based on groundwater level data, a soil moisture profile is simulated to predict the alternating wet and dry periods and map them to the growing season, forming a planting schedule with water stress early warning. This method can dynamically predict changes in soil fertility and provide precisely tailored variety-specific parameters for seed stem treatment.

Description

Technical Field

[0001] This invention relates to the field of agricultural planting technology, and in particular to an ecological and high-quality planting method based on the cultivation of Solomon's Seal. Background Technology

[0002] Current techniques for cultivating Solomon's Seal (Polygonatum odoratum) largely rely on static soil testing, such as measuring current soil pH, organic matter content, and moisture content. Cultivation plans are generally based on these instantaneous test data or regional average empirical parameters. For the pretreatment of seed stems, fixed pesticide concentrations and soaking times are typically used, or adjustments are made according to general growth regulation principles for broad crop categories. This approach, based on static snapshots and general experience, struggles to address the dynamic changes in the soil environment and cannot precisely match the biological characteristics of specific Solomon's Seal germplasm.

[0003] Existing technical solutions have shortcomings. Static soil data cannot reflect the natural decline in fertility over the planting cycle, leading to a lack of foresight in the formulation of base fertilizer and topdressing programs, and easily resulting in problems such as excessive fertility in the early stages or insufficient supply in the later stages. Commonly used seed stem pretreatment parameters ignore the genetic differences in tolerance among different Solomon's seal germplasm resources, and parameter settings may exceed the adaptation range of some germplasm sources, causing decreased seed stem vigor or phytotoxicity. This invention aims to solve how to predict the dynamic changes in soil fertility during the planting cycle, and how to make the key operational parameters of seed stem pretreatment simultaneously adaptable to the environmental conditions of specific plots and the inherent genetic characteristics of the selected Solomon's seal cultivar. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an ecological and high-quality cultivation method based on Solomon's seal cultivation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an ecological and high-quality planting method based on Polygonatum odoratum cultivation, comprising:

[0006] Based on the pre-set Solomon's Seal planting plots, multiple environmental data collection points are established to capture complete native environmental background data. The native environmental background data includes historical values ​​of pH at each level of the soil profile, annual records of organic matter fluctuations, and seasonal fluctuation data of groundwater levels.

[0007] The historical pH values ​​of each layer of the captured soil profile were analyzed over time to generate a trend map of soil pH evolution in the Solomon's Seal planting area.

[0008] By combining the annual fluctuation record of organic matter with the soil particle composition information, a soil fertility decline model for the Solomon's seal planting plot is generated.

[0009] Based on the correlation analysis between the soil pH evolution trend map and the soil fertility decline model, the key parameter set for the pretreatment of Solomon's seal rhizome was determined, and the matching of the key parameter set with germplasm resource bank information was verified, and data items that exceeded the tolerance range of Solomon's seal germplasm were removed.

[0010] Based on the seasonal fluctuation data of groundwater level, soil moisture profile simulation is performed to predict the alternation pattern of wet and dry periods in different soil layers during the planting cycle. The alternation pattern of wet and dry periods is then mapped to the growth stage of Solomon's seal to form a planting schedule with water stress early warning.

[0011] As a further aspect of the present invention, the step of performing time-series variation analysis on the historical pH values ​​of each layer of the captured soil profile to generate a soil pH evolution trend map of the Solomon's seal planting plot includes:

[0012] The soil pH evolution trend map includes the pH dynamics of different soil layers, the time points of potential acidification or alkalization, and the rate curve of pH change.

[0013] The historical pH values ​​of each level of the soil profile from all environmental data collection points are spatially gridded, merged, and time-corrected to form a soil pH data cube under a standard time series.

[0014] From the soil pH data cube, the pH change sequences of the topsoil, subsoil and subsoil layers are separated, and the pH dynamic curves of each soil layer are plotted.

[0015] The trend inflection points of the dynamic acidity and alkalinity curves of each soil layer are identified, and the time points when the acidity and alkalinity change from stable to rapidly changing are recorded.

[0016] Calculate the change in pH value within a set time period before and after the potential acidification or alkalization time point, and generate the rate curve of pH change.

[0017] The dynamics of acidity and alkalinity of different soil layers, the time points of potential acidification or alkalization, and the rate curves of acidity and alkalinity changes are integrated and encoded into a visualized trend map of soil acidity and alkalinity evolution.

[0018] As a further aspect of the present invention, a soil fertility decline model for the Solomon's seal planting plot is generated by combining the annual organic matter fluctuation record with soil particle composition information, including:

[0019] The soil fertility decline model is used to simulate the decline in soil fertility capacity under conditions of no external nutrient input.

[0020] Data cleaning was performed on the annual organic matter fluctuation records to obtain soil organic matter content values ​​measured at specific times each year over several consecutive years.

[0021] Soil samples were obtained from the Solomon's Seal planting site, and the soil particle composition information at each of the environmental data collection points was determined by particle size analysis experiments, including the content ratio of sand, silt and clay.

[0022] An organic matter mineralization rate lookup table based on soil texture type is established. The dominant soil texture category of the Solomon's seal planting plot is determined according to the soil particle composition information, and the corresponding theoretical mineralization coefficient is obtained from the organic matter mineralization rate lookup table.

[0023] The theoretical mineralization coefficient was used to iteratively simulate the decreasing initial organic matter content, and the theoretical soil organic matter content at the end of each future year was calculated.

[0024] The theoretical soil organic matter content is corrected with the measured annual changes in organic matter content to construct a soil fertility decline model that can predict changes in basic soil fertility under different farming patterns.

[0025] As a further aspect of the present invention, soil moisture profile simulation is performed based on the aforementioned groundwater level seasonal fluctuation data to predict the alternation pattern of wet and dry periods in different soil layers during the planting cycle, including:

[0026] The seasonal fluctuation data of groundwater level were coupled with the atmospheric precipitation data and evaporation data of the same period to perform water balance analysis, and the recharge flux and consumption flux of soil moisture in the root zone were calculated.

[0027] Based on the soil moisture movement equation, the replenishment flux and consumption flux of soil moisture in the root zone are used as boundary conditions to simulate the movement process of water in the topsoil, subsoil and subsoil layers.

[0028] Based on the simulated water transport process in the topsoil, subsoil and subsoil layers, the volumetric water content of each soil layer at different time points is extracted to form a soil layer water content time series.

[0029] Set the humidity threshold range for effective water absorption by the Solomon's Seal root system for each soil layer;

[0030] By analyzing the soil moisture content time series, the continuous period when the volumetric moisture content is lower than the humidity threshold range is identified. The continuous period is marked as the dry period of the soil layer, and the remaining periods are marked as the wet period of the soil layer, thereby obtaining the alternation pattern of wet and dry periods of the different soil layers.

[0031] As a further aspect of the present invention, the alternation pattern of wet and dry periods is mapped to the growth stages of Polygonatum odoratum to form a planting schedule with water stress early warning, including:

[0032] Based on the phenological observation data of Solomon's seal, the complete growth cycle of Solomon's seal is divided into the dormancy period, the budding and leaf unfolding period, the stem and leaf growth period, the flowering and fruiting period, and the rhizome enlargement period.

[0033] The predicted alternation patterns of wet and dry periods in the different soil layers are aligned with the complete growth cycle of the Solomon's Seal according to the time coordinate.

[0034] The analysis examines whether the rhizome enlargement period overlaps with the drought period in the soil layer where the main root system is distributed, on the aligned time axis.

[0035] If the root and stem enlargement period overlaps with the drought period in the soil layer where the main root system is distributed, a high-level water stress warning will be marked on the corresponding time period of the planting schedule.

[0036] If the vegetative growth period overlaps with the drought period of the cultivated layer, a medium-level water stress warning is marked on the corresponding time period of the planting schedule, and finally the planting schedule containing multi-level water stress warning information is generated.

[0037] As a further aspect of the present invention, it also includes the step of developing a nutrient management plan based on the planting schedule and the soil fertility decline model:

[0038] From the planting schedule with water stress warning, read the start and end times of each growth stage of Solomon's Seal, as well as the water stress warning level corresponding to each stage;

[0039] From the soil fertility depletion model, query the predicted values ​​of basic nitrogen, phosphorus and potassium supply in the soil at the beginning of Solomon's seal planting and during subsequent key phenological periods;

[0040] Based on the data on the stage absorption characteristics of nitrogen, phosphorus and potassium nutrients of Polygonatum odoratum at different growth stages, the theoretical nutrient requirements of Polygonatum odoratum at each growth stage are calculated.

[0041] Subtract the predicted values ​​of the basic nitrogen, phosphorus and potassium supply in the soil at the same stage from the theoretical nutrient requirements to obtain the nutrient difference that needs to be supplemented from external sources at each growth stage.

[0042] Based on the water stress warning level, adjust the application strategy for the nutrient deficit that needs to be supplemented from external sources. For periods marked with high-level water stress warnings, formulate a nutrient supplementation plan that mainly uses foliar application and supplements it with soil application, and generate a detailed nutrient management plan.

[0043] As a further aspect of the present invention, it also includes the step of planning soil improvement and ecological cover based on the soil pH evolution trend map:

[0044] Query the soil pH evolution trend map to obtain the pH change range of the topsoil and the time nodes of potential acidification or alkalization during the Solomon's seal planting cycle.

[0045] If the pH range of the cultivated soil exceeds the suitable pH range for the high-quality growth of Solomon's seal, then a target time period for soil improvement is determined. The target time period should be earlier than the potential acidification or alkalization time point or adjacent to the planting start period.

[0046] Calculate the amount of acidic or alkaline soil conditioner required per unit area based on the difference between the current pH value and the target pH value.

[0047] In the nutrient management plan, the application of the acidic or alkaline amendment required per unit area of ​​soil is scheduled within the target time period and carried out in conjunction with the application of base fertilizer.

[0048] On the surface of the planting area where soil improvement and base fertilizer application have been completed, an ecological cover layer composed of a mixture of forest litter and crop straw is laid. The thickness of the ecological cover layer is determined based on the simulated evaporation rate of the soil moisture movement equation.

[0049] As a further aspect of the present invention, it also includes the step of establishing an ecological irrigation triggering mechanism based on soil moisture feedback:

[0050] Within the Solomon's Seal planting area, a soil moisture sensor network corresponding to the location of the environmental data collection point is deployed to monitor the volumetric moisture content of the topsoil and subsoil in real time.

[0051] An irrigation trigger threshold is set that is associated with the humidity threshold range for effective water absorption by the roots of the Solomon's seal, and the irrigation trigger threshold is set to be slightly higher than the lower limit of the humidity threshold range.

[0052] The volumetric water content data monitored in real time by the soil moisture sensor network is continuously compared with the irrigation trigger threshold of the corresponding soil layer;

[0053] When the real-time volumetric moisture content of a certain monitoring point is lower than the irrigation trigger threshold corresponding to the soil layer, and the monitoring period is not marked with a high-level water stress warning in the planting schedule, a micro-irrigation instruction for the area around the monitoring point is automatically generated.

[0054] The micro-irrigation command will control the irrigation system to provide localized irrigation by replenishing water to the humidity threshold range without causing deep seepage.

[0055] As a further aspect of the present invention, a soil microenvironment regulation process coupling ecological cover and micro-irrigation is also included:

[0056] When executing the micro-irrigation command for localized irrigation, the water volume, duration, and specific location of each irrigation are recorded simultaneously.

[0057] The study analyzed the difference in the time required for the soil volumetric moisture content to recover to the humidity threshold range after executing the same micro-irrigation command in areas where the ecological cover layer was laid and areas where it was not laid.

[0058] Based on the time difference required for the soil volumetric moisture content to recover to the humidity threshold range, the irrigation trigger threshold in the area where the ecological cover layer is laid is dynamically adjusted to extend the irrigation interval.

[0059] Temperature sensors placed under the ecological cover layer are used to monitor soil temperature changes and ensure that the temperature difference between irrigation water and soil remains within a set range.

[0060] Irrigation is scheduled for early morning or evening to match the temperature change rhythm of the soil microenvironment under the ecological cover layer, forming a water-heat synergistic regulation mode.

[0061] As a further aspect of the present invention, the initial organic matter content is iteratively decreased using the theoretical mineralization coefficient to calculate the theoretical soil organic matter content at the end of each future year, including:

[0062] The soil organic matter content measured at the beginning of the planting year of Solomon's seal is obtained as the initial organic matter content, and the initial organic matter content is input into the starting calculation node of the iterative decreasing simulation.

[0063] In each simulated year, the soil organic matter content at the beginning of the current year is multiplied by the theoretical mineralization coefficient to calculate the annual organic matter mineralization decomposition consumption.

[0064] Subtract the annual organic matter mineralization and decomposition consumption from the soil organic matter content value at the beginning of the current year to obtain the predicted median value of the soil organic matter content at the end of the year.

[0065] Based on the amount of carbon input returned to the soil annually by the root residues and aboveground litter of Solomon's seal, the predicted median value of soil organic matter content at the end of the year is incrementally compensated to generate the theoretical soil organic matter content at the end of the year.

[0066] The theoretical soil organic matter content at the end of the previous year is assigned as the soil organic matter content at the beginning of the next simulated year. The iterative process is repeated to calculate the theoretical soil organic matter content at the end of each future year.

[0067] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0068] A soil fertility decline model is generated by calculating the annual fluctuations of organic matter and soil particle composition information. This operation couples time-series data characterizing dynamic changes in soil fertility with physical structural parameters that determine nutrient retention capacity. Mechanistically, it simulates the continuous process of soil's basic nutrient supply capacity decreasing over time, thus transforming fertilization management from a lagging judgment based on static content indicators to a proactive regulation based on the decline trajectory. Planting operations can then be precisely implemented before the critical nutrient requirement period for *Polygonatum odoratum* based on the model's predicted fertility inflection point.

[0069] The key parameter set for seed stem pretreatment obtained from the analysis was verified for matching with germplasm resource bank information, and out-of-limit items were removed. This operation compared and screened environmental adaptation parameters with genetic databases recording the physiological tolerance thresholds of different germplasm sources. This ensured that the final treatment parameters, such as soaking and disinfection, met the environmental regulation needs of specific plots while being strictly limited within the safe physiological window defined by the genetic background of the target variety. This eliminated the risk of germplasm-specific phytotoxicity or physiological stress caused by excessive environmental optimization parameters, ensuring the safety of seed stem treatment and germination consistency. Attached Figure Description

[0070] Figure 1 This is a flowchart of the ecological high-quality planting method based on Solomon's seal cultivation as described in this invention;

[0071] Figure 2 A flowchart generated from the soil pH evolution trend diagram;

[0072] Figure 3 A flowchart generated for a soil fertility degradation model;

[0073] Figure 4 This is a diagram showing the effect of soil improvement.

[0074] Figure 5 This is a monitoring diagram showing the changes in topsoil volumetric moisture content and irrigation-triggered events in Area 3. Detailed Implementation

[0075] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0076] See Figure 1Based on a pre-defined Solomon's Seal planting site, multiple environmental data collection points were established to capture complete native environmental background data. This native environmental background data included historical pH values ​​at each soil profile level, annual organic matter fluctuation records, and seasonal groundwater level fluctuation data. The captured historical pH values ​​at each soil profile level were analyzed over time to generate a soil pH evolution trend map for the Solomon's Seal planting site. A soil fertility decline model for the Solomon's Seal planting site was generated by combining the annual organic matter fluctuation records with soil particle composition information. Based on the correlation analysis between the soil pH evolution trend map and the soil fertility decline model, a key parameter set for Solomon's Seal seed pretreatment was determined, and the matching accuracy of this key parameter set with germplasm resource bank information was verified, eliminating data items exceeding the tolerance range of the Solomon's Seal germplasm. Based on the seasonal fluctuation data of groundwater level, soil moisture profile simulation is performed to predict the alternation pattern of wet and dry periods in different soil layers during the planting cycle. The alternation pattern of wet and dry periods is then mapped to the growth stage of Solomon's seal to form a planting schedule with water stress early warning.

[0077] See Figure 2 In one embodiment of the present invention, multiple environmental data collection points are established based on a preset Solomon's Seal planting plot. The environmental data collection points capture the historical pH values ​​of each layer of the soil profile. In the example scenario, the Solomon's Seal planting plot is divided into ten environmental data collection points. Each environmental data collection point records the pH values ​​of the topsoil, subsoil and subsoil layers for each quarter over the past five years. The pH historical values ​​of all environmental data collection points are spatially gridded and time-corrected to form a soil pH data cube under a standard time series. The soil pH data cube stores pH data in terms of time, spatial location and soil depth. In some embodiments, the pH change sequences of the topsoil, subsoil, and subsoil layers are separated from the soil pH data cube. The pH change sequences are represented in time series form, and the pH dynamic curves of the topsoil, subsoil, and subsoil layers are plotted respectively. The pH dynamic curves show the pH change trend of each soil layer over time. Trend inflection points are identified for the pH dynamic curves of the topsoil, subsoil, and subsoil layers. Trend inflection point identification is achieved by analyzing the changes in the slope of the curves, and the time nodes when the pH changes from stable to rapidly changing, indicating potential acidification or alkalization, are recorded.

[0078] Optionally, calculate the change in pH value within a set time period before and after the potential acidification or alkalization point, generating a rate curve of pH change. The set time period is six months, and the rate of pH change is calculated using the following formula:

[0079]

[0080] in: Indicates the rate of change in pH. Indicates the time node pH value over time Indicates the time point before pH value over time This represents half the set time period. It can be understood that the rate curve of pH change visualizes how quickly pH changes. In the data comparison example, the pH value of the topsoil layer decreased from 6.0 to 5.4 before and after the potential acidification or alkalization time point. The calculated rate of pH change, v, is negative, reflecting the acidification trend. In some embodiments, the pH dynamics of different soil layers, the time points of potential acidification or alkalization, and the rate curve of pH change are integrated and encoded into a visualized soil pH evolution trend map. The soil pH evolution trend map is presented in a multi-layer chart format, overlaying the pH dynamic curves of the topsoil, subsoil, and subsoil layers, marking the time points of potential acidification or alkalization, and attaching a sub-chart of the rate curve of pH change. The example scenario data comparison shows that the pH dynamic curve of the topsoil layer shows a downward trend, the pH dynamic curve of the subsoil layer fluctuates gently, and the pH dynamic curve of the subsoil layer rises slightly.

[0081] In practical implementation, the construction of the soil pH data cube relies on the spatial distribution and temporal integrity of environmental data collection points. Spatial gridding maps discrete environmental data collection points to a uniform grid, and temporal correction aligns different sampling times to a unified timestamp. It can be understood that trend inflection point identification uses the moving window averaging method to detect curve turning points, identifying the time points when pH transitions from stable to rapidly changing, indicating potential acidification or alkalization. Optionally, the rate curve of pH change is calculated using the central difference method, with Δ set to three months in the formula to balance sensitivity and noise. In the data comparison example, the pH change rate v in the central soil layer is close to zero, indicating dynamic stability of pH. In practical implementation, the soil pH evolution trend map is used to determine the key parameter set for subsequent pretreatment of *Polygonatum odoratum* seed stems. In the example scenario, the soil pH evolution trend map shows that the cultivated layer exhibits potential acidification or alkalization in the second quarter of the third year, with the rate curve of pH change corresponding to a significant negative peak.

[0082] See Figure 3In one embodiment of the present invention, the annual organic matter fluctuation records of the Solomon's Seal planting plots are cleaned. The data cleaning process removes outliers and fills in missing values, obtaining soil organic matter content values ​​measured at specific times each year for several consecutive years. In the example scenario, the specific time is after the Solomon's Seal harvest in autumn each year, obtaining a five-year sequence of soil organic matter content values. Soil samples from the Solomon's Seal planting plots are obtained, and the soil particle composition information at each environmental data collection point is determined through particle size analysis experiments. The particle size analysis experiments use the pipette method or laser diffraction method. The soil particle composition information includes the content ratio of sand, silt, and clay. A data comparison example shows that at environmental data collection point 1, the sand content is 60%, the silt content is 25%, and the clay content is 15%. An organic matter mineralization rate lookup table based on soil texture type is established. This table includes annual theoretical mineralization coefficient values ​​for texture categories such as sandy soil, loam, and clay. The dominant soil texture category of the *Polygonatum odoratum* planting plot is determined based on soil particle composition information. In this example, sand content predominates, classifying the site as sandy loam. The theoretical mineralization coefficient for sandy loam is obtained from the organic matter mineralization rate lookup table. In some embodiments, the theoretical mineralization coefficient is used to perform an iterative decreasing simulation of the initial organic matter content to calculate the theoretical soil organic matter content at the end of each future year. The soil organic matter content measured at the beginning of the *Polygonatum odoratum* planting year is obtained as the initial organic matter content, and this initial organic matter content is input into the starting calculation node of the iterative decreasing simulation.

[0083] It is understandable that the iterative decreasing simulation process is a continuous calculation loop. Within each simulated year, the soil organic matter content at the beginning of the current year is multiplied by the theoretical mineralization coefficient to calculate the annual organic matter mineralization and decomposition consumption. The annual organic matter mineralization and decomposition consumption is then subtracted from the initial soil organic matter content to obtain the predicted median value of the year-end soil organic matter content. Based on the annual carbon input returned to the soil by the root residues and aboveground litter of *Polygonatum odoratum*, the predicted median value of the year-end soil organic matter content is incrementally compensated to generate the theoretical soil organic matter content at the end of the year. The annual carbon input is estimated through field observation of *Polygonatum odoratum* biomass. Optionally, the theoretical soil organic matter content at the end of the year can be assigned as the soil organic matter content at the beginning of the next simulated year, and the iterative process can be repeated to calculate the theoretical soil organic matter content at the end of each future year. In the data comparison example, the initial organic matter content is 20 grams per kilogram, the theoretical mineralization coefficient is 5%, and the estimated annual carbon input is 0.8 grams per kilogram, using the formula:

[0084]

[0085] in: This represents the theoretical soil organic matter content at the end of year n. Indicates the first The soil organic matter content at the end of the year (i.e., the beginning of the nth year). Represents the theoretical mineralization coefficient. The annual carbon input is represented by the theoretical soil organic matter content at the end of the third year, calculated to be 19.05 grams per kilogram. In some embodiments, the theoretical soil organic matter content is compared with the measured annual organic matter fluctuation records to perform model parameter correction. The model parameter correction process compares the differences between the simulated and measured sequences, adjusts the values ​​of the theoretical mineralization coefficient or the annual carbon input, and constructs a soil fertility decline model that can predict changes in basic soil fertility under different farming patterns. Data comparison shows that the average error between the theoretical soil organic matter content sequence output by the adjusted model and the measured annual organic matter fluctuation records is less than 5%.

[0086] In practical implementation, the organic matter mineralization rate lookup table is established based on long-term locational experimental data of soil types in the reference area. The theoretical mineralization coefficient of sandy loam is higher than that of clay loam. It can be understood that the calculation of annual organic matter mineralization decomposition consumption reflects the natural decline of soil organic matter without external input, and the amount of carbon input returned to the soil annually by the root residues of *Polygonatum odoratum* and aboveground litter is a compensating term. Optionally, the number of iterations in the iterative decreasing simulation corresponds to the number of predicted years; the example simulates the theoretical changes in soil organic matter content over the next 5 years. In practical implementation, the model parameter calibration uses the least squares method to optimize the theoretical mineralization coefficient, making the predicted output of the soil fertility decline model closely resemble the measured annual changes in organic matter.

[0087] In one embodiment of the present invention, soil moisture profile simulation is performed based on seasonal fluctuation data of groundwater level captured from the Solomon's seal planting plot. The seasonal fluctuation data of groundwater level includes the groundwater level depth value monitored monthly. In the example scenario, the data collection point No. 2 records that the groundwater level depth is 0.8 meters during the rainy season and 2.5 meters during the dry season. The seasonal fluctuation data of groundwater level is coupled with the atmospheric precipitation data and evaporation data of the same period for water balance analysis. The atmospheric precipitation data comes from the daily rainfall records of the meteorological station, and the evaporation data comes from the observation values ​​of the small evaporation pan. The recharge flux and consumption flux of soil moisture in the root zone are calculated. The recharge flux of soil moisture in the root zone includes atmospheric precipitation infiltration and groundwater capillary rise, and the consumption flux of soil moisture in the root zone includes soil evaporation and Solomon's seal transpiration. It is understandable that, based on the soil moisture movement equation, the recharge and consumption fluxes of soil moisture in the root zone are used as boundary conditions to simulate the movement of water in the topsoil, subsoil, and subsoil layers. The soil moisture movement equation uses the Richards equation to describe unsaturated soil water flow, and the soil profile moisture content distribution is obtained through numerical solution. In some embodiments, based on the simulated water movement process in the topsoil, subsoil, and subsoil layers, the volumetric moisture content of each soil layer at different time points is extracted to form a soil moisture content time series. In the example scenario, the volumetric moisture content data of the topsoil layer for every ten days during the growing season is extracted to constitute the topsoil layer moisture content time series.

[0088] A humidity threshold range for effective water absorption by the roots of *Polygonatum odoratum* was set for each soil layer. This threshold range was obtained by measuring soil moisture characteristic curves through pot experiments. Data comparison examples show that the humidity threshold range for the topsoil layer is 18% to 25% volumetric water content, while the threshold range for the subsoil layer is 20% to 28% volumetric water content. The soil moisture content time series was analyzed to identify periods where the volumetric water content was below the humidity threshold range. These periods were marked as the dry period for the soil layer, and the remaining periods were marked as the wet period. This revealed the alternation pattern of wet and dry periods for different soil layers. Data comparison examples show that the topsoil layer moisture content time series showed a volumetric water content below 18% from early July to mid-August, and this period was marked as the dry period for the topsoil layer. Optionally, the predicted alternation patterns of wet and dry periods in different soil layers are mapped to the growth stages of *Polygonatum odoratum*. Based on phenological observation data, the complete growth cycle of *Polygonatum odoratum* is divided into dormancy, budding and leaf unfolding, stem and leaf growth, flowering and fruiting, and rhizome enlargement. The phenological observation data for *Polygonatum odoratum* comes from three consecutive years of fixed-point observation records. The predicted alternation patterns of wet and dry periods in different soil layers are aligned with the complete growth cycle of *Polygonatum odoratum* according to the time coordinate. The alignment process uses calendar dates as the reference to plot the dry period, wet period, and phenological period of *Polygonatum odoratum* on the same time axis.

[0089] In some embodiments, the analysis examines whether the rhizome enlargement period overlaps with the drought period of the main root distribution soil layer on the aligned time axis. The main root distribution soil layer is determined based on observations of the vertical distribution of the Solomon's seal root system. In the data comparison example, the rhizome enlargement period is from September to October, and the main root distribution soil layer consists of the topsoil and the upper subsoil layer. If the rhizome enlargement period overlaps with the drought period of the main root distribution soil layer, a high-level water stress warning is marked on the corresponding time period of the planting schedule. The example scenario data shows that the upper subsoil layer experiences a 15-day drought period in September, which overlaps with the initial stage of the rhizome enlargement period. Therefore, a high-level water stress warning is marked on the corresponding time period in September of the planting schedule. If the vegetative growth period overlaps with the drought period in the topsoil layer, a medium-level water stress warning is marked on the corresponding time period of the planting schedule. This results in a planting schedule containing multiple levels of water stress warning information. In the example, the vegetative growth period is from May to July, and the topsoil layer experiences a drought period from early July to mid-August. Since these two periods overlap in July, a medium-level water stress warning is marked on the corresponding time period in July of the planting schedule. It can be understood that the water balance coupling analysis uses the following formula:

[0090]

[0091] in: Indicates the change in soil water storage in the root zone. This represents the total flux of soil moisture recharge in the root zone. This represents the total soil moisture consumption flux and the total soil moisture recharge flux in the root zone. The sum of atmospheric precipitation infiltration and groundwater capillary rise, representing the total flux consumed. It is the sum of soil evaporation and Solomon's seal transpiration.

[0092] In practice, atmospheric precipitation infiltration is calculated based on precipitation data and soil infiltration capacity, while groundwater capillary rise is estimated using an empirical model based on groundwater level depth and soil texture. It can be understood that soil evaporation and Solomon's seal transpiration are estimated by splitting potential evapotranspiration data with crop coefficients. Optionally, the lower limit of the humidity threshold range corresponds to the soil volumetric moisture content when Solomon's seal roots begin to have difficulty absorbing water. In practice, the planting schedule is presented in tabular form, with columns including date, Solomon's seal growth stage, soil moisture status at each layer, and water stress warning level markers.

[0093] In one embodiment of the present invention, a nutrient management plan is formulated based on a planting schedule with water stress warning and a soil fertility decline model. The start and end times of each growth stage of *Polygonatum odoratum*, as well as the corresponding water stress warning level for each stage, are read from the planting schedule with water stress warning. In the example scenario, the start time of the rhizome enlargement stage is September 1st, and the end time is October 31st, with a high-level water stress warning marked for this period. The predicted values ​​of basic soil nitrogen, phosphorus, and potassium supply at the start of *Polygonatum odoratum* planting and during subsequent key phenological stages are retrieved from the soil fertility decline model. The soil fertility decline model outputs dynamic predicted values ​​of basic soil nitrogen, phosphorus, and potassium supply, as shown in Table 1.

[0094] Table 1: Prediction of Soil Base Fertilizer Supply During Key Phenological Stages of Polygonatum odoratum

[0095]

[0096] Based on data on the stage-specific absorption characteristics of nitrogen, phosphorus, and potassium nutrients by Polygonatum odoratum at different growth stages, the theoretical nutrient requirements of Polygonatum odoratum at each growth stage were calculated. The stage-specific absorption characteristics data of Polygonatum odoratum were obtained from cultivation experiments of the Polygonatum odoratum variety. The theoretical nutrient requirements were calculated using the formula... ,in This indicates the theoretical nutrient requirement. This indicates the dry matter mass accumulated per unit area of ​​Solomon's Seal during this growth stage. This indicates the average percentage content of nitrogen, phosphorus, or potassium nutrients in the dry matter of *Polygonatum odoratum*. In some embodiments, the theoretical nutrient requirement is subtracted from the predicted values ​​of the soil's basic nitrogen, phosphorus, and potassium supply at the same stage to obtain the nutrient difference that needs to be supplemented from external sources at each growth stage. The calculation method is as follows ,in This indicates the predicted values ​​of soil-based nitrogen, phosphorus, or potassium supply at the same stage. It can be understood that the application strategy for adjusting the nutrient deficit that needs to be supplemented externally is adjusted according to the water stress warning level. For periods marked with high-level water stress warnings, a nutrient supplementation plan is formulated, primarily using foliar application supplemented by soil application, generating a detailed nutrient management plan. In the example, the nitrogen nutrient deficit during the root and stem enlargement period is 2.1 kg per acre. Because this period is marked with a high-level water stress warning, the nutrient management plan arranges for 1.5 kg to be supplemented through foliar spraying of urea solution and 0.6 kg through shallow furrow application.

[0097] Optionally, soil improvement and ecological cover can be planned based on the soil pH evolution trend map. The map can be queried to obtain the pH variation range of the topsoil and the potential acidification or alkalization time points during the *Polygonatum odoratum* planting cycle. The example shows that the pH variation range of the topsoil is from pH 5.4 to 6.1, and the potential acidification or alkalization time point is in April of the third year after planting. If the pH variation range of the topsoil exceeds the suitable pH range for optimal growth of *Polygonatum odoratum*, then the target time period for soil improvement is determined. The target time period should be earlier than the potential acidification or alkalization time point or immediately adjacent to the planting start date. The suitable pH range for optimal growth of *Polygonatum odoratum* is from pH 5.8 to 6.5. In the example, the current pH is 5.4, which is lower than the lower limit of the suitable range; therefore, the target time period for soil improvement is determined to be one week before the planting start date. Based on the difference between the current pH value and the target pH value, the amount of acidic or alkaline amendment required per unit area of ​​soil is calculated. In this example, the target pH value is set to pH 6.0, and based on the soil buffer curve, 80 kg of quicklime is required per acre. In some embodiments, the application of the acidic or alkaline amendment required per unit area of ​​soil is scheduled within the target time period in the nutrient management plan and is carried out in conjunction with the application of base fertilizer. In this example, 80 kg / acre of quicklime and 2000 kg / acre of well-rotted organic fertilizer are plowed into the tillage layer as base fertilizer one week before the start of planting. On the surface of the planting area after soil improvement and base fertilizer application, an ecological cover layer composed of a mixture of forest litter and crop straw is laid. The thickness of the ecological cover layer is determined based on the simulated evaporation rate of the soil moisture movement equation. The simulated evaporation rate data comes from water balance coupling analysis. In this example, based on the average daily simulated evaporation rate of 5 mm during the growing season, the thickness of the ecological cover layer is determined to be 8 cm.

[0098] See Figure 4 The pH regulation process of quicklime in improving acidic soil can be visually presented through a time-series dynamic curve. Specifically, the pH of the unimproved topsoil (red curve) was 5.4 before improvement and remained stable throughout the monitoring period, consistently below the lower limit of the suitable pH range for optimal growth of *Polygonatum odoratum* (5.8–6.5, green background area). After improvement, the pH of the topsoil (green curve) showed a significant and continuous increase after the application of quicklime (80 kg / mu, blue dashed line): the pH rose to 5.7 one week after improvement, to 5.9 one month after improvement, and stabilized at 6.0 three and six months after improvement, fully entering the suitable pH range. This result verifies the neutralizing effect of quicklime as an alkaline amendment on acidic soil. Its pH regulation rate and final stable value both meet the soil microenvironment requirements for *Polygonatum odoratum* cultivation, providing a quantitative basis for the improvement strategy of synergistic application of quicklime and basal fertilizer one week before planting.

[0099] In one embodiment of the present invention, a soil moisture sensor network corresponding to the locations of environmental data collection points is deployed within the Solomon's seal planting plot. The soil moisture sensor network is constructed based on wireless sensor nodes and is used to monitor the volumetric moisture content of the topsoil and subsoil layers in real time. In the example scenario, the Solomon's seal planting plot is divided into five monitoring areas, with the center of each area corresponding to an environmental data collection point. A set of sensors is deployed to monitor the volumetric moisture content of the 0-20 cm topsoil layer and the 20-40 cm subsoil layer, respectively. An irrigation trigger threshold is set that is associated with the moisture threshold range for effective water absorption by the Solomon's seal root system. The irrigation trigger threshold is set slightly higher than the lower limit of the moisture threshold range. In the data comparison example, the moisture threshold range for the topsoil layer is 18% to 25% volumetric moisture content, and the corresponding irrigation trigger threshold is set to 20%; the moisture threshold range for the subsoil layer is 20% to 28% volumetric moisture content, and the corresponding irrigation trigger threshold is set to 22%. The system continuously compares the real-time volumetric moisture content data monitored by the soil moisture sensor network with the irrigation trigger threshold of the corresponding soil layer. This comparison is performed every half hour by the data acquisition unit. When the real-time volumetric moisture content of a monitoring point is lower than the irrigation trigger threshold for the corresponding soil layer, and no high-level water stress warning is marked in the planting schedule for the monitoring period, a micro-irrigation instruction is automatically generated for the area surrounding the monitoring point. In the example, the topsoil sensor in monitoring area 3 reported a volumetric moisture content of 19.5% at noon on a certain day in July. This value is lower than the 20% irrigation trigger threshold, and there is no high-level water stress warning marked in the planting schedule for that day. Therefore, the system generates a micro-irrigation instruction for monitoring area 3. In some embodiments, the micro-irrigation instruction controls the irrigation system to provide localized irrigation with a volume of water sufficient to replenish the moisture threshold without causing deep seepage. The irrigation system is a drip irrigation system, and the volume of water for a single irrigation is determined by the formula:

[0100]

[0101] in: Indicates the amount of water used for a single irrigation. This indicates the area covered by the micro-irrigation command. Indicates the planned depth of the wetting layer. Indicates soil field water holding capacity. This indicates the actual volumetric moisture content of the soil at the moment irrigation is triggered.

[0102] It is understandable that when executing micro-irrigation commands for localized irrigation, the water volume, duration, and specific location of each irrigation are recorded simultaneously. The irrigation water volume is read from the flow meter, the irrigation duration is recorded by the controller, and the specific location is determined by the location number of the trigger sensor. Analysis shows the difference in time required for soil volumetric moisture content to recover to the humidity threshold range after executing the same micro-irrigation command in areas with and without ecological cover. Data comparison examples show that under the same micro-irrigation command, it takes 6 hours for the soil volumetric moisture content in the ecological cover area to recover from 20% to 24%, while it takes 4 hours in the uncovered area, a difference of 2 hours. Optionally, the irrigation trigger threshold in the ecological cover area can be dynamically adjusted based on the time difference required for soil volumetric moisture content to recover to the humidity threshold range, extending the irrigation interval. In the example, for the ecological cover area, the topsoil irrigation trigger threshold is dynamically adjusted from 20% to 19% to extend the time interval between two irrigations. Soil temperature changes are monitored using temperature sensors placed 5 cm deep beneath the ecological cover layer. This ensures that the temperature difference between the irrigation water and soil remains within a set range, which is no more than 5 degrees Celsius. Irrigation is scheduled for early morning or evening to match the temperature variation rhythm of the soil microenvironment beneath the ecological cover layer, forming a hydrothermal synergy regulation mode. In this example, the system automatically generates micro-irrigation commands and executes them after 6 PM on the same day. At this time, the monitored soil temperature beneath the ecological cover layer is 25 degrees Celsius, and the irrigation water temperature is 22 degrees Celsius, with the temperature difference within the set range.

[0103] See Figure 5In the ecological irrigation triggering mechanism based on soil moisture feedback, the dynamic changes in the volumetric moisture content of the topsoil layer in Area 3 and the irrigation decision-making logic can be quantitatively analyzed through this graph. The blue curve in the graph represents the measured moisture content, which exhibits significant diurnal fluctuations during the monitoring period. This is directly related to atmospheric evaporation, crop transpiration, and soil moisture redistribution. The red dashed line represents the preset irrigation trigger threshold (20%), which is set slightly higher than the lower limit of the effective water absorption moisture threshold range for Solomon's seal roots (18%–25%), and is a key condition for initiating micro-irrigation. The red dots mark the irrigation trigger event (19.5%), representing the specific time when the system automatically generates a micro-irrigation command when the real-time moisture content is below the trigger threshold and there is no high-level water stress warning. From a time series perspective, around noon on July 8th, the measured moisture content of the topsoil layer in Area 3 first dropped to 19.5%, below the 20% irrigation trigger threshold, and the planting schedule for that day did not mark a high-level water stress warning, thus triggering a micro-irrigation command for that area. The data intuitively presents the coupling relationship between soil moisture dynamics and irrigation decisions, providing data support for subsequent soil microenvironment regulation that couples ecological cover and micro-irrigation: by comparing the difference in water content recovery time between areas with and without ecological cover under the same micro-irrigation command, the irrigation trigger threshold can be dynamically adjusted to optimize the water and heat synergistic regulation mode.

Claims

1. An ecological and high-quality cultivation method based on Polygonatum odoratum cultivation, characterized in that, The method includes: Based on the pre-set Solomon's Seal planting plots, multiple environmental data collection points are established to capture complete native environmental background data. The native environmental background data includes historical values ​​of pH at each level of the soil profile, annual records of organic matter fluctuations, and seasonal fluctuation data of groundwater levels. The historical pH values ​​of each layer of the captured soil profile were analyzed over time to generate a trend map of soil pH evolution in the Solomon's Seal planting area. By combining the annual fluctuation record of organic matter with the soil particle composition information, a soil fertility decline model for the Solomon's seal planting plot is generated. Based on the correlation analysis between the soil pH evolution trend map and the soil fertility decline model, the key parameter set for the pretreatment of Solomon's seal rhizome was determined, and the matching of the key parameter set with germplasm resource bank information was verified, and data items that exceeded the tolerance range of Solomon's seal germplasm were removed. Based on the seasonal fluctuation data of groundwater level, soil moisture profile simulation is performed to predict the alternation pattern of wet and dry periods in different soil layers during the planting cycle. The alternation pattern of wet and dry periods is then mapped to the growth stage of Solomon's seal to form a planting schedule with water stress early warning.

2. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 1, characterized in that, The step involves performing a time-series variation analysis on the historical pH values ​​of each layer of the captured soil profile to generate a soil pH evolution trend map of the Solomon's seal planting area, including: The soil pH evolution trend map includes the pH dynamics of different soil layers, the time points of potential acidification or alkalization, and the rate curve of pH change. The historical pH values ​​of each level of the soil profile from all environmental data collection points are spatially gridded, merged, and time-corrected to form a soil pH data cube under a standard time series. From the soil pH data cube, the pH change sequences of the topsoil, subsoil and subsoil layers are separated, and the pH dynamic curves of each soil layer are plotted. The trend inflection points of the dynamic acidity and alkalinity curves of each soil layer are identified, and the time points when the acidity and alkalinity change from stable to rapidly changing are recorded. Calculate the change in pH value within a set time period before and after the potential acidification or alkalization time point, and generate the rate curve of pH change. The dynamics of acidity and alkalinity of different soil layers, the time points of potential acidification or alkalization, and the rate curves of acidity and alkalinity changes are integrated and encoded into a visualized trend map of soil acidity and alkalinity evolution.

3. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 1, characterized in that, By combining the annual organic matter fluctuation records with soil particle composition information, a soil fertility decline model for the Solomon's seal planting plot is generated, including: The soil fertility decline model is used to simulate the decline in soil fertility capacity under conditions of no external nutrient input. Data cleaning was performed on the annual organic matter fluctuation records to obtain the soil organic matter content values ​​measured in the corresponding period of each year over several consecutive years. Soil samples were obtained from the Solomon's Seal planting site, and the soil particle composition information at each of the environmental data collection points was determined by particle size analysis experiments, including the content ratio of sand, silt and clay. An organic matter mineralization rate lookup table based on soil texture type is established. The dominant soil texture category of the Solomon's seal planting plot is determined according to the soil particle composition information, and the corresponding theoretical mineralization coefficient is obtained from the organic matter mineralization rate lookup table. The theoretical mineralization coefficient was used to iteratively simulate the decreasing initial organic matter content, and the theoretical soil organic matter content at the end of each future year was calculated. The theoretical soil organic matter content is corrected with the measured annual changes in organic matter content to construct a soil fertility decline model that can predict changes in basic soil fertility under different farming patterns.

4. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 1, characterized in that, Based on the aforementioned groundwater level seasonal fluctuation data, soil moisture profile simulation was conducted to predict the alternation pattern of wet and dry periods in different soil layers during the planting cycle, including: The seasonal fluctuation data of groundwater level were coupled with the atmospheric precipitation data and evaporation data of the same period to perform water balance analysis, and the recharge flux and consumption flux of soil moisture in the root zone were calculated. Based on the soil moisture movement equation, the replenishment flux and consumption flux of soil moisture in the root zone are used as boundary conditions to simulate the movement process of water in the topsoil, subsoil and subsoil layers. Based on the simulated water transport process in the topsoil, subsoil and subsoil, the volumetric water content of each soil layer at different time points was extracted to form a soil moisture content time series. Set the humidity threshold range for effective water absorption by the Solomon's Seal root system for each soil layer; By analyzing the soil moisture content time series, the continuous period when the volumetric moisture content is lower than the humidity threshold range is identified. The continuous period is marked as the dry period of the soil layer, and the remaining periods are marked as the wet period of the soil layer, thereby obtaining the alternation pattern of wet and dry periods of the different soil layers.

5. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 1, characterized in that, The alternation of wet and dry periods is mapped to the growth stages of Polygonatum odoratum to form a planting schedule with water stress early warning, including: Based on the phenological observation data of Solomon's seal, the complete growth cycle of Solomon's seal is divided into the dormancy period, the budding and leaf unfolding period, the stem and leaf growth period, the flowering and fruiting period, and the rhizome enlargement period. The predicted alternation patterns of wet and dry periods in the different soil layers are aligned with the complete growth cycle of the Solomon's Seal according to the time coordinate. The analysis examines whether the rhizome enlargement period overlaps with the drought period in the soil layer where the main root system is distributed, on the aligned time axis. If the root and stem enlargement period overlaps with the drought period in the soil layer where the main root system is distributed, a high-level water stress warning will be marked on the corresponding time period of the planting schedule. If the vegetative growth period overlaps with the drought period of the cultivated layer, a medium-level water stress warning is marked on the corresponding time period of the planting schedule, and finally the planting schedule containing multi-level water stress warning information is generated.

6. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 5, characterized in that, It also includes the step of developing a nutrient management plan based on the planting schedule and the soil fertility decline model: From the planting schedule with water stress warning, read the start and end times of each growth stage of Solomon's Seal, as well as the water stress warning level corresponding to each stage; From the soil fertility depletion model, query the predicted values ​​of basic nitrogen, phosphorus and potassium supply in the soil at the beginning of Solomon's seal planting and during subsequent key phenological periods; Based on the data on the stage absorption characteristics of nitrogen, phosphorus and potassium nutrients of Polygonatum odoratum at different growth stages, the theoretical nutrient requirements of Polygonatum odoratum at each growth stage are calculated. Subtract the predicted values ​​of the basic nitrogen, phosphorus and potassium supply in the soil at the same stage from the theoretical nutrient requirements to obtain the nutrient difference that needs to be supplemented from external sources at each growth stage. Based on the water stress warning level, adjust the application strategy for the nutrient deficit that needs to be supplemented from external sources. For periods marked with high-level water stress warnings, formulate a nutrient supplementation plan that mainly uses foliar application and supplements it with soil application, and generate a detailed nutrient management plan.

7. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 6, characterized in that, It also includes the step of planning soil improvement and ecological cover based on the soil pH evolution trend map: Query the soil pH evolution trend map to obtain the pH change range of the topsoil and the potential acidification or alkalization time points during the Solomon's seal planting cycle. If the pH range of the cultivated soil exceeds the suitable pH range for high-quality growth of Solomon's seal, then the target time period for soil improvement is determined. The target time period should be earlier than the potential acidification or alkalization time point or adjacent to the planting start period. Calculate the amount of acidic or alkaline soil conditioner required per unit area based on the difference between the current pH value and the target pH value. In the nutrient management plan, the application of the acidic or alkaline amendment required per unit area of ​​soil is scheduled within the target time period and carried out in conjunction with the application of base fertilizer. On the surface of the planting area where soil improvement and base fertilizer application have been completed, an ecological cover layer composed of a mixture of forest litter and crop straw is laid. The thickness of the ecological cover layer is determined based on the simulated evaporation rate of the soil moisture movement equation.

8. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 4, characterized in that, It also includes the step of establishing an ecological irrigation triggering mechanism based on soil moisture feedback: Within the Solomon's Seal planting area, a soil moisture sensor network corresponding to the location of the environmental data collection point is deployed to monitor the volumetric moisture content of the topsoil and subsoil in real time. An irrigation trigger threshold is set that is associated with the humidity threshold range for effective water absorption by the roots of the Solomon's seal, and the irrigation trigger threshold is set to be slightly higher than the lower limit of the humidity threshold range. The volumetric water content data monitored in real time by the soil moisture sensor network is continuously compared with the irrigation trigger threshold of the corresponding soil layer; When the real-time volumetric moisture content of a certain monitoring point is lower than the irrigation trigger threshold corresponding to the soil layer, and the monitoring period is not marked with a high-level water stress warning in the planting schedule, a micro-irrigation instruction for the area around the monitoring point is automatically generated. The micro-irrigation command will control the irrigation system to provide localized irrigation by replenishing water to the humidity threshold range without causing deep seepage.

9. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 8, characterized in that, It also includes soil microenvironment regulation processes that couple ecological cover and micro-irrigation: When executing the micro-irrigation command for localized irrigation, the water volume, duration, and specific location of each irrigation are recorded simultaneously. The study analyzed the difference in the time required for the soil volumetric moisture content to recover to the humidity threshold range after executing the same micro-irrigation command in areas with and without ecological cover. Based on the time difference required for the soil volumetric moisture content to recover to the humidity threshold range, the irrigation trigger threshold in the area where the ecological cover layer is laid is dynamically adjusted to extend the irrigation interval. Temperature sensors placed under the ecological cover layer are used to monitor soil temperature changes and ensure that the temperature difference between irrigation water and soil remains within a set range. Irrigation is scheduled for early morning or evening to match the temperature change rhythm of the soil microenvironment under the ecological cover layer, forming a water-heat synergistic regulation mode.

10. The ecological high-quality planting method based on Polygonatum odoratum cultivation according to claim 3, characterized in that, Using the theoretical mineralization coefficient, an iterative decreasing simulation of the initial organic matter content was performed to calculate the theoretical soil organic matter content at the end of each future year, including: The soil organic matter content measured at the beginning of the planting year of Solomon's seal is obtained as the initial organic matter content, and the initial organic matter content is input into the starting calculation node of the iterative decreasing simulation. In each simulated year, the soil organic matter content at the beginning of the current year is multiplied by the theoretical mineralization coefficient to calculate the annual organic matter mineralization decomposition consumption. Subtract the annual organic matter mineralization and decomposition consumption from the soil organic matter content value at the beginning of the current year to obtain the predicted median value of the soil organic matter content at the end of the year. Based on the amount of carbon input returned to the soil annually by the root residues and aboveground litter of Solomon's seal, the predicted median value of soil organic matter content at the end of the year is incrementally compensated to generate the theoretical soil organic matter content at the end of the year. The theoretical soil organic matter content at the end of the previous year is assigned as the soil organic matter content at the beginning of the next simulated year. The iterative process is repeated to calculate the theoretical soil organic matter content at the end of each future year.

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