A method for regulating potassium fertilizer supply based on rhizosphere ion migration

Abstract

The present application belongs to the technical field of crop fertilization control, and particularly relates to a potassium fertilizer supply regulation method based on rhizosphere ion migration. First, a benchmark potassium requirement intensity curve of crops is constructed based on historical data to determine a potassium requirement time window and a benchmark potassium supply time point. Real-time collected water state data is analyzed by a water pulse identification criterion library to identify and quantify water pulse events. Subsequently, a multivariate linear regression model of a rhizosphere water pulse correction factor and a potassium ion rhizosphere migration model are used to accurately predict the effective arrival time window of potassium ions under pulse conditions. Finally, the time deviation between the arrival time window and the potassium requirement time window is calculated, and the deviation is used to drive dynamic correction of the benchmark potassium supply time point, thereby achieving precise synchronization of fertilization time and crop potassium requirement absorption and effectively improving potassium fertilizer utilization efficiency.

Description

Technical Field

[0001] This invention belongs to the field of crop fertilization control technology, specifically relating to a method for regulating potassium fertilizer supply based on rhizosphere ion migration. Background Technology

[0002] Potassium is an essential macronutrient for crop growth and development. For quality-oriented crops, the core requirement for potassium ion supply is not simply "meeting yield demands," but rather "precisely matching the potassium demand rhythm during the crop's growth period" to ensure an effective supply of potassium ions during the critical period of crop quality formation.

[0003] In actual field production, potassium ions migrate in the soil mainly through diffusion, a slow process (averaging only a few millimeters per day) that is constrained by factors such as soil adsorption-desorption balance and soil moisture content. In particular, "water pulses" such as rainfall and irrigation are key factors exacerbating potassium supply imbalances. Water pulses can alter the soil environment within minutes to hours, significantly increasing soil moisture content, expanding the rhizosphere water film thickness, and accelerating the diffusion and convective migration of potassium ions.

[0004] Currently, potassium fertilizer regulation strategies are typically based on a "field-scale averaging + experience-dependent" approach. Specific implementation methods include: determining the total application rate based on pre-sowing soil available potassium testing results; allocating fertilizer according to a fixed basal-topdressing ratio (e.g., 6:4 or 5:5); and applying fertilizer based on common crop growth time points (e.g., the first topdressing 30 days after tobacco transplanting) or recommended application rates published by regional agricultural extension departments. This potassium fertilizer regulation strategy is based on three fundamental assumptions: first, the macroscopic detection value of soil available potassium (field-wide sampling) accurately reflects the accessibility of potassium ions in the crop root absorption area (rhizosphere); second, the risks of chloride ion accumulation and excessive soil salinity after fertilization can be mitigated through empirical rules such as "avoiding high-temperature fertilization" and "controlling single application rates"; and third, the potassium requirement rhythm of the same crop at the same growth stage remains consistent across different years and weather conditions, requiring no dynamic adjustment.

[0005] As can be seen from the above specific implementation methods, current potassium fertilizer regulation strategies, which formulate fertilization plans on a daily or weekly basis, cannot respond to the minute- to hourly fluctuations in rhizosphere potassium ion migration under water pulse-driven conditions. Specifically, under water pulse-driven conditions, high soil moisture content accelerates potassium ion migration, causing a mismatch between the time when potassium ions reach the rhizosphere and the crop's potassium requirement time. This results in the same fertilizer dosage and application location exhibiting drastically different effects under different water pulse conditions, leading to significant fluctuations in crop quality. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: A method for regulating potassium fertilizer supply based on rhizosphere ion migration includes the following steps: Based on soil basic index data, crop growth characteristic data and production data of multiple target fields in multiple historical years, a baseline potassium requirement intensity curve of the crop is fitted. The continuous period in the baseline potassium demand intensity curve where the potassium demand intensity is greater than or equal to the potassium demand intensity threshold is defined as the potassium demand time window of the crop. The potassium ion migration days prior to the start time of the potassium demand time window are set as the baseline potassium supply time points; Collect moisture status data for the target field; moisture status data includes: water input and soil moisture content; Construct a moisture pulse recognition criterion library; the moisture pulse recognition criterion library contains criteria for each layer recognition scenario; the criteria are: soil moisture content change ≥ preset ratio, and moisture input ≥ minimum moisture input threshold; Based on the hierarchical identification scenario to which the target field belongs, the corresponding criteria are selected from the moisture pulse identification criterion library; The moisture status data is matched with the criteria. If the match is successful, it is determined that a moisture pulse has occurred in the target field. Under the condition of water pulse, a multiple linear regression model of rhizosphere water pulse correction factor is established based on the water pulse intensity, water pulse duration and water pulse influence depth of the target field. The rhizosphere water pulse correction factor of the target field was calculated based on the multiple linear regression model, and a rhizosphere migration model of potassium ions was established using the rhizosphere water pulse correction factor of the target field. The effective arrival time window of potassium ions in the rhizosphere was calculated using a potassium ion migration model; wherein, the time for potassium ions to arrive in the rhizosphere is denoted as T1, the time for potassium ion concentration to reach its peak is denoted as T2, and the effective arrival time window of potassium ions in the rhizosphere is [T1, T2]. Calculate the time deviation between the effective rhizosphere arrival time window and the crop's potassium requirement time window; The baseline potassium supply time point in the crop baseline potassium supply strategy is corrected based on the time deviation. Add potassium fertilizer to the target fields according to the revised potassium supply timing: Compared with existing technologies, this invention has the following advantages and beneficial effects: By fitting a baseline potassium requirement intensity curve using historical soil, crop, and production data from multiple target fields and defining a potassium requirement time window, a precise quantitative baseline for crop potassium requirement rhythm is first constructed. Based on this, a water pulse identification criterion library containing stratified scenario criteria is built, enabling real-time matching of collected water status data with the criteria to accurately identify water pulse events affecting ion migration. Furthermore, under the condition of water pulse occurrence, a multiple linear regression model of the rhizosphere water pulse correction factor and a rhizosphere migration model of potassium ions are established based on pulse intensity, duration, and depth of influence, thereby scientifically predicting the effective arrival time window of potassium ions under current water disturbance. By calculating the time deviation between this effective arrival time window and the crop's inherent potassium requirement time window, and dynamically correcting the baseline potassium supply time point accordingly, the "supply-demand mismatch" problem caused by changes in potassium ion migration rate due to water pulses such as rainfall or irrigation is effectively solved. This achieves precise synchronization between potassium fertilizer application time and the critical period of crop rhizosphere absorption, significantly improving potassium fertilizer utilization efficiency and crop quality. Attached Figure Description

[0007] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a potassium fertilizer supply regulation method based on rhizosphere ion migration provided in an embodiment of the present invention. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. The embodiments described below are some, but not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0009] In the following description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other embodiments, well-known structures, materials, or methods are not specifically described to avoid obscuring the invention. Unless otherwise specified, the materials, instruments, and reagents used in the following embodiments are commercially available. Unless otherwise specified, the techniques used in the embodiments are conventional methods well known to those skilled in the art.

[0010] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0011] Example 1: A method for regulating potassium fertilizer supply based on rhizosphere ion migration is provided, the core technical route of which is as follows: 1. Establish a benchmark potassium supply time point: Based on basic soil characteristics, crop varieties and growth stages, and historical production data, establish a benchmark potassium supply time point that does not consider water pulse interference, and use the benchmark potassium supply time point as the basis for regulation.

[0012] 2. Dynamic response to water pulses: Real-time water status data of target fields are collected by multi-source sensors. Based on a historically calibrated water pulse identification criterion library, water pulses are identified, including: the type, intensity, duration and depth of influence of the water pulse.

[0013] 3. Predicting rhizosphere migration pathways: Construct a rhizosphere water pulse correction factor library, embed the rhizosphere water pulse correction factors into the rhizosphere potassium ion migration model, and predict the effective arrival time window of rhizosphere potassium ions under water pulse.

[0014] 4. Potassium supply timing correction: Based on the deviation between the effective arrival time window of potassium ions to the rhizosphere and the potassium requirement time window of the crop, the dynamic correction amount of the potassium supply timing is calculated to obtain the target potassium supply timing.

[0015] By constructing the aforementioned potassium fertilizer supply control framework, which includes "baseline potassium supply time point construction, water pulse identification, root potassium ion migration prediction, and potassium supply time point", the problem of "misalignment" is solved, which is caused by the sudden change in soil moisture state at the minute-hour level due to water pulses (rainfall / irrigation), which in turn leads to a sudden change in rhizosphere potassium ion migration efficiency and causes a misalignment between the effective arrival time window of potassium ions and the time window of crop potassium requirement.

[0016] Based on the aforementioned potash fertilizer supply control framework, this embodiment provides a potash fertilizer supply regulation method based on rhizosphere ion migration, including... Figure 1 The following steps are shown: Step 1.1: Collect soil basic index data, crop growth characteristic data and production data of multiple target fields in multiple historical years to build a basic database.

[0017] The purpose of this step is to provide comprehensive and objective data support for constructing a benchmark potassium demand intensity curve and calculating benchmark fertilization parameters, so as to ensure the scientific validity and suitability of the benchmark potassium supply strategy.

[0018] 1. Collect soil baseline data for the target field from multiple historical years. The method for collecting basic soil index data is as follows: Using the "S-shaped sampling method," soil samples were collected from the 0-20cm (topsoil layer) and 20-40cm (sub-topsoil layer) depths within each target field. At least five sampling points were set up for each target field. All collected soil samples were mixed, and the following index data were extracted from the mixed soil samples: (1) Soil texture: The soil type (sand loam, loam, clay) was determined by the content of sand (2-0.05mm), silt (0.05-0.002mm), and clay (<0.002mm) using a laser particle size analyzer. (2) pH value: Detected using potentiometric method (soil-to-water ratio 1:2.5), with an accuracy of ±0.01; (3) Organic matter content: determined by potassium dichromate oxidation-external heating method; (4) Available potassium content: determined by ammonium acetate extraction-flame photometry method; (5) Equivalent adsorption capacity: determined by batch equilibrium adsorption test, expressed as potassium ion adsorption capacity (cmol / kg).

[0019] 2. Collect crop growth characteristic data of the target field in multiple historical years. The method for collecting crop growth characteristic data is as follows: identify the crop variety (such as the cigar tobacco variety "Cuba No. 1"), planting density, target yield and quality indicators (such as single leaf weight ≥8g and total sugar content 18%-22%) of cigar tobacco); record key growth time nodes according to the growth period division standard (such as the transplanting period, the clump stage, the vigorous growth period and the maturity period of cigar tobacco).

[0020] 3. Collect production data from multiple historical years. Production data includes: fertilization records (potassium fertilizer type, amount of potassium fertilizer applied, time of application of potassium fertilizer, location of application of potassium fertilizer), meteorological data (rainfall, accumulated temperature), yield data (yield per unit area), and quality testing data (such as the content of aroma substances in tobacco leaves and combustibility).

[0021] A basic database is constructed using collected soil basic index data, crop growth characteristic data, and historical production data. This database serves as the data foundation for the subsequent construction of a pulse recognition criterion library, a correction factor library, and a fast migration model library. It also provides a data source for parameter calls in each subsequent step.

[0022] Step 1.2: Based on the basic database, fit the baseline potassium requirement intensity curve of the crop.

[0023] The purpose of this step is to establish a "standard reference system" for the potassium requirement rhythm of crops, so as to provide a quantitative basis for subsequent judgment on whether the arrival time of potassium ions is "out of sync".

[0024] The baseline potassium requirement intensity curve refers to the curve showing the change in potassium ion demand intensity per unit time and per unit biomass of crops during the entire growth period under conditions of no water pulse and no other stress. The horizontal axis represents the growth period (represented by "days after transplanting"), and the vertical axis represents the average potassium requirement intensity (unit: mg / (kg・d)).

[0025] Step 1.2.1: Extract the target parameters that do not trigger moisture-free pulses from the basic database.

[0026] The observational data selected from the basic database that meet the three screening criteria of "no extreme weather events, standardized fertilization implementation, and yield and quality meeting standards" are used to extract the following target parameters: The start and end times of each growth stage, the amount of potassium absorbed by the crop, and the average biomass per plant are all measured. The start and end times of each growth stage are calculated based on key growth time points in the crop growth characteristic data. Potassium absorption is calculated as total potassium input multiplied by potassium fertilizer utilization rate, where total potassium input corresponds to the amount of potassium fertilizer applied in the production data. The average biomass per plant refers to the average total dry matter mass of a single plant, corresponding to the yield data in the production data. Furthermore, potassium fertilizer utilization rate refers to the percentage of applied potassium fertilizer absorbed by the crop during the current season, which is affected by fertilizer type, application method, and soil conditions. It can be determined experimentally in advance based on the actual growth characteristics of the target crop and soil type, such as 0.6-0.7 for sandy loam, 0.7-0.8 for loam, and 0.65-0.75 for clay.

[0027] Furthermore, the target parameters also include: target yield, potassium requirement coefficient per unit yield, available potassium supply in the soil, available potassium contribution from organic fertilizer, and potassium fertilizer utilization rate. Among these, the potassium requirement coefficient per unit yield refers to the amount of potassium oxide absorbed from the soil for every 100 kg of economic yield produced by the crop; this can also be determined experimentally in advance based on the actual growth characteristics of the target crop. The available potassium supply in the soil corresponds to the available potassium content in the basic soil index data. The available potassium contribution from organic fertilizer refers to the total amount of potassium ions in the organic fertilizer applied to the soil that can be directly absorbed and utilized by the crop.

[0028] Step 1.2.2: Based on the target parameters, obtain the average potassium requirement intensity of crops in the target field at each growth stage.

[0029] The specific method is as follows: Step 1.2.2.1: Calculate the duration of each growth stage of the crop in each historical year based on the start and end times.

[0030] First, based on the crop's growth pattern, the entire growth period of the crop is divided into multiple growth stages. Taking a single historical year as an example, the duration of each growth stage for cigar tobacco leaves is shown in Table 1 below.

[0031] Table 1 shows the start and end times, duration, and growth characteristics of each growth stage of cigar tobacco leaves in a single historical year. reproductive period Start and end times (after transplanting) Phase duration (days) Core growth characteristics Seedling establishment period 1-15 days 15 Root system failure and slow growth Clustering period 16-35 days 20 The plant grows in clusters, and its potassium requirement begins to rise. Prosperous Long-Term 36-60 days 25 Rapid leaf expansion triggers peak potassium requirement Maturity 61-85 days 25 As leaves mature, potassium requirements decrease. It should be noted that the start and end times and stage durations shown in Table 1 are example data. The start and end times corresponding to each growth stage are determined based on the crop type, the climatic conditions of the cultivation area, and the actual field morphological characteristics of the crop. Specifically, by monitoring the growth characteristics of the crop, the entire growth period of the crop is divided into different growth stages. Under different growth environments, each growth stage of the crop exhibits different growth characteristics. Multiple crops can be randomly selected for observation experiments. The growth characteristics of each crop in each growth stage are identified with reference to industry standards and common industry classification rules. At the same time, the start and end times of each crop in each growth stage are recorded. Furthermore, the stage duration of each crop in each growth stage is calculated based on the start and end times. The average stage duration of all crops in each growth stage is taken as the final stage duration.

[0032] Step 1.2.2.2: Calculate the potassium requirement intensity of crops in each growth stage for each historical year based on the stage duration, crop potassium uptake, and average biomass per crop plant.

[0033] 1. From the selected target parameters, extract two core parameters for each growth stage: crop potassium uptake (kg / hm²) and average biomass per plant.

[0034] Taking a single historical year as shown in step 1.2.2.1 as an example, the core parameters extracted from the target parameters of cigar tobacco leaves are shown in Table 2 below.

[0035] Table 2 shows the potassium uptake and average biomass per plant at each growth stage of cigar tobacco leaves in a single historical year (the data in the table are for illustrative purposes only). reproductive period Potassium uptake by crops (kg / hm²) Average biomass per crop plant (kg / plant) Phase duration (days) Seedling establishment period 2.25 0.0273 15 Clustering period 4.5 0.0727 20 Prosperous Long-Term 6.75 0.1697 25 Maturity 3.0 0.2121 25 2. Calculate the potassium requirement intensity for each growth stage.

[0036] Potassium intensity is defined as "the amount of potassium ions required per unit time and per unit of biomass", and the calculation formula is: Potassium intensity = (Crop potassium uptake × 10) 6 ) ÷ (Average biomass per plant × 16500 × stage duration).

[0037] It should be noted that the unit for crop potassium uptake is kg / hm², multiplied by 10. 6 Converted to mg / hm².

[0038] Taking the peak growing season as an example, assuming there are 16,500 cigar tobacco seedlings in the target field, then based on the data shown in Table 2, the potassium requirement intensity of cigar tobacco leaves can be calculated as: (6.75 × 10⁻⁶) 6 )÷(0.1697×16500×25)=96.4mg / (kg·d).

[0039] The potassium requirements during the seedling establishment period, the seedling formation period, and the growth period can be calculated using the same method.

[0040] Step 1.2.2.3: For each reproductive stage, take the average potassium requirement intensity of multiple historical years to obtain the average potassium requirement intensity of each reproductive stage.

[0041] For example, the average potassium requirement intensity for each reproductive stage in the three historical years was calculated as shown in Table 3 below.

[0042] Table 3 shows the potassium requirement intensity statistics for each reproductive stage over three years: reproductive period Potassium intensity required in the first year (mg / (kg・d)) Potassium intensity required in the second year (mg / (kg・d)) Potassium intensity required in the third year (mg / (kg・d)) Average potassium requirement intensity (mg / (kg・d)) Seedling establishment period 85 92 88 88.3 Clustering period 135 142 138 138.3 Prosperous Long-Term 185 192 188 188.3 Maturity 105 112 108 108.3 Step 1.2.3: Based on the average potassium requirement intensity, fit the baseline potassium requirement intensity curve of the crop throughout the entire growth period.

[0043] The core logic of fitting the baseline potassium demand intensity curve is as follows: regression fitting is performed separately for each growth stage to obtain piecewise functions, and then smoothing is applied to eliminate discontinuities between segments, ultimately forming a continuous and differentiable potassium demand intensity curve. The specific method is as follows: Step 1.2.3.1: Obtain the average potassium requirement of crops in the experimental field at each growth stage, following the method used to obtain the average potassium requirement of crops in the target field at each growth stage.

[0044] The purpose of this step is to supplement the data sample and improve the universality of the potassium demand curve intensity.

[0045] Step 1.2.3.2: Based on the average potassium requirement intensity of the crop in the experimental field at each growth stage, calculate the average potassium requirement intensity of the crop in the target field at the midpoint of each growth stage using the average value method.

[0046] Taking cigar tobacco as an example, based on the data shown in Table 3, the average potassium requirement intensity of crops in the target field at the midpoint of each growth stage is calculated as shown in Table 4 below.

[0047] Table 4 shows the average potassium requirement intensity of crops in the target field at the midpoint of each growth stage: reproductive period Midpoint of the stage (days after transplanting x) <![CDATA[Average potassium requirement intensity y1 (mg / (kg·d)) of the target field]]> <![CDATA[Average potassium requirement intensity y2 (mg / (kg·d)) of experimental fields]]> <![CDATA[Average potassium requirement intensity y at the midpoint moment (y = (y1 + y2) / 2)]]> Seedling establishment period 7.5 88.3 85.6 86.95 Clustering period 25.5 138.3 142.1 140.2 Prosperous Long-Term 48 188.3 190.5 189.4 Maturity 73 108.3 110.2 109.25 Step 1.2.3.3: Use the average potassium requirement intensity of the crop at the midpoint of adjacent growth stages to perform linear interpolation, obtain the average potassium requirement intensity of the crop at the start and end time points of each growth stage in the target field, and establish a potassium requirement intensity database table.

[0048] The specific method is as follows: 1. Data preprocessing and timeline unification (1) Determine the time axis range: Based on the entire growth period of the crop, determine the range of days after transplanting. For example, the entire growth period of cigar tobacco is 85 days (corresponding to Table 1), and the time axis is x=1,2,3…85 days.

[0049] (2) Split the raw data into daily data: The average potassium requirement intensity of crops in the target field at the midpoint of each growth stage is split into each day according to the stage duration.

[0050] For example, if the average potassium requirement during the seedling establishment period (x = 1-15 days) is 86.95 mg / (kg·d), then this average potassium requirement should be assigned as the initial average potassium requirement for each day from day 1 to day 15. Referring to the amplitude method for the seedling establishment period, initial average potassium requirements should be assigned to the rosette stage, vigorous growth stage, and maturity stage respectively.

[0051] Similarly, the average potassium requirement intensity of crops in the experimental field at the midpoint of each growth stage was broken down into daily intervals according to the stage duration.

[0052] 2. For each day: The average potassium requirement intensity in the target field and the average potassium requirement intensity in the experimental field are collected to establish an average potassium requirement intensity array for each day. The mean of the average potassium requirement intensity in the average potassium requirement intensity array is calculated to obtain the average potassium requirement intensity for each day.

[0053] 3. Extract the average potassium requirement intensity at the start, midpoint, and end times of each reproductive stage, and establish a potassium requirement intensity database table. See Table 5 below.

[0054] Table 5 is a database table of potassium demand intensity: reproductive period (Days, average potassium intensity required at start time mg / (kg・d)) (Daily average potassium intensity required at midpoint mg / (kg・d)) (Days, average potassium intensity required at termination time mg / (kg・d)) Seedling establishment period （1,82） （7.5,86.95） （15,90） Clustering period （16,110） （25.5,140.2） （35,140） Prosperous Long-Term （36,165） （48,189.4） （60,170） Maturity （61,145） （73,109.25） （85,105） Step 1.2.3.4: Based on the potassium requirement intensity database table, establish a fitting function for the potassium requirement intensity of crops in the target field at each growth stage.

[0055] Based on the reproductive period, the entire reproductive period is divided into 4 fitting intervals, and a fitting function is established for each fitting interval.

[0056] 1. Seedling establishment period (x = 1-15 days) The potassium requirement intensity of cigar tobacco leaves in the slow-growth stage exhibits a slow, linear increase, and a linear function y= can be chosen. a1x+ b 1 is used as the fitting function during the seedling establishment period.

[0057] Based on the data shown in Table 5 above, the following example illustrates the points: Input data points: (1,82), (7.5,86.95), (15,90); Fitting tools: Python's scikit-learn library or SPSS's linear regression function; Output: Obtain the coefficients a 1 = 0.51, b 1 = 79.5, and the function is y = 0.51x + 79.5.

[0058] 2. Clustering period (x=16-35 days) The potassium requirement intensity of cigar tobacco leaves during the rosette stage shows a rapid increase, and a quadratic polynomial function y= can be selected. a 2x 2 + b 2x+ c 2 is used as the fitting function for the crowning stage.

[0059] Based on the data shown in Table 5 above, the following example illustrates the points: Input data points: (16,110), (25.5,140.2), (35,140); Fitting tools: Python's scikit-learn library or SPSS's linear regression function; Output: Obtain the coefficients a 2 = 0.075, b 2 = 5.3 c 2 = 44.8, the function is y = 0.075x 2 +5.3x+44.8.

[0060] 3. Peak season (x=36-60 days) The potassium requirement intensity of cigar tobacco leaves during the peak growing season shows an initial increase followed by a decrease, and a quadratic polynomial function y= can be chosen. a 3x 2 + b 3x+ c 3 serves as the fitting function for the peak period.

[0061] Based on the data shown in Table 5 above, the following example illustrates the points: Input data points: (61,145), (73,109.25), (85,105); Fitting tools: Python's scikit-learn library or SPSS's linear regression function; Output: Obtain the coefficients a3 = -0.058 b 3 = 17.6 c 3 = -315.2, the function is y = -0.058x 2 +17.6x-315.2.

[0062] 4. Maturity period (x = 61-85 days) The potassium requirement intensity of mature cigar tobacco leaves shows a continuous decline, and an exponential decay function y= can be selected. a 4 e -b4x + c 4 serves as the fitting function during the peak period.

[0063] Based on the data shown in Table 5 above, the following example illustrates the points: Input data points: (36,165), (48,189.4), (60,170); Fitting tools: Python's scikit-learn library or SPSS's linear regression function; Output: Obtain the coefficients a 4 = 118.5 b 4 = 0.014 c 4 = 81.3, the function is y = 118.5 e -0.014x +81.3.

[0064] Step 1.2.3.5: Smooth the boundaries between adjacent reproductive stages.

[0065] After piecewise fitting, numerical discontinuities or abrupt slope changes may occur at the intersection of adjacent reproductive stages. It is necessary to modify the fitting by constraining boundary conditions to achieve continuous and differentiable curves.

[0066] (1) Core constraints At the stage dividing points (x=15, 35, 60 days), two requirements must be met: first, the function values ​​must be equal, that is, the predicted value of the end point of the previous stage = the predicted value of the start point of the next stage; second, the derivatives must be equal, that is, the slope of the tangent line at the end point of the previous stage = the slope of the tangent line at the start point of the next stage.

[0067] (2) Corrected example (taking the dividing point x=35 days as an example) Before correction: The predicted value of the clustering period function at x=35 is 140.1, and the predicted value of the vigorous growth period function at x=35 is 132.5, with a difference of 7.6, indicating a breakpoint; Constraint correction: Readjust the coefficients of the two functions based on the condition that "y values ​​and derivatives are equal when x=35"; Corrected: The clustering period function is adjusted to y = 0.072x 2+5.35x + 44.2; the long-term function is adjusted to y = -0.06x. 2 +17.8x-320.5; When x=35, the y-value of both segments of the function is 139.8, and the derivative is 10.7, thus eliminating the breakpoint.

[0068] Following the method shown in the above correction example, the same correction was performed on the dividing point x=15 and 60 days respectively, and finally a continuous and smooth potassium demand intensity curve was obtained.

[0069] Step 1.3: Define the continuous period in the baseline potassium requirement intensity curve where the potassium requirement intensity is ≥ the potassium requirement intensity threshold as the potassium requirement time window of the crop, and mark the start and end times of the potassium requirement time window.

[0070] Potassium demand threshold = peak potassium demand × preset ratio (e.g., 70%). The potassium demand time window is the core reference benchmark for subsequent calculation of time deviation.

[0071] It should be noted that, according to the definition of potassium demand intensity mentioned above, the peak potassium demand intensity is the maximum amount of potassium absorbed per unit time during the crop's growth period, corresponding to the physiological stage where the crop's demand for potassium ions is most urgent and its absorption efficiency is highest. The time period corresponding to 70% × the peak potassium demand intensity is defined as the potassium demand time window W. k The core logic is to accurately pinpoint the "critical range for efficient potassium uptake" in crops. If only the "peak moment" is used as the potassium supply node, it is easily affected by the field environment (such as water fluctuations and delayed soil potassium supply), resulting in a mismatch between potassium ion supply and the actual needs of crops. The 70% × potassium requirement peak corresponds to the "range before and after the peak," forming a time window and improving the fault tolerance of potassium supply operations. Furthermore, the intensity of potassium uptake by crops follows a "bell-shaped curve" before and after the peak. The 70% × potassium requirement peak is the critical line for potassium uptake efficiency to go from rapid increase to slow decrease. When it is above 70% × potassium requirement peak, the potassium uptake efficiency is still maintained above 70% of the peak, which belongs to the "efficient potassium uptake range." When it is below 70%, the potassium uptake efficiency is significantly reduced, and the cost-effectiveness of potassium supply decreases.

[0072] Step 1.4: Set the multiple potassium ion migration days before the start time of the potassium demand time window as the baseline potassium supply time points.

[0073] The baseline potassium supply time point is set to several potassium ion migration days (e.g., 3-5 days) before the start time of the potassium demand time window to ensure that potassium ions can reach the rhizosphere when the potassium demand time window opens; if there are multiple potassium demand intensity peaks in the baseline potassium demand intensity curve, multiple baseline potassium supply time points are set in chronological order.

[0074] The baseline potassium supply time point of the crop is used as the initial input parameter for subsequent correction calculations, and all subsequent corrections are dynamically adjusted based on this baseline.

[0075] Step 2.1: Collect the current moisture status data of the target field.

[0076] The purpose of this step is to comprehensively capture short-term water changes that may trigger abrupt changes in rhizosphere potassium ion migration, providing a data foundation for subsequent identification of water pulses.

[0077] Moisture status data is the core input for identifying moisture pulses. It is necessary to eliminate data differences through multi-source data acquisition and standardization processing to provide a unified and objective data source for subsequent pulse identification.

[0078] Moisture status data includes: the time of water input, the amount of water input (rainfall data and / or irrigation data) at each time of water input, and the soil moisture content at each time of water input.

[0079] (1) Rainfall data Rainfall data includes both forecast and measured rainfall data. Forecast rainfall data is obtained from national meteorological platform API interfaces (such as China Weather Network and Meteorological Data Center) for the target field, providing forecasts for the next 6-24 hours, including forecast rainfall amount, probability of rainfall, and duration of rainfall. Measured rainfall data is obtained by installing tipping bucket rain gauges (0.2mm accuracy) in the field to record the start time, duration, and cumulative rainfall in real time, with a data collection frequency of 15 minutes per measurement.

[0080] (2) Irrigation data Irrigation data includes irrigation plan records and irrigation log records. Irrigation plan records are pre-entered irrigation plans, including planned irrigation time, irrigation method (drip irrigation / sprinkler irrigation / flood irrigation), designed irrigation volume, and irrigation area. Irrigation log records are real-time records of actual irrigation execution, including actual irrigation start / end time, actual irrigation volume (read by a flow meter), and irrigation uniformity (sampled and tested for soil moisture content variation coefficient).

[0081] (3) Soil moisture content data Three monitoring points were set up in the field using a diagonal method. At each monitoring point, soil moisture sensors (frequency domain reflectance method, FDR, accuracy ±1%) were installed at depths of 10cm, 20cm, and 40cm. The sensor data was collected every 10 minutes and uploaded to the data platform in real time via a wireless transmission module (LoRa / NB-IoT). Step 2.2: Standardize the moisture status data.

[0082] The purpose of this step is to transform water status data from different sources and in different formats into standardized fields, eliminating the differences in the sources and formats of rainfall data, irrigation data, and soil moisture content data, and enabling unified data access and analysis.

[0083] The specific method is as follows: Step 2.2.1: Convert rainfall data, irrigation data, and soil moisture content data into standardized fields.

[0084] The standardized field definitions and conversion rules are as follows: (1) Rainfall forecast field The rainfall forecast field is represented as R_fore(H), where H is the forecast duration (in hours), and the field value is the cumulative forecast rainfall (in mm) for the next H hours. For example, "R_fore(12)=25mm" means that the forecast rainfall for the next 12 hours is 25mm.

[0085] (2) Rainfall measurement fields The measured rainfall field is represented as R_obs(τ1), where τ1 is the duration of rainfall (in hours), and the field value is the cumulative measured rainfall (in mm) within the τ1 time period. For example, "R_obs(6)=30mm" means that the measured rainfall in the past 6 hours was 30mm.

[0086] (3) Irrigation field The irrigation field is represented as Irr(τ2), where τ2 is the irrigation duration (in hours), and the field value is the cumulative actual irrigation volume (in mm) within the time period. For example, "Irr(2) = 40 mm" means that the actual irrigation volume in the past 2 hours was 40 mm.

[0087] (4) Soil moisture content change field The soil moisture content change field is represented as ΔW(z,Δt), where z is the soil depth (unit: cm), Δt is the time interval (unit: hours), and the field value is the change in soil moisture content at depth z over time Δt (unit: %). The calculation formula is ΔW(z,Δt)=W(z,t)-W(z,t-Δt), where W(z,t) is the soil moisture content at depth z at time t.

[0088] Step 2.2.2: Standardize the format of fields.

[0089] All standardized fields are stored in a structured format of "value + unit", timestamps are uniformly in UTC+8 time zone, and data precision is retained to one decimal place.

[0090] The standardized fields can be directly used as the basis for moisture pulse identification.

[0091] Step 3.1: Construct a moisture pulse recognition criterion library.

[0092] The purpose of this step is to transform moisture pulse identification from the "subjective experience judgment" used in existing technologies to "objective rule judgment based on data calibration," thereby improving the accuracy and reproducibility of identification.

[0093] A water pulse refers to a rapid and significant periodic increase in soil moisture content within a short period of time, which directly drives the dissolution and migration of readily available nutrients in the soil (such as fast-supplying potassium), affecting the absorption efficiency of crop roots.

[0094] Moisture pulses can be triggered by a single concentrated rainfall event, short-term snowmelt, or short-term irrigation operations (sprinkler irrigation, drip irrigation, etc.).

[0095] The characteristics of a water pulse are: the process of water entering the soil is short-lived, and the soil moisture content rises in a stepwise manner; the increase in moisture content is usually ≥5%, and water will infiltrate into the deeper soil layers, forming a distinct moist front; after the water pulse occurs, the soil moisture will gradually decrease with evaporation and crop absorption until the next pulse occurs. The whole process presents a pulse-like fluctuation of "rapid rise - slow fall", which quickly breaks the original soil moisture balance and affects the processes of crop root water absorption and nutrient migration.

[0096] In normal water replenishment scenarios (such as continuous light rain or long-term flooding), soil moisture rises slowly and steadily without obvious pulse peaks. The method described in this embodiment identifies moisture pulses to determine whether an event has occurred in the target field that disrupts the original soil moisture balance and affects crop root water absorption and nutrient migration, which is a prerequisite for dynamic correction.

[0097] By establishing a moisture pulse criterion library based on historical data, we can achieve quantitative identification of the type, intensity, duration, and depth of influence of moisture pulses, thus avoiding the bias of subjective experience judgment.

[0098] The moisture pulse identification criterion database is a database built by layering soil type, slope position and irrigation method based on historical data of "moisture input, soil moisture content response and fertilization effect". This database is used to store criteria for identifying moisture pulses in different scenarios.

[0099] Specifically, the following steps are included: Step 3.1.1: Construct multiple hierarchical identification scenarios for moisture pulses based on soil type, slope position, and irrigation method.

[0100] The soil types include sandy loam, loam, and clay; the slope types include uphill, middle, and downhill; and the irrigation methods include drip irrigation, sprinkler irrigation, and flood irrigation. A total of 27 layered recognition scenes are generated through these combinations.

[0101] Step 3.1.2: Collect valid historical data for each layer recognition scenario.

[0102] Valid historical data includes: water input data (rainfall / irrigation amount, duration), soil moisture content response data (moisture content changes at different depths), and rhizosphere potassium ion migration response data after fertilization.

[0103] Step 3.1.3: Extract the minimum threshold of moisture input that triggers the moisture pulse from the valid historical data of each layer recognition scenario.

[0104] The minimum threshold for triggering a moisture pulse is the critical value that distinguishes between effective moisture input that can trigger a moisture pulse and ineffective moisture input that cannot trigger a moisture pulse. Moisture input below this threshold (such as a small amount of dew or light rain) cannot effectively trigger a moisture pulse.

[0105] Based on the aforementioned characteristics of moisture pulses, this embodiment uses "soil moisture content change ≥ 5%" as a prerequisite for determining an effective moisture pulse trigger. It filters out all moisture inputs that meet the condition of soil moisture content change ≥ 5% from valid historical data, and further finds the minimum value among the filtered moisture inputs, which is the minimum threshold for triggering a moisture pulse.

[0106] For example, in the scenario of sandy loam soil + downhill + drip irrigation, when the change in soil moisture content ΔW(20cm,24h) ≥ 5% and Irr(24h) ≥ 30mm (minimum threshold for water input), it is determined that a water pulse has occurred in the target field; in the scenario of clay soil + uphill + flood irrigation, when the change in soil moisture content ΔW(20cm,24h) ≥ 5% and R_obs(24h) ≥ 20mm (minimum threshold for water input), it is determined that a water pulse has occurred in the target field.

[0107] Step 3.1.4: Using the change in soil moisture content and the minimum threshold of water input as criteria, the results are stored in a structured database according to the hierarchical recognition scenarios to obtain a moisture pulse recognition criterion library.

[0108] Step 3.1.5: Use the quantile method to divide all moisture inputs that meet the criteria in the historical valid data into multiple numerical intervals, and assign a corresponding pulse intensity value to each numerical interval; add the corresponding pulse intensity value of each numerical interval to the moisture pulse recognition criterion library.

[0109] Water pulse intensity refers to the pulsed water supply per unit area of ​​soil. It is used to quantify the water requirements of crops at different growth stages and directly determines the soil pore moisture level and potassium ion diffusion rate. Different intensities of water pulses correspond to the water requirements of crops at different stages. For example, weak pulses are suitable for the water requirements during the seedling establishment period, while strong pulses are suitable for the water requirements during the vigorous growth period, enabling on-demand water supply.

[0110] The quantile method is used to extract the dividing boundaries of moisture pulse intensity, establishing multiple numerical intervals. Specifically, using the 25%, 50%, and 75% quantiles, all moisture input data in the valid historical data that meet the criteria are divided into four numerical intervals: 0-25% quantile, 25%-50% quantile, 50%-75% quantile, and 75%-100% quantile. Moisture input in the 0-25% quantile interval is defined as the first moisture pulse intensity, moisture input in the 25%-50% quantile interval as the second moisture pulse intensity, moisture input in the 50%-75% quantile interval as the third moisture pulse intensity, and moisture input in the 75%-100% quantile interval as the fourth moisture pulse intensity. The first moisture pulse intensity is assigned a value 'a' (e.g., a=1), the second moisture pulse intensity is assigned a value 'b' (e.g., b=2), the third moisture pulse intensity is assigned a value 'c' (e.g., c=1), and the fourth moisture pulse intensity is assigned a value 'd' (e.g., d=1). Step 3.2: Based on the hierarchical identification scenario to which the target field belongs, select the corresponding criteria from the moisture pulse identification criterion library.

[0111] Step 3.3: Match the moisture status data with the criteria.

[0112] If the change in soil moisture content is ≥5% and the amount of water input is ≥ the minimum threshold for water input, then the target field is determined to have experienced a water pulse.

[0113] Step 3.4: Under the condition that a moisture pulse has occurred, quantify and output the moisture pulse intensity, moisture pulse duration and moisture pulse influence depth of the target field.

[0114] Step 3.4.1: Match the numerical range to which the water input belongs, and use the pulse intensity value corresponding to the numerical range as the water pulse intensity of the target field.

[0115] For example, in a scenario of sandy loam soil, downhill slope, and drip irrigation, if the soil moisture content change ΔW(20cm,24h) of the target field is ≥5%, and the irrigation Irr(24h) of the target field is 30mm, a water pulse is determined to have occurred in the target field through criterion matching. After numerical interval matching, the irrigation Irr(24h) of the target field is found to be within the 0-25th percentile range, so the output water pulse intensity value for the target field is 1.

[0116] Step 3.4.2: The duration during which the change in soil moisture content is greater than or equal to a preset ratio is taken as the duration of the moisture pulse in the target field.

[0117] Step 3.4.3: Establish a relationship model between soil moisture content response depth and water input through regression analysis, and obtain the soil moisture content response depth corresponding to water input based on the relationship model.

[0118] The depth of influence of water pulses accurately pinpoints the soil layer range affected by water. This depth determines the optimal application location of potash fertilizer. By establishing a functional relationship between input quantity and depth of influence, water distribution under different water inputs can be predicted.

[0119] Regression analysis was performed on historical data to determine the relationship between soil moisture content response depth and water input. Specifically, with water input as the independent variable and soil moisture content response depth as the dependent variable, a scatter plot of the independent and dependent variables was plotted to determine whether the relationship between the variables was linear or nonlinear. A linear regression model or a bivariate polynomial regression model was selected to fit the relationship between soil moisture content response depth and water input, establishing a model of the relationship between the two. The water input from the water state data was then substituted into the model to calculate the soil moisture content response depth.

[0120] Step 4.1: Based on the water pulse intensity, water pulse duration, and water pulse influence depth of the target field, establish a multiple linear regression model for the rhizosphere water pulse correction factor.

[0121] The purpose of this step is to establish a multiple linear regression model of the rhizosphere water pulse correction factor based on objective experimental data, calculate the rhizosphere water pulse correction factor under different stratified identification scenarios based on the multiple linear regression model, and quantify the impact of water pulse on potassium ion migration efficiency.

[0122] The rhizosphere water pulse correction factor is a core parameter for quantifying the impact of water pulses on potassium ion migration efficiency. Factors influencing the rhizosphere water pulse correction factor include: soil type, initial soil moisture content, water pulse intensity, and water pulse duration.

[0123] Specifically, the following steps are included: Step 4.1.1: Using the isotope tracking method, the ratio of potassium ion migration efficiency in each experimental scenario to that in the no-pulse scenario was determined to obtain the initial correction factor for rhizosphere water pulse in each experimental scenario.

[0124] The purpose of this step is to obtain basic data through field control trials. Three replicate plots are set up for each experimental scenario (soil type × initial moisture content × moisture pulse intensity × moisture pulse duration), and the "isotope tracing method" (e.g., ...) is used. 86 The migration rate of potassium ions was determined by Rb tracer, and the ratio of the migration efficiency of potassium ions under different experimental scenarios to the migration rate of potassium ions under no-pulse scenarios was calculated as an initial value correction factor.

[0125] 1. Division of Experimental Scenarios Experimental scenarios were set up by combining image factors of four rhizosphere water pulse correction factors: soil type, initial soil moisture content (e.g., 10%), water pulse intensity, and water pulse duration.

[0126] Multiple replicate experimental plots were set up for each experimental scenario in order to reduce random errors in the field and ensure the reliability and statistical significance of the experimental data. Finally, the average value of the three plots was taken as the valid data for that scenario.

[0127] A blank control plot without moisture pulse was set up, with all other conditions being exactly the same as the experimental plot, to compare and analyze the specific effects of moisture pulse on potassium ion migration.

[0128] 2. Isotope tracing method for precise quantification of migration rate (1) Technical principles Rubidium has similar chemical properties to potassium and can simultaneously reflect the migration pattern of potassium ions in soil; therefore, this embodiment uses... 86 Rb (rubidium-86) isotope tracer for potassium ions. Equal amounts of potassium ions were added to the soil of each experimental plot and the control plot. 86 Rb markers, tracking 86 The movement trajectory and speed of Rb markers in the soil.

[0129] (2) Measurement indicators Soil samples from different soil layers are collected regularly, and the concentrations of these samples are measured using specialized instruments. 86 The concentration of Rb is used to calculate the potassium ion migration rate, which is the distance or mass of potassium ions that move in the soil per unit time.

[0130] 3. Calculate the initial value correction factor (1) Calculate the migration efficiency ratio Taking any experimental plot as an example: the ratio of potassium ion migration efficiency = potassium ion migration rate in the experimental scenario ÷ potassium ion migration rate in the blank control scenario. This ratio directly reflects the degree to which the moisture pulse promotes or inhibits potassium ion migration. A ratio > 1 indicates that the moisture pulse promotes potassium ion migration, and the larger the ratio, the more significant the promoting effect.

[0131] (2) Calculate the initial value correction factor The migration efficiency ratio calculated above is directly used as the initial correction factor for the rhizosphere water pulse, providing basic data for subsequent fitting of the multiple linear regression model.

[0132] Step 4.1.2: Establish a multiple linear regression model of the rhizosphere water pulse correction factor and multiple influencing factors.

[0133] The purpose of this step is to construct a multiple linear regression model using sufficient experimental data, establish a quantitative functional relationship between the rhizosphere water pulse correction factor and various influencing factors, and revise the multiple linear regression model by combining historical data, thereby improving the accuracy and practicality of the calculation results of the rhizosphere water pulse correction factor.

[0134] 1. Data Collection Multiple sets (≥300 sets) of valid data were collected from different experimental scenarios. Each set of valid data included: an initial correction factor and the quantified values ​​of four influencing factors. The quantified values ​​of the four influencing factors were: soil type, initial moisture content, moisture pulse intensity, and moisture pulse duration.

[0135] It should be noted that soil type, as an independent variable, needs to be quantified as a soil texture coefficient. Differences in pore structure and water and fertilizer retention capacity among different textures (sandy loam / loam / clay loam) significantly affect water infiltration rate and potassium ion migration pathways. The soil type coefficient is a quantification of qualitative factors; for example, sandy loam is assigned a value of 0.8, loam 1.0, and clay loam 1.2. The soil type coefficients for different soil textures can be determined based on the correlation between core soil physicochemical properties and potassium migration efficiency through standardized field experiments and data normalization. Specifically: The essence of soil texture coefficient is to quantify the influence of soil pore structure and clay content on water infiltration and potassium ion migration. The core logic is: the higher the clay content, the smaller the soil pores, the slower the water infiltration, the greater the resistance to potassium ion migration, and the higher the coefficient value; taking loam as the baseline (coefficient 1.0), the ratio of potassium migration efficiency of other soil types to loam is the corresponding coefficient.

[0136] (1) Three types of soil were selected: sandy loam, loam and clay loam. The same initial water content (60% field capacity), the same reference potassium source (available potassium in soil) and the same water pulse intensity were set. Three replicate plots were set for each soil type to determine the effective migration rate of potassium ions in the soil.

[0137] (2) Calculate the potassium ion migration rate. This is achieved using isotope tracing (…). 86 The average migration rates (example data) of potassium ions in the three soil types were determined by Rb: v1 = 0.937 cm / h in sandy loam, v2 = 0.75 cm / h in loam, and v3 = 0.625 cm / h in clay loam.

[0138] (3) Normalization of potassium ion migration rate. Based on the potassium ion migration rate v2=0.75cm / h in loam, after normalization, the soil type coefficients are r1=0.75 / 0.937=0.8 for sandy loam, r2=0.75 / 0.75=1.0 for loam, and r3=0.75 / 0.625=1.2 for clay loam.

[0139] 2. Model building and fitting The multiple linear regression method was used to input the transfer efficiency ratio and effective value for each experimental scenario into the multiple linear regression model to calculate the parameters of the multiple linear regression model.

[0140] Multiple linear regression model: Rhizosphere water pulse correction factor = A × soil type coefficient + B × initial water content + C × water pulse intensity + D × water pulse duration + E; A, B, C, D, and E are all parameters of the multiple linear regression model.

[0141] Step 5.1: Calculate the rhizosphere water pulse correction factor for the target field based on the multiple linear regression model, and use the rhizosphere water pulse correction factor for the target field to establish a rhizosphere migration model of potassium ions.

[0142] The rhizosphere migration model of potassium ions includes the time it takes for potassium ions to reach the rhizosphere and the time it takes for potassium ion concentration to reach its peak.

[0143] The time it takes for potassium ions to reach the rhizosphere is T1 = T base ×Fw; where T base This represents the basic arrival time of potassium ions migrating from the potassium fertilizer application site to the rhizosphere under pulse-free conditions; Fw is the rhizosphere water pulse correction factor.

[0144] The time for potassium ion concentration to reach its peak is T2 = T1 × (1 + Fw).

[0145] Furthermore, T base It is based on a fundamental database, using basic soil physicochemical properties and baseline potassium source conditions as variables. Data was obtained through multiple sets of field experiments, and then fitted using regression analysis. Details are as follows: 1. Variable setting Three or more typical soil types (sandy loam, loam, and clay loam) were selected as core variables.

[0146] Three initial water content gradients were set for each soil type (e.g., 40%, 60%, and 80% field capacity).

[0147] All treatments used a baseline potassium source, without applying water pulses or additional potassium fertilizer, to eliminate interfering factors.

[0148] 2. Replication and Measurement Each treatment was performed in three replicates to ensure data reliability. Rhizosphere soil samples were collected periodically to measure changes in potassium concentration and record the time to reach the effective uptake concentration, denoted as the measured value T. Simultaneously, the potassium release per unit time was calculated and denoted as the measured value K.

[0149] 3. Determine the fitted variables Soil clay content and initial moisture content were selected as independent variables for the fitting, as these are the core soil factors affecting potassium migration and release. The measured values ​​of T and K under the corresponding treatments were used as dependent variables.

[0150] 4. Construct the fitting formula T base The fitting formula is: T base =f(x1,x2)=p1×x1+p2×x2+p3; Where x1 is the soil content, x2 is the initial water content gradient, and p1, p2, and p3 are the fitting coefficients.

[0151] Using multiple sets of experimental data, the fitting coefficients were determined using the least squares method. The basal arrival time T of potassium ions migrating from the potassium fertilizer application site to the rhizosphere under water-free pulsed conditions was analyzed. base For example, 10 sets of experimental data were selected (as shown in Table 6 below), and the fitting coefficients p1, p2, and p3 were solved using the least squares method.

[0152] Table 6 is a statistical table of measured values ​​for clay loam content, initial water content gradient, and foundation arrival time (example data): Serial Number <![CDATA[x1 (% clay loam content)]]> <![CDATA[x2 (Initial water content gradient%)]]> <![CDATA[T base Measured value (h) 1 12 40 12.5 2 12 60 10.2 3 12 80 8.1 4 18 40 15.3 5 18 60 12.8 6 18 80 9.7 7 24 40 18.6 8 24 60 15.4 9 24 80 11.3 10 30 40 21.2 Substitute the experimental data shown in Table 6 into T. base The fitting formula was used to calculate p1=0.52, p2=-0.08, p3=9.6, and finally T was obtained. base Fitting formula (example): T base =0.52x1-0.08x2+9.6.

[0153] 5. Calculate T base The fitting results under the condition of 60% field capacity of loam soil were used as T. base The final benchmark value.

[0154] Step 5.2: Calculate the effective arrival time window of the rhizosphere using the rhizosphere migration model.

[0155] The purpose of this step is to rapidly predict the effective arrival time window of potassium ions under moisture pulse conditions, so as to provide a quantitative basis for corrective decision-making.

[0156] For example: When the experimental scenario involves a clay loam soil content of 15% and an initial moisture content of 50%, substitute T... base The fitting formula is used to calculate: T base =0.52×15-0.08×50+9.6=7.8-4+9.6=13.4h.

[0157] Step 6.1: Calculate the time deviation between the effective rhizosphere arrival time window and the crop's potassium requirement time window.

[0158] Time deviation includes: time deviation amount ΔT w The relationship between the time window position and the time window position.

[0159] Time deviation △T w It refers to the difference between the effective duration of a moisture pulse and the duration of moisture action under conditions without a moisture pulse, reflecting the influence of the duration of the moisture pulse.

[0160] Step 6.1.1: Calculate the length of the overlap between the effective rhizosphere arrival time window and the crop's potassium requirement time window.

[0161] Overlapping time period length △T overlap =End time of overlapping period -Start time of overlapping period

[0162] Step 6.1.2: Calculate the time deviation of the root overlap period.

[0163] Time deviation △T w = Potassium ion migration time × (1 - length of rhizosphere overlap period ÷ crop potassium requirement time window W) k The length of the water pulse. Potassium ion migration time refers to the time required for potassium ions in the soil to migrate from the fertilization point to the rhizosphere microdomain of the crop after a water pulse application and reach an effective concentration that can be efficiently absorbed by the roots. The baseline migration time of potassium ions in different soil types can be determined through field experiments.

[0164] Step 6.1.3: Determine the positional relationship of the time windows.

[0165] If the effective root zone arrival time window [T1, T2] is entirely within the crop's potassium requirement time window W k Previously (i.e., T2) <W k If the starting time is determined, then potassium ions are considered to have arrived at the rhizosphere ahead of schedule. If the effective root zone arrival time window [T1, T2] is generally within the crop's potassium requirement time window W k After that (i.e., T1>W) k If the termination time is determined, then potassium ions are considered to have arrived at the rhizosphere with a lag. If the time T2 when the potassium ion concentration reaches its peak falls within the potassium requirement time window W of the crop. kThe start time and the potassium requirement window of the crop W k Between the termination times (i.e. W) k Start time < T2 <W k If the end time is not specified, it is determined that the effective arrival time window of the rhizosphere is misaligned with the potassium requirement time window of the crop.

[0166] If the time T1 for potassium ions to reach the rhizosphere falls within the potassium requirement time window W of the crop... k The start time and the potassium requirement window of the crop W k Between the termination times (i.e. W) k Start time < T1 <W k If the end time is not specified, it is determined that the effective arrival time window of the rhizosphere is misaligned with the potassium requirement time window of the crop.

[0167] Step 6.2: Correct the baseline potassium supply time point in the crop baseline potassium supply strategy based on the time deviation.

[0168] include: Step 6.2.1: Obtain the time-point correction amount of potassium ions based on the time deviation, moisture pulse intensity, and soil type.

[0169] Correction calculation is the core link of dynamic control. It is driven by the "deviation between the arrival time window and the key potassium demand window" to achieve dynamic adjustment of the potassium supply time.

[0170] Specifically, the following steps are included: Step 6.2.1.1: Establish a multiple nonlinear regression model with the time-point correction amount as the dependent variable and the time deviation amount, water pulse intensity and soil type as independent variables.

[0171] The purpose of this step is to describe how water pulses alter the time from potassium release to crop uptake by the crop through the synergistic effect of key influencing factors (time deviation, water pulse intensity, and soil type).

[0172] The time-point correction amount ΔT refers to the actual time T for potassium ions to reach the rhizosphere after the water pulse is triggered. 实际 Compared to the waterless pulse condition, the basal arrival time T of potassium ions migrating from the potassium fertilizer application site to the rhizosphere is... base The change in ΔT and T. 实际 -T base If the time-point correction amount ΔT < 0, it means that the water pulse shortens the actual time for potassium ions to reach the rhizosphere; if the time-point correction amount ΔT > 0, it means that the water pulse prolongs the actual time for potassium ions to reach the rhizosphere.

[0173] Based on the above definitions of time deviation, moisture pulse intensity, soil type, and time-point correction amount, it can be concluded that: (1) The greater the intensity of the water pulse, the higher the soil moisture content, the better the pore connectivity, the smaller the resistance to potassium ion diffusion, the faster the migration speed, and the more the time point correction amount is biased towards the negative value.

[0174] (2) The larger the time deviation, the longer the duration of the water pulse, and the more time potassium ions have to complete the desorption and migration from the solid phase to the liquid phase, further shortening the response time.

[0175] (3) Different soil types respond differently to pulses. For example, sandy loam has large pores and rapid water infiltration, resulting in high pulse efficiency. Under the same water pulse and event deviation adjustment, the time point correction amount is more biased towards negative values. Clay loam has small pores and slow water infiltration, making it easy for water pulses to cause surface water accumulation. Under the same conditions, the time point correction amount is less biased towards negative values.

[0176] Based on the influence of water pulse intensity, time deviation, and soil type on the point-in-time correction, it can be seen that the point-in-time correction has a nonlinear relationship with water pulse intensity, time deviation, and soil type. Therefore, the multivariate nonlinear regression model established with time deviation, water pulse intensity, and soil type as independent variables can be expressed as: △T=k1×△T w ×ln(I)+k2×r+k3. Where I is the water pulse intensity, r is the soil type coefficient, and k1, k2, and k3 are all fitting coefficients.

[0177] Similarly, multiple sets of experimental data including soil texture coefficient, water pulse intensity, time deviation and time point correction can be collected by referring to the field experiments in steps 4.1 and 5.1 (as shown in Table 7 below). The fitting coefficient of the multivariate nonlinear regression model can be calculated using the experimental data to obtain the final multivariate nonlinear regression model.

[0178] Table 7 is a statistical table of experimental data (example data) for soil texture coefficient, moisture pulse intensity, time deviation, and time-point correction. Soil type Soil type coefficient r Moisture pulse intensity I <![CDATA[Time deviation ΔT w > Time-point correction amount △T Sandy loam soil 0.8 15 2 -1.2 Sandy loam soil 0.8 15 4 -1.8 Sandy loam soil 0.8 30 2 -2.5 Sandy loam soil 0.8 30 4 -3.2 loam 1.0 15 2 -0.8 loam 1.0 15 4 -1.3 loam 1.0 30 2 -1.8 clay loam soil 1.2 1.2 2 -0.3 clay loam soil 1.2 15 4 -0.6 In Table 7, a negative △T indicates that the water pulse shortens the potassium response time; the soil texture coefficient is a quantitative assignment of soil type, with sandy loam having a larger porosity and clay loam having a larger porosity.

[0179] Substituting the data shown in Table 7 into the multivariate nonlinear regression model, and performing nonlinear regression fitting using the least squares method, the fitting coefficients are obtained as k1=-0.15, k2=0.5, and k3=-0.2. The final expression for the multivariate nonlinear regression model is: ΔT=-0.15ΔT w ×ln(I)+0.5r-0.2.

[0180] Step 6.2.1.2: Calculate the time-point correction amount using a multivariate nonlinear regression model.

[0181] Step 6.2.2: Based on the direction of time deviation, use the time point correction amount to correct the benchmark potassium supply time point in the crop benchmark potassium supply strategy.

[0182] If potassium ions arrive at the rhizosphere prematurely, or if a potassium time window is required... k Start time < T1 <W k The termination time needs to be delayed, and the corrected potassium supply time point T = T0 + ΔT; T0 is the baseline potassium supply time point.

[0183] If potassium ions arrive at the root zone or W ions are delayed k Start time < T2 <W k The termination time needs to be advanced to the potassium supply time point. The corrected potassium supply time point is T=T0-△T.

[0184] Step 7: Add potassium fertilizer to the target field according to the revised potassium supply time.

[0185] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0186] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0187] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0188] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the scope of the invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness and purpose of the invention, should still fall within the scope of the disclosed technical content. Furthermore, terms such as "upper," "lower," "left," "right," and "middle" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

Claims

1. A method for regulating potassium fertilizer supply based on rhizosphere ion migration, characterized in that, Includes the following steps: Based on soil basic index data, crop growth characteristic data and production data of multiple target fields in multiple historical years, a baseline potassium requirement intensity curve of the crop is fitted. The continuous period in the baseline potassium demand intensity curve where the potassium demand intensity is greater than or equal to the potassium demand intensity threshold is defined as the potassium demand time window of the crop. The potassium ion migration days prior to the start time of the potassium demand time window are set as the baseline potassium supply time points; Collect moisture status data for the target field; moisture status data includes: water input and soil moisture content; Construct a moisture pulse recognition criterion library; the moisture pulse recognition criterion library contains criteria for each layer recognition scenario; the criteria are: soil moisture content change ≥ preset ratio, and moisture input ≥ minimum moisture input threshold; Based on the hierarchical identification scenario to which the target field belongs, the corresponding criteria are selected from the moisture pulse identification criterion library; The moisture status data is matched with the criteria. If the match is successful, it is determined that a moisture pulse has occurred in the target field. Under the condition of water pulse, a multiple linear regression model of rhizosphere water pulse correction factor is established based on the water pulse intensity, water pulse duration and water pulse influence depth of the target field. The rhizosphere water pulse correction factor of the target field was calculated based on the multiple linear regression model, and a rhizosphere migration model of potassium ions was established using the rhizosphere water pulse correction factor of the target field. The effective arrival time window of potassium ions in the rhizosphere was calculated using a potassium ion migration model; wherein, the time for potassium ions to arrive in the rhizosphere is denoted as T1, the time for potassium ion concentration to reach its peak is denoted as T2, and the effective arrival time window of potassium ions in the rhizosphere is [T1, T2]. Calculate the time deviation between the effective arrival time window of potassium ions in the rhizosphere and the potassium requirement time window of the crop; The baseline potassium supply time point in the crop baseline potassium supply strategy is corrected based on the time deviation. Potassium fertilizer should be added to the target fields according to the revised potassium supply time.

2. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 1, characterized in that, The baseline potassium requirement curve for crops was fitted, including: Extract target parameters from the basic database that do not trigger moisture-free pulses; Based on the target parameters, obtain the average potassium requirement intensity of crops in the target field at each growth stage; Based on the average potassium requirement intensity, a baseline potassium requirement intensity curve for crops throughout their entire growth period is fitted.

3. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 2, characterized in that, The target parameters include: the start and end times of each growth stage, the amount of potassium absorbed by the crop, and the average biomass of a single crop plant; To obtain the average potassium requirement intensity of crops in the target field at each growth stage, the following steps are included: Based on the start and end times, calculate the duration of each growth stage of the crop in each historical year; Based on the duration of each growth stage, the amount of potassium absorbed by the crop, and the average biomass per plant, the potassium requirement intensity of the crop at each growth stage in each historical year was calculated; Potassium requirement intensity = (Crop potassium absorption × 10) 6 ) ÷ (average crop biomass × stage duration); For each reproductive stage, the average potassium requirement intensity of multiple historical years is taken to obtain the average potassium requirement intensity of each reproductive stage.

4. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 3, characterized in that, Fitting the baseline potassium requirement intensity curve of the crop throughout its entire growth period includes the following steps: The average potassium requirement of crops in the experimental field at each growth stage was obtained using the same method as that used to obtain the average potassium requirement of crops in the target field at each growth stage. Based on the average potassium requirement intensity of crops in the experimental field at each growth stage, the average potassium requirement intensity of crops in the target field at the midpoint of each growth stage is calculated by the average value method. By using the average potassium requirement intensity of crops at the midpoint of adjacent growth stages, linear interpolation is performed to obtain the average potassium requirement intensity of crops in the target field at the start and end times of each growth stage, and a potassium requirement intensity database table is established. Based on the potassium requirement intensity database table, a fitting function for the potassium requirement intensity of crops in the target field at each growth stage is established.

5. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 1, characterized in that, The following steps are included in constructing a moisture pulse identification criterion library: Multiple hierarchical identification scenarios for moisture pulses are constructed based on soil type, slope position, and irrigation method; Collect valid historical data for each hierarchical recognition scenario; Extract the minimum threshold of water input that triggers the water pulse from the valid historical data in each hierarchical identification scenario; Using the change in soil moisture content and the minimum threshold of water input as criteria, the moisture pulse recognition criteria library is obtained by storing the scene in a structured database according to the hierarchical recognition. The quantile method is used to divide all moisture inputs that meet the criteria in the historical valid data into multiple numerical intervals, and a corresponding pulse intensity value is assigned to each numerical interval; the pulse intensity value assigned to each numerical interval is added to the moisture pulse recognition criterion library.

6. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 5, characterized in that, The quantitative output of the moisture pulse intensity, moisture pulse duration, and moisture pulse influence depth of the target field includes the following steps: Determine the numerical range to which the water input in the water status data belongs, and use the pulse intensity value corresponding to the numerical range as the water pulse intensity of the target field. The duration of soil moisture content change ≥ a preset ratio is taken as the water pulse duration of the target field. A model of the relationship between soil moisture content response depth and water input was established by regression analysis, and the soil moisture content response depth corresponding to water input was obtained based on the model.

7. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 1, characterized in that, Establish a multiple linear regression model for the rhizosphere water pulse correction factor, including the following steps: Isotope tracking was used to determine the ratio of potassium ion migration efficiency in each experimental scenario to that in the no-pulse scenario, thus obtaining the initial correction factor for rhizosphere water pulse in each experimental scenario. The experimental scenario also included multiple influencing factors of the rhizosphere water pulse correction factor. These factors included a combination of soil type, initial soil moisture content, water pulse intensity, and water pulse duration. The initial correction factor was the ratio of potassium ion migration efficiency. The ratio of potassium ion migration efficiency = potassium ion migration rate in the experimental scenario ÷ potassium ion migration rate in the blank control scenario. A multiple linear regression model was established to compare the rhizosphere water pulse correction factor with multiple influencing factors.

8. The method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 1, characterized in that, The rhizosphere migration model of potassium ions includes: the time for potassium ions to reach the rhizosphere and the time for potassium ion concentration to reach its peak. The time it takes for potassium ions to reach the rhizosphere is T1 = T base ×Fw; where T base This represents the basal arrival time of potassium ions migrating from the potassium fertilizer application site to the rhizosphere under pulse-free conditions; Fw is the rhizosphere water pulse correction factor. The time for potassium ion concentration to reach its peak is T2 = T1 × (1 + Fw).

9. A method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 1, characterized in that, Time deviation includes: the amount of time deviation and the positional relationship of the time window; Calculating the time deviation between the effective arrival time window of potassium ions in the rhizosphere and the potassium requirement time window of the crop includes the following steps: Calculating the overlap period length between the effective arrival time window of potassium ions in the rhizosphere and the potassium requirement time window of the crop; overlap period length = end time of overlap period - start time of overlap period; Calculating the time deviation based on the overlap period length; time deviation = migration time of potassium ions × (1 - overlap period length ÷ length of the potassium requirement time window Wk of the crop); Determining the positional relationship of the time windows, including: If the effective arrival time window of potassium ions in the rhizosphere is entirely before the potassium requirement time window of the crop, it is determined that potassium ions arrive in the rhizosphere ahead of schedule; If the effective arrival time window of potassium ions in the rhizosphere is entirely after the potassium requirement time window of the crop, it is determined that potassium ions arrive in the rhizosphere late; If the time when the potassium ion concentration reaches its peak is between the start time and the end time of the potassium requirement time window of the crop, it is determined that the effective arrival time window of potassium ions in the rhizosphere partially overlaps with the potassium requirement time window of the crop. If the time when potassium ions arrive in the rhizosphere falls between the start and end times of the crop's potassium requirement time window, it is determined that the effective arrival time window of potassium ions in the rhizosphere partially overlaps with the crop's potassium requirement time window.

10. A method for regulating potassium fertilizer supply based on rhizosphere ion migration according to claim 9, characterized in that, The correction of the baseline potassium supply time point in the crop baseline potassium supply strategy based on time deviation includes the following steps: The time-point correction amount of potassium ions is obtained based on the time deviation, moisture pulse intensity, and soil type. Based on the direction of time deviation, the benchmark potassium supply time point in the crop benchmark potassium supply strategy is corrected using the time point correction amount. Obtaining the time-point correction value of potassium ions includes the following steps: establishing a multiple nonlinear regression model with the time-point correction value as the dependent variable and the time deviation, water pulse intensity, and soil type as independent variables; the multiple nonlinear regression model is expressed as: ΔT = k1 × ΔT w ×ln(I)+k2×r+k3; where I is the water pulse intensity, r is the soil type coefficient, and k1, k2, and k3 are all fitting coefficients; the time-point correction amount is calculated using a multivariate nonlinear regression model; The steps to revise the baseline potassium supply time point in the crop baseline potassium supply strategy include: If potassium ions arrive at the rhizosphere ahead of time, or if the start time of the potassium demand time window is less than the time when potassium ions arrive at the rhizosphere but less than the end time of the potassium demand time window, then the potassium supply time point will be delayed. The delayed potassium supply time point T = T0 + ΔT, where T0 is the baseline potassium supply time point. If potassium ions arrive at the rhizosphere late, or if the start time of the potassium demand time window is less than the time when the potassium ion concentration reaches its peak but less than the end time of the potassium demand time window, then the potassium supply time point will be advanced. The advanced potassium supply time point T = T0 - ΔT.

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