A fertilization method to achieve increased yield of soapberry plants for multiple economic goals

Abstract

This invention discloses a fertilization method for increasing the yield of soapberry trees by achieving multiple economic objectives. It relates to the field of soapberry cultivation technology. During the budding, flowering, fruit-setting, and ripening stages of soapberry trees, the total nitrogen, total phosphorus, and total potassium contents of the soil within the canopy area of ​​each tree are measured. After harvesting, multiple economic indicators for each tree are measured, and the tree diameter is recorded. A mixed-effects multinomial regression method is used to construct the functional relationships between the total nitrogen, total phosphorus, and total potassium contents of the soil and the various economic indicators at each stage, as well as the functional relationships among the total nitrogen, total phosphorus, and total potassium contents. Nonlinear optimization iterative machine learning is performed on each yield indicator function and the relationship function of total nitrogen, total phosphorus, and total potassium for each stage to obtain the optimal solution for each yield indicator. Based on the optimal solution, the optimal solution for the comprehensive traits at each stage is calculated. Then, at each stage, the amount and ratio of fertilizer are determined based on the total nitrogen, total phosphorus, and total potassium contents of the soil at the proposed fertilization location.

Description

Technical Field

[0001] This invention relates to the field of soapberry cultivation technology, and more specifically to a fertilization method for achieving increased production of soapberry to achieve multiple economic goals. Background Technology

[0002] The soapberry tree is an important economic tree species. Its fruit, the soapberry pod, has unique medicinal value and is widely used in industries such as manufacturing, food processing, and environmental protection. Currently, in agricultural production, how to accurately calculate the optimal fertilizer application rate based on soil conditions and tree characteristics to achieve the optimization of multiple economic objectives—including pods, seeds, shells, polysaccharides, stigmatic acid, and saponins—is a long-standing and urgent problem to be solved.

[0003] Therefore, how to perform multi-objective fertilization of soapberry based on mixed-effect models and optimization algorithms is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention is hereby proposed.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A multi-objective fertilization method for Gleditsia sinensis based on a mixed-effects model and optimization algorithm, the process of which includes:

[0007] (1) During the budding, flowering, fruit setting and ripening stages of the soapberry tree, the soil total nitrogen, total phosphorus and total potassium content within the canopy area of ​​each tree were measured. After harvesting, multiple economic indicators of each tree were measured and the diameter of the fruit tree was recorded. The mixed-effects multinomial regression method was used to construct the functional relationship between the soil total nitrogen, total phosphorus and total potassium content and various economic indicators at each stage, as well as the functional relationship between the soil total nitrogen, total phosphorus and total potassium content.

[0008] (2) Using optimization algorithms, nonlinear optimization iterative machine learning is performed on each yield index function and the relationship function of total nitrogen, total phosphorus and total potassium in soil for each period to obtain the optimal solution for each yield index.

[0009] (3) Based on the optimal solutions of total nitrogen, total phosphorus and total potassium in the soil for each economic indicator in each period, calculate the optimal solution of the comprehensive characteristics in each period, and use it as the soil testing and fertilization standard for each period. Then, in each period, determine the amount and ratio of fertilizer based on the total nitrogen, total phosphorus and total potassium content of the soil at the proposed fertilization site.

[0010] Preferably, in step (1), multiple economic indicators include the yield of pods per plant, seed yield, shell yield, polysaccharide gum yield, spiculin yield, and saponin yield.

[0011] Preferably, in step (1), the functional relationship between the total nitrogen, total phosphorus, and total potassium content of the soil and various economic indicators for each period is constructed as follows: taking the total nitrogen, total phosphorus, and total potassium content for each period as a fixed effect, the diameter at rootstock as a covariate, and the individual plant as a random effect, the model is set as follows:

[0012] Y = β0 + β1X + β2X 2 +D+b+ε

[0013] Where Y is the yield indicator; X is the total nitrogen, total phosphorus, and total potassium content of the soil; β0, β1, and β2 are fixed-effects parameters; D is diameter at breast height; b is the random effect; and ε is the error term. Maximum likelihood estimation is used to estimate the fixed-effects parameters β0, β1, and β2, as well as the variance of the random effect. The model's fit is tested by calculating the goodness of fit and performing a significance test. For models that do not fit successfully, additional measurement data are added until all models fit successfully.

[0014] Preferably, in step (1), the method for constructing the functional relationship between the total nitrogen, total phosphorus and total potassium content in the soil is as follows: set up a linear regression model, fit the total nitrogen with the total phosphorus and total potassium of the soil in each period, and then fit the total phosphorus with the total potassium. Establish the functional relationship between the total nitrogen, total phosphorus and total potassium in the soil through three equations.

[0015] The three equations are:

[0016] Total nitrogen = β0 + β1 × total phosphorus + ε;

[0017] Total nitrogen = β0 + β1 × total potassium + ε;

[0018] Total phosphorus = β0 + β1 × total potassium + ε.

[0019] The functional relationship between total nitrogen, total phosphorus, and total potassium in soil is: Y = β0 + β1X + ε; where Y represents total nitrogen or total phosphorus, and X represents total phosphorus or total potassium. β0 and β1 are parameters, and ε is the error term.

[0020] Preferably, in step (2), the specific computational steps of nonlinear optimization iterative machine learning are as follows:

[0021] 1) Initialization: Select an initial point x0, set the number of iterations n, and the storage length m of historical information;

[0022] 2) Iterative process:

[0023] a. Calculate the gradient g at the current point. k ;

[0024] b. Calculate the search direction p k The search direction is obtained by using information from the past m steps to approximate the inverse of the Hessian matrix.

[0025] c. Determine the step size α k Make it satisfy the following conditions:

[0026] Armijo conditions: and

[0027] Curvature condition:

[0028] d. Update the current point x k+1 =x k +α k p k ;

[0029] 3) Check the termination condition: the gradient has reached the preset maximum number of iterations. If the termination condition is not met, return to step 2); otherwise, output the current point as the optimal solution.

[0030] Preferably, the method for calculating the optimal solution of the comprehensive trait for each period in step (3) includes:

[0031] 1) The centroids of the optimal solutions for soil total nitrogen, total phosphorus, and total potassium for each economic indicator are obtained by using the direct mean method, and the optimal solution for the comprehensive trait is calculated. Specifically, the optimal soil total nitrogen content of all economic indicators is compiled into a dataset, and the mean is calculated. The mean values ​​of total phosphorus and total potassium are calculated in the same way.

[0032] 2) Combine the calculated mean values ​​of total nitrogen, total phosphorus, and total potassium into a vector, which is the centroid.

[0033] In step (3), the process of determining the amount and ratio of fertilizer based on the total nitrogen, total phosphorus, and total potassium content of the soil at the proposed fertilization site at each period is as follows: First, calculate the optimal nitrogen, phosphorus, and potassium fertilization amount for each period of the soapberry tree; then, at the period when fertilization is required, detect the total nitrogen, total phosphorus, and total potassium content of the soil in the rhizosphere of the target tree and compare it with the previously calculated optimal nitrogen, phosphorus, and potassium content for the corresponding period. If a certain element is deficient, apply the corresponding single fertilizer according to the difference. If a certain element is excessive, do not fertilize, and at the same time, take measures such as soil replacement and water flushing to make the content of this element in the soil fall within the calculated optimal value range. Attached Figure Description

[0034] Figure 1 The attached figure shows the functional relationship between soil nitrogen, phosphorus, and potassium content and pod yield per plant.

[0035] Figure 2 The attached figure shows the functional relationship between soil nitrogen, phosphorus, and potassium content and the yield of fruit shells per plant.

[0036] Figure 3 The attached figure shows the functional relationship between soil nitrogen, phosphorus, and potassium content and seed yield per plant.

[0037] Figure 4 The attached figure shows the functional relationship between soil nitrogen, phosphorus, and potassium content and polysaccharide gum content.

[0038] Figure 5 The attached figure shows the functional relationship between soil nitrogen, phosphorus, and potassium content and spiculic acid content.

[0039] Figure 6 The attached figure shows the functional relationship between soil nitrogen, phosphorus, and potassium content and saponin content.

[0040] Figure 7 The attached figures show the optimal values ​​for soapberry yield prediction based on nonlinear optimization iterative machine learning; each point represents one calculation, with green dots representing the optimal value, and the color becoming darker the further away from the optimal value, and the same applies below. Figure a shows the calculated pod yield per plant; Figure b shows the calculated shell yield per plant; Figure c shows the calculated seed yield per plant.

[0041] Figure 8 The attached figure shows the predicted values ​​of polysaccharide gum, spiculin, and saponins based on nonlinear optimization iterative machine learning. a) Calculation result for polysaccharide gum; b) Calculation result for spiculin; c) Calculation result for saponins.

[0042] Figure 9 The attached figure shows the prediction of yield and quality of a single soapberry plant using the optimal solution algorithm based on comprehensive traits. Note: Red dots represent optimal values, and green dots represent the substituted data values. Detailed Implementation

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Overview of the test site and test materials

[0045] The experimental site was located in Liangmo Village, Fencheng Town, Xiangfen County, Linfen City, Shanxi Province, at 35°48′52″N, 111°11′18″E, and an altitude of 530m. The site has a temperate monsoon climate with an average annual temperature of 12.73℃, an average January temperature of 4-5℃, an average July temperature of 26℃, an average annual rainfall of 513mm, a frost-free period of 185 days, a pH of 8.49, and soil nitrogen, phosphorus, and potassium content (see Table 1). The experimental material was 4-year-old grafted seedlings of the superior variety of *Gleditsia sinensis* 'Shuaiding' (8-year-old rootstock and 4-year-old scion of the same variety). The seedlings were in good growth condition and of basically uniform size.

[0046] Table 1. Nutrient element content in the soil layer of the test site before the experiment (g / kg)

[0047] Nutrients content N 0.52±0.11 P 0.14±0.04 K 9.9±0.25

[0048] Indicator Measurement Methods

[0049] Example 1: Fertilizer Application Prediction

[0050] (1) Determination of soil nitrogen, phosphorus and potassium content

[0051] During the budding, flowering, fruit setting, and ripening stages of the soapberry tree, soil samples were collected from the 0-20cm soil layer beneath each tree using a stainless steel soil drill. The samples were then placed in resealable bags and brought back to the laboratory. Plant debris and stones were removed from the soil, which was then dried and ground. The soil was sieved using a 100-mesh standard sieve, and 50g of the sieved soil sample was mixed and prepared for use. The samples were then digested with concentrated sulfuric acid, and the various nutrient elements were determined. Total nitrogen was determined using the Kjeldahl method, total phosphorus using the alkali-fusion molybdenum-antimony spectrophotometric method, and total potassium using the flame photometer method. The results are shown in Table 2. The functional relationships between total nitrogen, total phosphorus, and total potassium in the soil are shown in formulas (1)-(3).

[0052] Table 2 Soil Nitrogen, Phosphorus, and Potassium Content Survey Table

[0053] index average value Standard deviation Maximum value Minimum value Coefficient of variation (%) N(g / kg) 3.45 1.62 6.70 0.61 0.47 P(g / kg) 0.20 0.09 0.39 0.05 0.46 K(g / kg) 5.96 2.39 10.83 1.50 0.40

[0054] N = 0.57 × K (1)

[0055] N = 16.39 × P(2)

[0056] P = 0.03 × K (3)

[0057] Determination of pod, shell, and seed yield

[0058] After the pods matured, they were collected from individual plants, brought back to the laboratory, rinsed thoroughly with distilled water, and then air-dried in a cool, shaded place at 25±5℃. One month later, the following indicators were measured using an electronic balance:

[0059] Single fruit weight: The weight of a single fruit, in grams;

[0060] Pod yield per plant: the sum of the weights of all individual pods per plant, in kg;

[0061] Yield per plant: The pods are manually peeled, the pods are opened, the pods are extracted and weighed, kg;

[0062] Seed yield per plant: The pods are manually peeled, the seeds are extracted and weighed (kg).

[0063] The results are shown in Table 3.

[0064] Table 3. Survey of Yield per Plant

[0065] index average value Standard deviation Maximum value Minimum value Coefficient of variation (%) Pod yield per plant (kg) 0.73 0.77 2.66 0.01 1.05 Fruit shell yield per plant (kg) 0.62 0.66 2.19 0.01 1.07 Seed yield per plant (kg) 0.11 0.13 0.47 * 1.11

[0066] Polysaccharide gum content determination

[0067] Remove damaged or insect-infested seeds from the soapberry seeds, and select plump seeds of roughly the same size. Crush the seeds using a pulverizer and pass them through a 100-mesh sieve. Weigh 10g of soapberry seed powder from each plant using an electronic balance and place it in a 100ml beaker. Add deionized water at a ratio of 1:10 (g / mL) and stir. Then place the beaker in a water bath at 60℃ and stir. Centrifuge the stirred sample for 10 minutes (5000r / min) to extract the supernatant. Precipitate the soapberry polysaccharide gum with 95% ethanol, with the ethanol volume equal to the volume of the supernatant. While stirring, slowly pour the ethanol into the supernatant, and the white flocculent material will gradually separate out. Large white flocculent particles were placed in a petri dish. The remaining, difficult-to-remove white flocculent particles were centrifuged with the liquid at 5000 rpm for 10 minutes. The precipitate was removed, spread evenly, and placed back into the previous petri dish. The spread white flocculent particles were then placed in a 50°C electric thermostatic drying oven for drying. After drying, the particles were weighed, and the gel yield was calculated using the following formula:

[0068] A = B / C × 100

[0069] In the formula: A is the content of soapberry polysaccharide gum, %; B is the weight of soapberry polysaccharide gum after drying, g; C is the weight of soapberry seed powder, g.

[0070] Determination of spiculic acid content

[0071] Preparation of the standard curve: Prepare a methanol solution at a concentration of 10 mg / ml. Absorb 10 μL, 20 μL, 30 μL, 40 μL, and 50 μL into 10 ml test tubes, respectively. After evaporating the solvent by heating, cool the test tubes in ice water. Then add 0.5 ml of 8% vanillin ethanol solution and 5 ml of 77% sulfuric acid, shake thoroughly, and heat in a 60℃ water bath for 30 min. After cooling to room temperature, use a UV-Vis spectrophotometer to perform a full wavelength scan in the range of 454 nm to determine the highest absorption peak for each sample. Measure each tube three times and calculate the average value. Accurately weigh 10 mg of spiculic acid and prepare a 2 mg / ml methanol solution. Take 300 mg of the sample, add methanol, and dilute to 30 ml. Absorb 20 μL into a 10 ml test tube. Perform the remaining steps as described above and determine the absorbance value according to the standard curve determination procedure.

[0072] Saponin content determination

[0073] Saponins were extracted from soapberry using a traditional water extraction method. The pods were ground using liquid nitrogen. After grinding, 10g of soapberry powder was taken from each pod and soaked in distilled water at 25℃ for 10 hours. The mixture was then extracted in a water bath, filtered, and the residue was repeated twice. The extracts were combined and concentrated under reduced pressure to obtain a paste. Ten times the volume of isopropanol was added, and the solution was centrifuged at 4000 rpm for 10 minutes. The precipitate was discarded, and the supernatant was distilled under reduced pressure to recover the isopropanol. This purification process was repeated three times to obtain a pale yellow, viscous liquid containing saponins. The solid was then freeze-dried under vacuum to obtain a pale yellow solid. The contents were weighed and calculated. See Table 4.

[0074] Table 4. Survey of Gleditsia sinensis polysaccharide gum, stigmataic acid, and saponins

[0075] index average value Standard deviation Maximum value Minimum value Coefficient of variation (%) Polysaccharide gum content (%) 25.49 8.25 39.16 8.17 0.32 Hypochondral acid content (%) 0.40 0.13 0.81 0.16 0.33 Saponin content (%) 8.28 2.70 16.67 3.38 0.33

[0076] Simultaneously measure the diameter at ground level:

[0077] The average diameter at ground level of the plant was measured to be 6.86 ± 1.63 cm.

[0078] Following the aforementioned calculation steps, the optimal soil total nitrogen, total phosphorus, and total potassium contents for the *Gleditsia sinensis* trees during the budding, flowering, fruit setting, and ripening stages were calculated at the experimental site. The total nitrogen, total phosphorus, and total potassium contents at each stage were treated as fixed effects, ground diameter as a covariate, and individual tree as a random effect. The model was set as: Y = β0 + β1X + β2X 2 +D+b+ε;

[0079] Where Y is the yield index; X is the total nitrogen, total phosphorus, and total potassium content of the soil; β0, β1, and β2 are fixed-effect parameters; D is the diameter at rootstock; b is the random effect; and ε is the error term. Maximum likelihood estimation is used to estimate the fixed-effect parameters β0, β1, and β2, as well as the variance of the random effect. The model's fit is tested by calculating the goodness of fit and performing a significance test. For models that do not fit successfully, additional measurement data are added until all models successfully fit the soil.

[0080] Then, a linear regression model was set up, and total nitrogen was fitted with total phosphorus and total potassium in the soil for each period, and total phosphorus was fitted with total potassium. The functional relationship between total nitrogen, total phosphorus, and total potassium in the soil was established through three equations:

[0081] Total nitrogen = β0 + β1 × total phosphorus + ε;

[0082] Total nitrogen = β0 + β1 × total potassium + ε;

[0083] Total phosphorus = β0 + β1 × total potassium + ε.

[0084] The results show that the functional relationship between nitrogen, phosphorus, and potassium content and pod yield per plant is as follows: Figure 1 The functional relationship between soil nitrogen, phosphorus, and potassium content and per-plant fruit shell yield is shown in [reference needed]. Figure 2The functional relationship between soil nitrogen, phosphorus, and potassium content and seed yield per plant is shown in [reference needed]. Figure 3 The functional relationship between soil nitrogen, phosphorus, and potassium content and spiculic acid content is shown in [reference needed]. Figure 4 The functional relationship between soil nitrogen, phosphorus, and potassium content and spiculic acid content is shown in [reference needed]. Figure 5 The functional relationship between soil nitrogen, phosphorus, and potassium content and saponin content is shown in [reference needed]. Figure 6 .

[0085] (2) Calculation of the optimal nitrogen, phosphorus and potassium contents for multiple economic traits of Gleditsia sinensis

[0086] The optimal value for predicting soapberry yield is based on nonlinear optimization iterative machine learning, and the process is as follows:

[0087] 1) Initialization: Select an initial point x0, set the number of iterations n, and the storage length m of historical information;

[0088] 2) Iterative process:

[0089] a. Calculate the gradient g at the current point. k ;

[0090] b. Calculate the search direction p k The search direction is obtained by using information from the past m steps to approximate the inverse of the Hessian matrix.

[0091] c. Determine the step size α k Make it satisfy the following conditions:

[0092] Armijo conditions: and

[0093] Curvature condition:

[0094] d. Update the current point x k+1 =x k +α k p k ;

[0095] 3) Check the termination condition: the gradient has reached the preset maximum number of iterations. If the termination condition is not met, return to step 2); otherwise, output the current point as the optimal solution.

[0096] See results Figure 7 and Figure 8 .

[0097] Predictions show that when soil nitrogen, phosphorus, and potassium contents reach 4.11 g / kg, 0.25 g / kg, and 7.16 g / kg respectively, the yield per plant of *Gleditsia sinensis* can reach 5.86 kg; when soil nitrogen, phosphorus, and potassium contents reach 4.03 g / kg, 0.24 g / kg, and 6.95 g / kg respectively, the yield of fruit shells per plant can reach 5.04 kg; and when soil nitrogen, phosphorus, and potassium contents reach 3.97 g / kg, 0.24 g / kg, and 6.91 g / kg respectively, the seed yield per plant can reach... The yield of polysaccharide gum can reach 1.07 kg; when the soil nitrogen, phosphorus and potassium contents reach 3.77 g / kg, 0.22 g / kg and 6.49 g / kg respectively, the gum yield can reach 45%; when the soil nitrogen, phosphorus and potassium contents reach 3.72 g / kg, 0.23 g / kg and 6.2 g / kg respectively, the saponin content can reach 33.61%; when the soil nitrogen, phosphorus and potassium contents reach 3.74 g / kg, 0.23 g / kg and 6.49 g / kg respectively, the stigmacanoid content can reach 1.56%.

[0098] (3) Based on the optimal solutions for total nitrogen, total phosphorus, and total potassium in the soil for each economic indicator in each period, the optimal solution for the comprehensive trait in each period is calculated and used as the soil testing and fertilization standard for each period. The centroids of the optimal total nitrogen, total phosphorus, and total potassium contents of the aforementioned six traits are solved, showing that when the soil nitrogen, phosphorus, and potassium contents reach 3.74 g / kg, 0.22 g / kg, and 6.51 g / kg, respectively, the yield and quality of soapberry can reach a relatively good level simultaneously. Figure 9 Then, at each period, the amount and ratio of fertilizer are determined based on the total nitrogen, total phosphorus, and total potassium content of the soil at the proposed fertilization site, as shown in Table 2.

[0099] Fertilization was carried out according to the calculated optimal total nitrogen, total phosphorus, and total potassium contents of the soil at the budding, flowering, fruit setting, and ripening stages of the experimental site. At the corresponding time points in the second year, soil nutrient contents were measured. For areas where the total nitrogen, total phosphorus, and total potassium contents were lower than the calculated values, fertilization was increased to bring them closer to the calculated levels. For areas where the total nitrogen, total phosphorus, and total potassium contents were higher than the calculated values, irrigation and drainage were increased to help dissolve and dilute excess nutrients in the soil, bringing them closer to the calculated levels.

[0100] Table 2

[0101]

[0102]

[0103] Example 2: Verification of the effectiveness of the method in Example 1

[0104] Fertilization was carried out on the target area according to the method in Example 1. After one year of treatment, many economic indicators of soapberry in the study area were significantly improved, as detailed in Table 2.

[0105] Table 2. Increase in Economic Indicators of Gleditsia sinensis

[0106]

[0107] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0108] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fertilization method for achieving increased yield of soapberry plants to achieve multiple economic goals, characterized in that, The process includes: (1) During the budding, flowering, fruit setting and ripening stages of the soapberry tree, the soil total nitrogen, total phosphorus and total potassium content within the canopy area of ​​each tree were measured. After harvesting, multiple economic indicators of each tree were measured and the diameter of the fruit tree was recorded. The mixed-effects multinomial regression method was used to construct the functional relationship between the soil total nitrogen, total phosphorus and total potassium content and various economic indicators at each stage, as well as the functional relationship between the soil total nitrogen, total phosphorus and total potassium content. (2) Using optimization algorithms, nonlinear optimization iterative machine learning is performed on each economic indicator function for each period and the relationship functions of total nitrogen, total phosphorus and total potassium in the soil to obtain the optimal solution for each economic indicator; (3) Based on the optimal solutions of total nitrogen, total phosphorus and total potassium in soil for each economic indicator in each period, calculate the optimal solution of comprehensive characteristics in each period, and use it as the soil testing and fertilization standard for each period. Then, in each period, determine the amount and ratio of fertilizer based on the total nitrogen, total phosphorus and total potassium content of soil at the proposed fertilization site. In step (2), the specific computational steps of nonlinear optimization iterative machine learning are as follows: 1) Initialization: Select an initial point x0, set the number of iterations n, and the storage length m of historical information; 2) Iterative process: a. Calculate the gradient g at the current point. k ; b. Calculate the search direction p k The search direction is obtained by using information from the past m steps to approximate the inverse of the Hessian matrix. c. Determine the step size α k Make it satisfy the following conditions: Armijo condition: f (x k + α k p k )≤ f (x k ) + c1α k ∇ f (x k ) T p k Curvature condition: ∇ f (x k + α k p k ) T p k ≥c2∇ f (x k ) T p k d. Update the current point x k+1 = x k + α k p k ; 3) Check the termination condition: if the gradient has reached the preset maximum number of iterations; if the termination condition is not met, return to step 2); otherwise, output the current point as the optimal solution. The calculation method for the optimal solution of the comprehensive trait in each period in step (3) includes: 1) The centroids of the optimal solutions for total nitrogen, total phosphorus, and total potassium in soil for each economic indicator are obtained by using the direct mean method, and the optimal solution for the comprehensive trait is calculated. Specifically, the optimal total nitrogen content of soil for all economic indicators is compiled into a dataset, and the mean is calculated. The mean values ​​of total phosphorus and total potassium are calculated in the same way. 2) Combine the calculated mean values ​​of total nitrogen, total phosphorus, and total potassium into a vector. This vector is the centroid, which is the optimal solution for the overall trait. In step (3), the process of determining the amount and ratio of fertilizer based on the total nitrogen, total phosphorus and total potassium content of the soil at the proposed fertilization site at each period is as follows: calculate the optimal solution for comprehensive traits; then, at the period when fertilization is required, detect the total nitrogen, total phosphorus and total potassium content of the soil in the rhizosphere of the target tree and compare it with the optimal solution for the corresponding period calculated previously; when a certain element is insufficient, apply the corresponding single fertilizer according to the difference; when a certain element is excessive, do not apply fertilizer, and at the same time take measures such as soil replacement or water flushing to make the content of this element in the soil fall within the calculated optimal value range. In step (1), multiple economic indicators include pod yield, seed yield, shell yield, polysaccharide gum yield, spiculin yield, and saponin yield per plant.

2. The fertilization method for achieving increased yield of soapberry plants with multiple economic objectives according to claim 1, characterized in that, In step (1), the functional relationship between the soil total nitrogen, total phosphorus, and total potassium content and various economic indicators for each period is constructed as follows: taking the total nitrogen, total phosphorus, and total potassium content for each period as fixed effects, soil diameter as a covariate, and individual plant as a random effect, the model is set as follows: Y=β0+β1X+β2X 2 +D+b+e Where Y is an economic indicator; X is the total nitrogen, total phosphorus, and total potassium content of the soil; β0, β1, and β2 are fixed-effect parameters; D is diameter at breast height; b is the random effect; and ɛ is the error term. Maximum likelihood estimation is used to estimate the fixed-effect parameters β0, β1, and β2, as well as the variance of the random effect. The model fit is tested by calculating the goodness of fit and performing a significance test. For models that do not fit successfully, additional measurement data are added until all models fit successfully.

3. The fertilization method for achieving increased yield of soapberry plants with multiple economic objectives according to claim 2, characterized in that, In step (1), the method for constructing the functional relationship between soil total nitrogen, total phosphorus and total potassium content is as follows: set up a linear regression model, fit the total nitrogen with the total phosphorus and total potassium of the soil in each period, and then fit the total phosphorus with the total potassium. Establish the functional relationship between soil total nitrogen, total phosphorus and total potassium through three equations.

4. The fertilization method for achieving increased yield of soapberry plants with multiple economic objectives according to claim 3, characterized in that, The three equations are: Total nitrogen = β0 + β1 × total phosphorus + ɛ; Total nitrogen = β0 + β1 × total potassium + ɛ; Total phosphorus = β0 + β1 × total potassium + ɛ.

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