Forest precise fertilization decision-making method and system based on soil nutrient profit and loss

Abstract

The invention relates to the technical field of forest fertilization, in particular to a forest precise fertilization decision-making method and system based on soil nutrient profit and loss, and the method comprises the steps: obtaining basic information of a target forest land, and dividing forest growth stages based on the basic information; the method comprises the following steps: collecting an undisturbed soil sample and a mixed soil sample of a 0-40cm soil layer of a target forest land by adopting an S-shaped point distribution method, separating the mixed soil sample, and respectively measuring the soil nutrient content, the bioavailability, the soil enzyme activity and the microbial functional gene. According to the method, by measuring the dimension indexes, constructing a five-dimensional profit and loss evaluation index system for determining the reference value and dynamically adjusting the weight according to the forest land data, calculating the comprehensive profit and loss index, judging the nutrient state and generating the personalized fertilization scheme, the defects that an existing fertilization scheme is single in basis index and lacks an effective dynamic adjustment link are effectively overcome; the requirements of multi-generation continuous planting forest land are met, and the environmental risks such as fertilizer waste, soil acidification and water eutrophication are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of forest fertilization technology, specifically to a method and system for making precise fertilization decisions for forest trees based on soil nutrient balance. Background Technology

[0002] The healthy growth of trees is closely related to the supply of soil nutrients. Macroelements such as nitrogen, phosphorus, and potassium, as well as microelements such as iron, manganese, zinc, and boron, are crucial material bases for photosynthesis, biomass accumulation, and timber formation in trees. The state of soil nutrient balance directly determines the productivity level of forest land: when nutrient supply is insufficient, trees are prone to slow growth and reduced resilience; when nutrients are excessive, not only is fertilizer wasted, but environmental problems such as soil acidification and eutrophication of water bodies may also occur. To achieve efficient and sustainable forest management, precision fertilization has become a core technological direction. Its core logic is to accurately grasp the soil nutrient balance and formulate a fertilization plan that matches the needs of trees. This process requires comprehensively acquiring information on soil nutrient content, nutrient bioavailability (i.e., the forms of nutrients in the soil that can be directly absorbed and utilized by trees), soil environmental conditions affecting nutrient transformation, nutrient transformation activity such as enzymes, and key factors such as microbial function (e.g., soil microorganisms, which participate in nutrient cycling by decomposing organic nutrients and transforming nutrient forms).

[0003] Most existing precision fertilization decision-making methods for forests rely solely on the total amount of soil nutrients or the content of a single available nutrient to determine nutrient surplus or deficit, failing to adequately consider the bioavailability of soil nutrients and its coupling relationship with soil environmental conditions and soil microbial community function. Soil microorganisms are crucial drivers of nutrient transformation in soil; their community structure and the abundance of functional genes related to nutrient cycling directly affect the efficiency of converting unavailable nutrients into available nutrients. Especially in multi-generational continuously planted forests, long-term monoculture significantly alters the soil microbial community structure, leading to changes in nutrient transformation capacity. Existing methods ignore this coupling effect, relying solely on nutrient content indicators to determine surplus or deficit, failing to accurately identify the actual amount of nutrients available to forests. This results in fertilization plans that either overestimate nutrient availability leading to insufficient fertilization and affecting forest growth, or underestimate nutrient transformation potential leading to excessive fertilization, further exacerbating soil degradation, and failing to meet the actual needs of multi-generational continuously planted forests for precision fertilization. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for precise fertilization decisions for forest trees based on soil nutrient balance. This solves the problems of existing technologies that rely solely on the total amount or a single available form of soil nutrients, neglect soil environmental conditions, and the coupling of microorganisms with nutrient bioavailability, leading to insufficient or excessive fertilization and difficulty in adapting to the needs of multi-generational continuous planting forests.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for precision fertilization decision-making for forest trees based on soil nutrient balance, comprising: Obtain basic information about the target forest land and classify the growth stages of trees based on this information; Soil and microbial samples were collected and then processed separately. The study measured multiple indicators, including soil nutrient indicators, microbial functional gene indicators, bioavailability indicators, soil environmental indicators, nutrient transformation activity indicators, and soil environmental and enzyme activity indicators. A five-dimensional profit and loss evaluation index system is constructed, which specifically includes: determining the benchmark values ​​of each dimension based on the reference forest land data, calculating the profit and loss index of each dimension, and dynamically adjusting the weight of each dimension according to the growth stage of the trees; Calculate the comprehensive profit and loss index, and determine the nutrient profit and loss status of the forest land based on the comprehensive profit and loss index; Personalized fertilization plans are generated based on nutrient balance and deficiency type. These plans include fertilizer type, nutrient content ratio, dosage, application method, and application time.

[0006] Furthermore, the collection of soil and microbial samples includes: setting up sampling points in the target forest area using an S-shaped sampling method, and collecting undisturbed soil samples and mixed soil samples with a soil depth of 0-40 cm at the sampling points; the undisturbed soil samples are used to determine soil environmental indicators; the sample processing includes dividing the mixed soil samples into a first mixed soil sample, a second mixed soil sample, and a third mixed soil sample, the first mixed soil sample is used to determine soil nutrient content and bioavailability, the second mixed soil sample is used to determine soil enzyme activity, and the third mixed soil sample is used for microbial sample analysis.

[0007] Furthermore, the multi-dimensional indicators for measurement include: The determination of soil nutrient indicators includes the determination of the content of total nitrogen, total phosphorus, total potassium, total sodium, total calcium, total magnesium, total sulfur and total selenium in the soil; The determination of soil nutrient bioavailability indicators includes soil ammonium nitrogen, nitrate nitrogen, alkaline available nitrogen, available phosphorus, phosphorus extracted by calcium chloride, phosphorus extracted by citric acid, phosphorus extracted by phytase, phosphorus extracted by hydrochloric acid, available potassium, as well as the contents of available molybdenum, available silicon, available iron, available manganese, available zinc and available boron; The determination of soil environmental indicators includes soil temperature, humidity, pH value, bulk density, porosity, and maximum water holding capacity; The determination of soil nutrient transformation activity indicators includes the activities of β-glucosidase, cellobiase, protease, N-acetylglucosidase and phosphatase. The determination of microbial functional gene indicators included measuring the abundance of nitrogen-fixing gene nifH, denitrification genes nirK, nirS, nosZ, and norB, nitrate-reducing bacteria gene narG, ammonia-oxidizing genes AOA and AOB, phosphate monoester degradation genes phoD and phoC, inorganic phosphorus-dissolving bacteria gene pqqC, cellulose degradation genes fungcbhIR and GH74, and β-glucosidase gene bg1.

[0008] Furthermore, the construction of the five-dimensional profit and loss evaluation index system includes: The baseline values ​​for each dimension were determined, including the baseline values ​​for nutrient content S0, bioavailability B0, soil environment E0, nutrient transformation activity T0, and microbial function M0. The baseline values ​​were obtained based on data from healthy forest land with the same region, soil type, and no continuous planting. The calculation of single-dimensional profit and loss indices includes: nutrient content profit and loss index S = (measured nutrient content - S0) / S0 × 100%; bioavailability profit and loss index B = (measured available nutrient content - B0) / B0 × 100%; soil environmental baseline value E = (measured environmental index value - E0) / E0 × 100%; nutrient conversion activity baseline value T = (measured nutrient conversion enzyme activity - T0) / T0 × 100%; and microbial function profit and loss index M = (measured functional gene abundance - M0) / M0 × 100%.

[0009] Furthermore, based on the characteristics of nutrient requirements at different growth stages of trees, the dynamic adjustment of the weights of each dimension according to the growth stage of trees includes: When the trees are in the young forest stage, the weight distribution ratio of each dimension is 13-22:27-55:5-18:9-21:8-20; When the trees are in the mid-forest stage, the weight distribution ratio of each dimension is 15-25:20-45:8-22:10-25:13-28; When the trees are in the mature stage, the weight distribution ratio of each dimension is 10-25:18-35:6-20:15-30:18-35. The weights of each dimension include the weights of nutrient content, bioavailability, soil environment, nutrient transformation activity, and microbial function.

[0010] Furthermore, the formula for calculating the comprehensive profit and loss index I is as follows: ,in , , These are the weights for nutrient content, bioavailability, soil environment, nutrient transformation activity, and microbial function. The nutrient surplus or deficit status of forest land is determined based on the comprehensive profit and loss index I, which includes comparing the comprehensive profit and loss index I with a preset threshold to determine whether it is a nutrient surplus, nutrient balance or nutrient deficit.

[0011] Furthermore, the generation of personalized fertilization plans based on nutrient surplus / deficit status and deficiency type includes: When nitrogen deficiency is identified, it is recommended to apply slow-release nitrogen fertilizer. The amount of slow-release nitrogen fertilizer should be calculated based on the nutrient content baseline, measured total nitrogen content, ammonium nitrogen content, nitrate nitrogen content, alkaline available nitrogen content, soil bulk density, soil layer thickness, and safety factor. When phosphorus deficiency is identified, it is recommended to apply superphosphate in combination with bio-phosphorus-solubilizing bacteria fertilizer. The amount of superphosphate or calcium magnesium phosphate fertilizer should be calculated based on the bioavailability benchmark, measured total phosphorus content, available phosphorus content, phosphorus content extracted by calcium chloride, phosphorus content extracted by citric acid, phosphorus content extracted by phytase, phosphorus content extracted by hydrochloric acid, soil bulk density, soil layer thickness, and safety factor, and the amount of bio-phosphorus-solubilizing bacteria fertilizer should be determined. When it is determined that the microbial function is insufficient, it is recommended to apply compound bio-fertilizer, and determine the dosage and application time of the compound bio-fertilizer. The application time is the beginning of the wet season.

[0012] This invention also provides a forestry precision fertilization decision system based on soil nutrient balance, applied to any of the above-described forestry precision fertilization decision methods based on soil nutrient balance, comprising: The data acquisition module is used to collect soil samples and forest environmental data; The indicator analysis module is used to receive data, calculate soil nutrient content, bioavailability, and abundance of microbial functional genes, and output the calculation results. The profit and loss evaluation module is used to receive calculation results, calculate the comprehensive profit and loss index, determine the nutrient profit and loss status, and transmit the determination results. The decision output module is used to receive the judgment results and generate personalized fertilization plans based on the judgment results.

[0013] Furthermore, the data acquisition module includes an automatic soil sampling device, a multi-parameter sensor group, and a sample preservation unit; the automatic soil sampling device can control the sampling depth and the amount of samples taken at one time; the multi-parameter sensor group is used to monitor various environmental parameters such as soil pH, temperature and humidity in real time, and to collect forest environmental data; the sample preservation unit integrates a 4℃ cold storage chamber and a -80℃ freezer chamber.

[0014] Furthermore, the index analysis module is linked with laboratory testing equipment, which preferably includes a Kjeldahl nitrogen analyzer, an atomic absorption spectrometer, a fluorescence microplate reader, and a high-throughput sequencer. The index analysis module is used to receive the test data provided by the laboratory testing equipment, automatically calculate the soil nutrient content, bioavailability, and abundance of microbial functional genes, and output the calculation results to the profit and loss evaluation module.

[0015] Furthermore, the system also includes: The dynamic adjustment module receives the latest nutrient balance status generated by the profit and loss evaluation module based on the annual soil monitoring results of the target forest land, and adjusts the personalized fertilization plan in real time in combination with the current growth stage of the trees. The adjustment includes fertilizer type, nutrient content ratio, dosage, application method and application time.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention obtains basic information about the target forest land to divide the tree growth stages, collects and processes soil and microbial samples to determine multi-dimensional indicators of soil nutrients, microbial functional genes, soil environment, and enzyme activity. It constructs a five-dimensional profit and loss evaluation index system that uses forest land data to determine benchmark values, calculates a single-dimensional profit and loss index, and dynamically adjusts weights according to the tree growth stages. Furthermore, it calculates a comprehensive profit and loss index to determine the nutrient profit and loss status and generates personalized fertilization plans. This effectively solves the problems of insufficient or excessive fertilization caused by existing methods that only rely on the total amount or single available state of soil nutrients, ignore the soil environment, and the coupling of microorganisms and nutrient bioavailability. It avoids fertilizer waste and environmental problems such as soil acidification, adapts to the needs of multi-generational continuous planting forest land, maintains forest land productivity, and achieves precise fertilization and sustainable management of trees. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0019] Please see Figure 1 This invention provides a method for precision fertilization decision-making for forest trees based on soil nutrient balance, comprising: Obtain basic information about the target forest land and classify the growth stages of trees based on this information; Soil and microbial samples were collected and then processed separately. The study measured multiple indicators, including soil nutrient indicators, microbial functional gene indicators, bioavailability indicators, soil environmental indicators, nutrient transformation activity indicators, and soil environmental and enzyme activity indicators. A five-dimensional profit and loss evaluation index system is constructed, which specifically includes: determining the benchmark values ​​of each dimension based on the reference forest land data, calculating the profit and loss index of each dimension, and dynamically adjusting the weight of each dimension according to the growth stage of the trees; Calculate the comprehensive profit and loss index, and determine the nutrient profit and loss status of the forest land based on the comprehensive profit and loss index; Personalized fertilization plans are generated based on nutrient balance and deficiency type. These plans include fertilizer type, nutrient content ratio, dosage, application method, and application time.

[0020] Specifically, the implementation was carried out in a certain artificial forest as the target forest site. First, basic information about the target forest site was obtained, including site conditions such as tree species, soil type, altitude, slope, and aspect, as well as the planting age of the trees. Based on this, the growth stages were divided: 1-3 years after planting was the juvenile forest stage, 4-6 years was the middle forest stage, and 7 years and above was the mature forest stage. Then, an S-shaped sampling method was used to set up 8-10 sampling points in the target forest site. At each sampling point, undisturbed soil samples and mixed soil samples from the 0-40cm soil layer were collected. The mixed soil sample was divided into three parts for subsequent measurement of different indicators.

[0021] When measuring multi-dimensional indicators, soil temperature and humidity were directly acquired through multi-parameter sensors. Uncirculated soil samples were used to measure soil bulk density, porosity, and maximum water holding capacity. The first mixed soil sample was used to determine total nitrogen content using a Kjeldahl nitrogen analyzer, total phosphorus content using a molybdenum-antimony colorimetric method, and total potassium and available iron, manganese, and zinc content using atomic absorption spectrophotometry. The second mixed soil sample was used to determine the activities of β-glucosidase, cellobiase, protease, N-acetylglucosidase, and phosphatase using a fluorescence microplate reader. The third mixed soil sample was used to determine the abundance of nitrogen fixation gene nifH, denitrification genes nirK, nirS, nosZ, and norB, nitrate-reducing bacteria gene narG, ammonia oxidation genes AOA and AOB, phosphate monoester degradation genes phoD and phoC, inorganic phosphorus lysing bacteria gene pqqC, cellulose degradation genes fungcbhIR and GH74, and β-glucosidase gene bg1 using a high-throughput sequencer.

[0022] When constructing a five-dimensional profit and loss evaluation index system, healthy forest land with the same soil type in the same region and not continuously planted was selected as a control. The baseline values ​​for nutrient content (S0), bioavailability (B0), soil environment (E0), nutrient transformation activity (T0), and microbial function (M0) were determined. A single-dimensional profit and loss index was calculated. If the measured total nitrogen content was lower than S0, the nutrient content profit and loss index S was negative, indicating a nitrogen deficit. The weights were adjusted according to the growth stage: 18:44:9:16:14 for juvenile forests, 20:38:10:15:17 for mid-grown forests, and 22:30:9:18:21 for mature forests.

[0023] Calculate the comprehensive profit and loss index I. If, during the young forest stage, S=-12%, B=-10%, E=-8%, T=2%, and M=-3%, then I=(-12)×0.18+(-10)×0.44+(-8)×0.09+2×0.16+(-3)×0.14=-7.38%. Compare I with the preset threshold; I<-5% indicates nutrient deficiency. For the type of deficiency, if it is nitrogen deficiency, slow-release nitrogen fertilizer is recommended; if it is insufficient microbial function, compound bio-fertilizer is recommended to be applied at the beginning of the wet season to achieve precise fertilization, avoid fertilizer waste and soil acidification, and improve the fertility of continuously planted forest land.

[0024] In this embodiment, the collection of soil and microbial samples includes: setting up sampling points in the target forest area using an S-shaped sampling method, and collecting undisturbed soil samples and mixed soil samples with a soil depth of 0-40cm at the sampling points; the undisturbed soil samples are used to determine soil environmental indicators; the sample processing includes dividing the mixed soil samples into a first mixed soil sample, a second mixed soil sample, and a third mixed soil sample, the first mixed soil sample is used to determine soil nutrient content and bioavailability, the second mixed soil sample is used to determine soil enzyme activity, and the third mixed soil sample is used for microbial sample analysis.

[0025] Specifically, sample collection was conducted in a plantation within the target forest area. An S-shaped sampling method was used to establish 8-10 sampling points in each plot to ensure coverage of different areas. At each sampling point, undisturbed soil samples from the 0-40cm soil layer were collected using sampling tools, along with a mixed soil sample from the same layer. The mixed soil sample was thoroughly mixed to ensure representativeness.

[0026] The mixed soil sample was divided into three equal portions. The first portion was placed in a ventilated area to air dry naturally and will be used for subsequent determination of total nitrogen, total phosphorus, total potassium, and other nutrient contents, as well as the bioavailability of available nutrients. The second portion was stored in a 4°C refrigerator to prevent enzyme inactivation and will be used for subsequent determination of soil enzyme activities such as β-glucosidase and cellobiase. The third portion was stored in a -80°C freezer to prevent degradation of microbial functional genes and will be used for subsequent microbial sample analysis, ensuring the accuracy and reliability of the data for each detection indicator.

[0027] In this embodiment, the measurement of multi-dimensional indicators includes: The determination of soil nutrient indicators includes measuring the content of total nitrogen, total phosphorus, total potassium, total sodium, total calcium, total magnesium, total sulfur, and total selenium in the soil. The determination of soil nutrient bioavailability indicators includes soil ammonium nitrogen, nitrate nitrogen, alkaline available nitrogen, available phosphorus, phosphorus extracted by calcium chloride, phosphorus extracted by citric acid, phosphorus extracted by phytase, phosphorus extracted by hydrochloric acid, available potassium, as well as available molybdenum, available silicon, available iron, available manganese, available zinc, and available boron. The determination of soil environmental indicators includes soil temperature, humidity, pH value, bulk density, porosity, and maximum water holding capacity; The determination of soil nutrient transformation activity indicators includes the activities of β-glucosidase, cellobiase, protease, N-acetylglucosidase, and phosphatase. The determination of microbial functional gene indicators included measuring the abundance of nitrogen-fixing gene nifH, denitrification genes nirK, nirS, nosZ, and norB, nitrate-reducing bacteria gene narG, ammonia-oxidizing genes AOA and AOB, phosphate monoester degradation genes phoD and phoC, inorganic phosphorus-dissolving bacteria gene pqqC, cellulose degradation genes fungcbhIR and GH74, and β-glucosidase gene bg1.

[0028] Specifically, when determining soil nutrient indicators, undisturbed soil samples are taken, and the soil bulk density, porosity, and maximum water holding capacity are determined using the ring cutter method. The first air-dried mixed soil sample is taken, and the total nitrogen content of the soil is determined using the Kjeldahl method. The total phosphorus content is determined using the molybdenum-antimony colorimetric method, and the total potassium content, available iron, available manganese, and available zinc content are determined using atomic absorption spectrophotometry to comprehensively understand the status of basic and micronutrients in the soil.

[0029] When measuring soil environmental indicators, soil temperature and humidity are directly obtained through multi-parameter sensors, soil pH is measured using a pH meter, soil bulk density is measured using the ring cutter method, and maximum water holding capacity is determined using the weighing method.

[0030] When determining soil nutrient transformation activity indicators, a second mixed soil sample stored at 4℃ was taken, and the activities of β-glucosidase, cellobiase, protease, N-acetylglucosidase, and phosphatase were measured using a fluorescence microplate reader to understand the suitability of the soil environment for tree growth and nutrient transformation efficiency.

[0031] When determining the functional gene indicators of microorganisms, a third mixed soil sample stored at -80℃ was taken. Soil genomic DNA was extracted and high-throughput sequencers were used to sequence the nitrogen fixation gene nifH, denitrification genes nirK, nirS, nosZ, norB, nitrate-reducing bacteria gene narG, ammonia oxidation genes AOA, AOB, phosphate monoester degradation genes phoD, phoC, inorganic phosphorus lysing bacteria gene pqqC, cellulose degradation genes fungcbhIR, GH74, and β-glucosidase gene bg1. The abundance of these genes was analyzed to reflect the ability of soil microorganisms to participate in nutrient transformation.

[0032] In this embodiment, the construction of the five-dimensional profit and loss evaluation index system includes: The baseline values ​​for each dimension were determined, including the baseline values ​​for nutrient content S0, bioavailability B0, soil environment E0, nutrient transformation activity T0, and microbial function M0. The baseline values ​​were obtained based on data from healthy forest land with the same region, soil type, and no continuous planting. The calculation of single-dimensional profit and loss indices includes: nutrient content profit and loss index S = (measured nutrient content - S0) / S0 × 100%; bioavailability profit and loss index B = (measured available nutrient content - B0) / B0 × 100%; soil environmental baseline value E = (measured environmental index value - E0) / E0 × 100%; nutrient conversion activity baseline value T = (measured nutrient conversion enzyme activity - T0) / T0 × 100%; and microbial function profit and loss index M = (measured functional gene abundance - M0) / M0 × 100%.

[0033] Specifically, when determining the baseline values ​​for each dimension, healthy forest land with the same soil type in the same region and not continuously planted was selected as the control forest land. Soil samples were collected from this forest land, and the total nitrogen, total phosphorus and other nutrient contents in the soil were measured using the same measurement methods as the target forest land and determined as the nutrient content baseline value S0; the available nutrient content was measured and determined as the bioavailability baseline value B0; the soil temperature, humidity, pH value and other parameters were measured and determined as the soil environmental baseline value E0; the soil pH value and other parameters were measured and determined as the soil environmental baseline value E0; the enzyme activities such as β-glucosidase were measured and determined as the soil nutrient transformation activity baseline value T0; and the abundance of functional genes such as nitrogen fixation gene nifH was measured and determined as the microbial function baseline value M0.

[0034] When calculating the single-dimensional profit and loss index, if the measured total nitrogen content of the target forest land is 1.2 g / kg and S0 is 1.5 g / kg, then the nutrient content profit and loss index S = (1.2 - 1.5) / 1.5 × 100% = -20%, indicating a nitrogen nutrient deficit. If the measured available phosphorus content is 2.0 mg / kg and B0 is 1.8 mg / kg, then the bioavailability profit and loss index B = (2.0 - 1.8) / 1.8 × 100% = 11.1%, indicating a phosphorus bioavailability surplus. If the measured pH is 4.7 and E0 is 5.0, then the soil environmental deficit index B = (4.7-5.0) / 5.0 × 100% = -6%, indicating soil acidification; if the measured phosphatase activity is 1.0 μmol / h / g soil and T0 is 1.0 μmol / h / g soil, then the nutrient transformation activity deficit index B = (1.1-1.0) / 1.0 × 100% = 10%, indicating a surplus in phosphorus transformation activity; if the measured nifH gene abundance is 8.0 × 10⁻⁶... 5 Copy / g, M0 is 1.0 × 10 6 If the microbial functional surplus / deficit index is M = (8.0 × 10^6 copies / g soil), then the microbial functional surplus / deficit index is M = (8.0 × 10^6 copies / g soil). 5 -1.0×10 6 ) / 1.0×10 6 ×100%=-20% indicates insufficient function of nitrogen-fixing microorganisms.

[0035] In this embodiment, the dynamic adjustment of the weights of each dimension according to the growth stage of the trees includes: When the trees are in the young forest stage, the weight distribution ratio of each dimension is 13-22:27-55:5-18:9-21:8-20; When the trees are in the mid-forest stage, the weight distribution ratio of each dimension is 15-25:20-45:8-22:10-25:13-28; When the trees are in the mature stage, the weight distribution ratio of each dimension is 10-25:18-35:6-20:15-30:18-35. The weights of each dimension include the weights of nutrient content, bioavailability, soil environment, nutrient transformation activity, and microbial function.

[0036] Specifically, when the target forest is in the sapling stage, the trees need a large amount of directly absorbable nutrients for growth. At this time, the weights of nutrient content, bioavailability, and microbial function are allocated as follows: 13-22:27-55:5-18:9-21:8-20. The focus is on the actual nutrient content in the soil and the available nutrients to ensure the initial growth needs of the trees.

[0037] When trees enter the mid-forest stage, their growth rate slows down and their demand for nutrients tends to stabilize. The role of microorganisms in nutrient transformation gradually becomes more prominent. At this time, the weights of each dimension are adjusted to 15-25:20-45:8-22:10-25:13-28 to increase the importance of soil environmental conditions, nutrient transformation activity, and microbial function. By improving soil environmental conditions, enzyme transformation activity and microbial function can be enhanced to promote the transformation of unavailable nutrients into available ones.

[0038] When trees are in the mature stage, their growth slows down and their direct demand for nutrients decreases. However, the role of soil microorganisms in maintaining nutrient cycling is crucial. At this time, the weight distribution is 10-25:18-35:6-20:15-30:18-35, which significantly increases the weight of nutrient conversion activity and microbial function dimensions. By relying on enzymes and microorganisms to decompose organic nutrients and convert them into nutrients, the long-term productivity of the forest land can be maintained.

[0039] In this embodiment, the formula for calculating the comprehensive profit and loss index I is as follows: ,in , , These are the weights for nutrient content, bioavailability, soil environment, nutrient transformation activity, and microbial function. The nutrient surplus or deficit status of forest land is determined based on the comprehensive profit and loss index I, which includes comparing the comprehensive profit and loss index I with a preset threshold to determine whether it is a nutrient surplus, nutrient balance or nutrient deficit.

[0040] Specifically, when calculating the comprehensive profit and loss index I, if the nutrient content profit and loss index S = -15%, the bioavailability profit and loss index B = -8%, the soil environment E = -4%, the nutrient transformation activity T = 9%, and the microbial function profit and loss index M = 5% for a target forest land in the mid-forest stage, the corresponding weights are... =0.18、 =0.43、 =0.09、 =0.13、 =0.17, then I=(-15)×0.18+(-8)×0.43+(-4)×0.09+9×0.13+5×0.17=-2.7-3.44-0.36+1.17+0.85=-4.48%.

[0041] The preset thresholds are: I ≥ 5% is considered a nutrient surplus, -5% ≤ I < 5% is considered a nutrient balance, and I < -5% is considered a nutrient deficit. In the above case, I = -4.48% falls between -5% and 5%, indicating that the forest land is nutrient balanced. No large amount of fertilization is needed; simply maintaining the existing soil conditions is sufficient to avoid environmental problems caused by excessive fertilization.

[0042] In this embodiment, generating a personalized fertilization plan based on nutrient surplus / deficit status and deficiency type includes: When nitrogen deficiency is identified, it is recommended to apply slow-release nitrogen fertilizer. The amount of slow-release nitrogen fertilizer should be calculated based on the nutrient content baseline, measured total nitrogen content, ammonium nitrogen content, nitrate nitrogen content, alkaline available nitrogen content, soil bulk density, soil layer thickness, and safety factor. When phosphorus deficiency is identified, it is recommended to apply superphosphate in combination with bio-phosphorus-solubilizing bacteria fertilizer. The amount of superphosphate or calcium magnesium phosphate fertilizer should be calculated based on the bioavailability benchmark, measured total phosphorus content, available phosphorus content, phosphorus content extracted by calcium chloride, phosphorus content extracted by citric acid, phosphorus content extracted by phytase, phosphorus content extracted by hydrochloric acid, soil bulk density, soil layer thickness, and safety factor, and the amount of bio-phosphorus-solubilizing bacteria fertilizer should be determined. When it is determined that the microbial function is insufficient, it is recommended to apply compound bio-fertilizer, and determine the dosage and application time of the compound bio-fertilizer. The application time is the beginning of the wet season.

[0043] Specifically, when nitrogen deficiency is identified, it is recommended to apply slow-release nitrogen fertilizer. The dosage should be calculated based on the measured total nitrogen content, ammonium nitrogen content, nitrate nitrogen content, alkaline available nitrogen content, soil bulk density, soil layer thickness (0-40cm), and a safety factor of 1.2 in the target forest land, to ensure that the supplemented nitrogen can meet the needs of the trees and will not be lost quickly.

[0044] When phosphorus deficiency is identified, it is recommended to apply superphosphate in combination with bio-phosphorus-solubilizing bacteria fertilizer. The amount of superphosphate should be calculated based on the measured total phosphorus content, available phosphorus content, phosphorus content extracted by calcium chloride, phosphorus content extracted by citric acid, phosphorus content extracted by phytase, phosphorus content extracted by hydrochloric acid, soil bulk density, soil layer thickness, and safety factor, based on the bioavailability baseline value B0. At the same time, the amount of bio-phosphorus-solubilizing bacteria fertilizer should be determined according to the number of soil microorganisms. The bio-phosphorus-solubilizing bacteria can enhance the fertilization effect by improving phosphorus availability.

[0045] When it is determined that the microbial function is insufficient, it is recommended to apply compound bio-fertilizer. The amount of fertilizer should be determined according to the microbial function surplus / deficient index M. It should be applied at the beginning of the wet season, when the soil moisture is suitable for the survival and reproduction of microorganisms, which can quickly improve the soil microbial function and promote nutrient conversion.

[0046] Please see Figure 2 The present invention also provides a forestry precision fertilization decision system based on soil nutrient balance, comprising: The data acquisition module is used to collect soil samples and forest environmental data; The indicator analysis module is used to receive data, calculate soil nutrient content, bioavailability, and abundance of microbial functional genes, and output the calculation results. The profit and loss evaluation module is used to receive calculation results, calculate the comprehensive profit and loss index, determine the nutrient profit and loss status, and transmit the determination results. The decision output module is used to receive the judgment results and generate personalized fertilization plans based on the judgment results.

[0047] Specifically, the data acquisition module collects soil samples and forest environment data in a certain artificial forest (target forest land), the automatic soil sampling device collects soil samples from 0 to 40 cm in an S-shaped layout, the multi-parameter sensor group monitors soil pH, temperature and humidity in real time, and the sample preservation unit preserves samples for different purposes at 4℃ and -80℃ respectively.

[0048] The indicator analysis module receives information from the data acquisition module and works in conjunction with laboratory testing equipment such as Kjeldahl nitrogen analyzer, high-throughput sequencer, and fluorescence microplate reader. After receiving the test data, it automatically calculates the soil nitrogen, phosphorus, and potassium content, available nutrient bioavailability, and microbial functional gene abundance, and outputs the calculation results to the profit and loss evaluation module.

[0049] The profit and loss evaluation module receives the calculation results, substitutes them into the comprehensive profit and loss index formula to calculate the I value, compares it with the preset threshold to determine the nutrient profit and loss status, and transmits the determination result to the decision output module.

[0050] Based on the judgment results, the decision output module generates personalized fertilization plans that include fertilizer type, dosage, application method, and timing for situations such as nitrogen deficiency, phosphorus deficiency, or insufficient microbial function.

[0051] The dynamic adjustment module receives the latest nutrient balance generated by the profit and loss evaluation module based on the soil monitoring results of the year. Combined with the current growth stage of the trees, it adjusts the personalized fertilization plan in real time. The adjustment includes fertilizer type, nutrient content ratio, dosage, application method and application time.

[0052] The entire system streamlines the process from data collection to decision output, reducing manual intervention and human error, while improving the efficiency of precision fertilization decisions and making fertilization plans more aligned with actual needs.

[0053] In this embodiment, the data acquisition module includes an automatic soil sampling device, a multi-parameter sensor group, and a sample preservation unit; wherein, the automatic soil sampling device can control the sampling depth and the amount of sample taken at one time; the multi-parameter sensor group is used to monitor various environmental parameters such as soil pH, temperature and humidity in real time, and to collect forest environmental data; the sample preservation unit integrates a 4℃ cold storage chamber and a -80℃ freezer chamber.

[0054] Specifically, when the automatic soil sampling device in the data acquisition module is working in the target forest area, the sampling depth can be set to 0-40cm as needed, and the single sampling amount can be controlled to 500g, so as to ensure that the collected soil samples are representative and meet the requirements of subsequent testing.

[0055] Multi-parameter sensor arrays are installed in different areas of the forest to monitor environmental parameters such as soil pH, temperature, and humidity in real time, while also recording environmental data such as forest elevation, slope, and aspect, providing basic information for subsequent classification of tree growth stages and construction of a profit and loss evaluation system.

[0056] The sample preservation unit integrates a 4°C refrigerated compartment for storing soil samples containing enzymes whose activity is to be measured, preventing a decrease in enzyme activity; and a -80°C frozen compartment for storing soil samples containing microbial samples to be analyzed, avoiding degradation of microbial functional genes and ensuring the accuracy of subsequent test data.

[0057] The collaborative work of all components ensures the standardization of sampling, the real-time nature of environmental data, and the validity of samples, providing fundamental support for the accurate operation of subsequent modules.

[0058] In this embodiment, the index analysis module is linked with laboratory testing equipment, which preferably includes a Kjeldahl nitrogen analyzer, a high-throughput sequencer, and a fluorescence microplate reader. The index analysis module is used to receive the test data provided by the laboratory testing equipment, automatically calculate the soil nutrient content, bioavailability, and abundance of microbial functional genes, and output the calculation results to the profit and loss evaluation module.

[0059] Specifically, the index analysis module works in conjunction with laboratory testing equipment. After the Kjeldahl nitrogen analyzer measures the total nitrogen content in the soil, it transmits the data to the index analysis module. The high-throughput sequencer completes the sequencing of microbial functional genes, obtains gene abundance data, and sends it to the module. The fluorescence microplate reader detects soil enzyme activity and feeds the results back to the module.

[0060] After receiving these detection data, the indicator analysis module automatically calculates the content of soil nutrients such as total nitrogen, total phosphorus, and total potassium, the bioavailability of available nutrients such as available iron and available manganese, and the abundance of microbial functional genes such as nitrogen-fixing gene nifH. This ensures that the calculation process is accurate and efficient, reducing human error. The calculation results are then output to the profit and loss evaluation module to provide data support for subsequent profit and loss determination.

[0061] In this embodiment, the system further includes: The dynamic adjustment module receives the latest nutrient balance status generated by the profit and loss evaluation module based on the annual soil monitoring results of the target forest land, and adjusts the personalized fertilization plan in real time in combination with the current growth stage of the trees. The adjustment includes fertilizer type, nutrient content ratio, dosage, application method and application time.

[0062] Specifically, the dynamic adjustment module conducts forest nutrient monitoring and fertilization program adjustments on an annual cycle. Every spring, sampling points are set up using an S-shaped layout consistent with the initial sampling of the target forest land. Unsaturated soil samples and mixed soil samples from the 0-40cm soil layer are collected using an automatic soil sampling device. Multi-parameter sensor groups simultaneously collect environmental data such as soil temperature, humidity, and pH value for the year. The mixed soil sample is divided into three parts according to the previous sampling method and stored in a 4℃ cold storage and a -80℃ freezer, respectively.

[0063] The collected data and samples are transmitted to the index analysis module, where soil nutrient content, bioavailability, soil enzyme activity, and abundance of microbial functional genes are measured using a Kjeldahl nitrogen analyzer, atomic absorption spectrometer, fluorescence microplate reader, and high-throughput sequencer, and the corresponding index values ​​are calculated. Subsequently, the profit and loss evaluation module receives these index values, updates the weights of each dimension based on the current year's forest growth stage, calculates the comprehensive profit and loss index for the year, and determines the latest nutrient profit and loss status and deficiency type.

[0064] The dynamic adjustment module receives the latest judgment results from the profit and loss evaluation module and compares them with the previous year's fertilization plan and the trend of forest nutrient changes to adjust the fertilization plan. If the previous year was a nitrogen deficit and slow-release nitrogen fertilizer was applied, and the current year is a phosphorus deficit, the plan is adjusted to a combination of superphosphate and biological phosphorus-solubilizing fertilizer. Based on the current year's bioavailability benchmark values, the total phosphorus content, soil bulk density, soil layer thickness, and safety factor are measured, and the fertilizer dosage is recalculated. The application period still prioritizes the early wet season. The adjusted personalized fertilization plan is output by the decision output module to ensure that the annual plan is accurately matched with the current nutrient status of the forest and the needs of forest growth, avoiding fertilization deviations caused by long-term use of fixed plans and maintaining the continuous productivity of the forest.

[0065] In summary, this invention obtains basic information about the target forest land to divide the tree growth stages, collects and processes soil and microbial samples to determine multi-dimensional indicators of soil nutrients, microbial functional genes, soil environment, and enzyme activity. It constructs a three-dimensional profit and loss evaluation index system that uses forest land data to determine benchmark values, calculates a single-dimensional profit and loss index, and dynamically adjusts weights according to the tree growth stages. Furthermore, it calculates a comprehensive profit and loss index to determine the nutrient profit and loss status and generates personalized fertilization plans. This effectively solves the problems of insufficient or excessive fertilization caused by existing methods that rely solely on the total amount or single available form of soil nutrients, ignore soil environmental conditions, and fail to couple microorganisms with nutrient bioavailability. It avoids fertilizer waste and environmental problems such as soil acidification, adapts to the needs of multi-generational continuous planting forest land, maintains forest land productivity, and achieves precise fertilization and sustainable management of trees.

[0066] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for precision fertilization decision-making for forest trees based on soil nutrient balance, characterized in that, include: Obtain basic information about the target forest land and classify the growth stages of trees based on this information; Soil and microbial samples were collected and then processed separately. The study measured multiple indicators, including soil nutrient indicators, bioavailability indicators, soil environmental indicators, nutrient transformation activity indicators, and microbial functional gene indicators. A five-dimensional profit and loss evaluation index system is constructed, which specifically includes: determining the benchmark values ​​of each dimension based on the reference forest land data, calculating the profit and loss index of each dimension, and dynamically adjusting the weight of each dimension according to the growth stage of the trees; Calculate the comprehensive profit and loss index, and determine the nutrient profit and loss status of the forest land based on the comprehensive profit and loss index; Personalized fertilization plans are generated based on nutrient balance and deficiency type. These plans include fertilizer type, nutrient content ratio, dosage, application method, and application time.

2. The forestry precision fertilization decision-making method based on soil nutrient balance as described in claim 1, characterized in that, The collection of soil and microbial samples includes: setting up sampling points in the target forest area using the S-shaped sampling method, and collecting undisturbed soil samples and mixed soil samples with a soil depth of 0-40cm at the sampling points; the undisturbed soil samples are used to determine soil environmental indicators; the sample processing includes dividing the mixed soil samples into a first mixed soil sample, a second mixed soil sample, and a third mixed soil sample. The first mixed soil sample is used to determine soil nutrient content and bioavailability, the second mixed soil sample is used to determine soil enzyme activity, and the third mixed soil sample is used for microbial sample analysis.

3. The forestry precision fertilization decision-making method based on soil nutrient balance as described in claim 1, characterized in that, The multidimensional indicators measured include: The determination of soil nutrient indicators includes the content of total nitrogen, total phosphorus, total potassium, total sodium, total calcium, total magnesium, total sulfur, and total selenium in the soil; The determination of soil nutrient bioavailability indicators includes soil ammonium nitrogen, nitrate nitrogen, alkaline available nitrogen, available phosphorus, phosphorus extracted by calcium chloride, phosphorus extracted by citric acid, phosphorus extracted by phytase, phosphorus extracted by hydrochloric acid, available potassium, as well as the contents of available molybdenum, available silicon, available iron, available manganese, available zinc and available boron; The determination of soil environmental indicators includes soil temperature, humidity, pH value, bulk density, porosity and maximum water holding capacity; The determination of soil nutrient transformation activity indicators includes the activities of β-glucosidase, cellobiase, protease, N-acetylglucosidase and phosphatase. The determination of microbial functional gene indicators included measuring the abundance of nitrogen-fixing gene nifH, denitrification genes nirK, nirS, nosZ, and norB, nitrate-reducing bacteria gene narG, ammonia-oxidizing genes AOA and AOB, phosphate monoester degradation genes phoD and phoC, inorganic phosphorus-dissolving bacteria gene pqqC, cellulose degradation genes fungcbhIR and GH74, and β-glucosidase gene bg1.

4. The forestry precision fertilization decision-making method based on soil nutrient balance as described in claim 1, characterized in that, The construction of the five-dimensional profit and loss evaluation index system includes: The baseline values ​​for each dimension were determined, including the baseline values ​​for nutrient content S0, bioavailability B0, soil environment E0, nutrient transformation activity T0, and microbial function M0. The baseline values ​​were obtained based on data from healthy forest land with the same region, soil type, and no continuous planting. The calculation of single-dimensional profit and loss indices includes: nutrient content profit and loss index S = (measured nutrient content - S0) / S0 × 100%; bioavailability profit and loss index B = (measured available nutrient content - B0) / B0 × 100%; soil environmental baseline value E = (measured environmental index value - E0) / E0 × 100%; nutrient conversion activity baseline value T = (measured nutrient conversion enzyme activity - T0) / T0 × 100%; and microbial function profit and loss index M = (measured functional gene abundance - M0) / M0 × 100%.

5. The forestry precision fertilization decision-making method based on soil nutrient balance as described in claim 1, characterized in that, Based on the characteristics of nutrient requirements at different growth stages of trees, the dynamic adjustment of the weights of each dimension according to the growth stage of trees includes: When the trees are in the young forest stage, the weight distribution ratio of each dimension is 13-22:27-55:5-18:9-21:8-20; When the trees are in the mid-forest stage of growth, the weight distribution ratio of each dimension is 15-25:20-45:8-22:10-25:13-28; When the trees are in the mature stage, the weight distribution ratio of each dimension is 10-25:18-35:6-20:15-30:18-35. The weights of each dimension include the weights of nutrient content, bioavailability, soil environment, nutrient transformation activity, and microbial function.

6. The forestry precision fertilization decision-making method based on soil nutrient balance as described in claim 1, characterized in that, The formula for calculating the comprehensive profit and loss index I is as follows: ,in , , These are the weights for nutrient content, bioavailability, soil environment, nutrient transformation activity, and microbial function. The nutrient surplus or deficit status of forest land is determined based on the comprehensive profit and loss index I, which includes comparing the comprehensive profit and loss index I with a preset threshold to determine whether it is a nutrient surplus, nutrient balance or nutrient deficit.

7. The forestry precision fertilization decision-making method based on soil nutrient balance as described in claim 1, characterized in that, The generation of personalized fertilization plans based on nutrient surplus / deficit status and deficiency type includes: When nitrogen deficiency is identified, it is recommended to apply slow-release nitrogen fertilizer. The amount of slow-release nitrogen fertilizer should be calculated based on the nutrient content baseline, measured total nitrogen content, ammonium nitrogen content, nitrate nitrogen content, alkaline available nitrogen content, soil bulk density, soil layer thickness, and safety factor. When phosphorus deficiency is identified, it is recommended to apply superphosphate in combination with bio-phosphorus-solubilizing bacteria fertilizer. The amount of superphosphate or calcium magnesium phosphate fertilizer should be calculated based on the bioavailability benchmark, measured total phosphorus content, available phosphorus content, phosphorus content extracted by calcium chloride, phosphorus content extracted by citric acid, phosphorus content extracted by phytase, phosphorus content extracted by hydrochloric acid, soil bulk density, soil layer thickness, and safety factor, and the amount of bio-phosphorus-solubilizing bacteria fertilizer should be determined. When it is determined that the microbial function is insufficient, it is recommended to apply compound bio-fertilizer, and determine the dosage and application time of the compound bio-fertilizer. The application time is the beginning of the wet season.

8. A forestry precision fertilization decision system based on soil nutrient balance, applied to the forestry precision fertilization decision method based on soil nutrient balance as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect soil samples and forest environmental data; The indicator analysis module is used to receive data, calculate soil nutrient content, bioavailability, and abundance of microbial functional genes, and output the calculation results. The profit and loss evaluation module is used to receive calculation results, calculate the comprehensive profit and loss index, determine the nutrient profit and loss status, and transmit the determination results. The decision output module is used to receive the judgment results and generate personalized fertilization plans based on the judgment results.

9. The forestry precision fertilization decision system based on soil nutrient balance as described in claim 8, characterized in that, The data acquisition module includes an automatic soil sampling device, a multi-parameter sensor group, and a sample preservation unit. The automatic soil sampling device can control the sampling depth and the amount of samples taken at one time. The multi-parameter sensor group is used to monitor various environmental parameters such as soil pH, temperature, and humidity in real time, and to collect forest environmental data. The sample preservation unit integrates a 4℃ cold storage chamber and a -80℃ freezer chamber.

10. The forestry precision fertilization decision system based on soil nutrient balance as described in claim 8, characterized in that, The index analysis module is linked with laboratory testing equipment, which preferably includes a Kjeldahl nitrogen analyzer, an atomic absorption spectrometer, a fluorescence microplate reader, and a high-throughput sequencer. The index analysis module receives the test data provided by the laboratory testing equipment, automatically calculates soil nutrient content, bioavailability, and abundance of microbial functional genes, and outputs the calculation results to the profit and loss evaluation module.

11. The forestry precision fertilization decision system based on soil nutrient balance as described in claim 8, characterized in that, The system also includes: The dynamic adjustment module receives the latest nutrient balance status generated by the profit and loss evaluation module based on the annual soil monitoring results of the target forest land, and adjusts the personalized fertilization plan in real time in combination with the current growth stage of the trees. The adjustment includes fertilizer type, nutrient content ratio, dosage, application method and application time.

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