Method for optimizing productivity of man-made Chinese fir forest under multi-factor cooperative driving

Abstract

The invention relates to a method for optimizing productivity of a man-made Chinese fir forest under multi-factor cooperative driving. The method comprises the following steps: firstly, regularly collecting soil samples at different forest age stages, and comprehensively measuring physicochemical indexes, microbial community structures and enzyme activity to realize dynamic diagnosis of soil environment quality and accurate identification of limiting factors; based on the diagnosis result, the interaction relationship among soil nutrients, microorganisms and enzyme activity is analyzed, and a multi-factor cooperative regulation strategy including nutrient precise management, microbial agent inoculation and biological activator use is formulated; directional optimization is implemented by means of organic and inorganic fertilizer combined application, pH and humidity adjustment and the like, and soil properties and forest growth indexes are continuously monitored in the execution process; and finally, dynamically adjusting a management strategy according to a multi-dimensional evaluation result, constructing a synergistic effect model by using a structural equation model or a machine learning algorithm, quantifying the contribution degree of each factor, and realizing iterative optimization of the strategy.

Description

Technical Field

[0001] This invention belongs to the field of Chinese fir plantations, specifically a method for optimizing the productivity of Chinese fir plantations driven by multiple factors. Background Technology

[0002] As an important fast-growing timber species in my country, the sustainable improvement of the productivity of Chinese fir plantations is one of the core objectives of forestry research. It is well known that forest soil is the material basis for tree growth, and its environmental quality directly determines stand productivity. Traditional Chinese fir plantation management theories emphasize that soil nutrient status, especially the supply of macroelements such as nitrogen, phosphorus, and potassium, is a key factor affecting tree growth. Therefore, current mainstream management practices mainly rely on the regular application of chemical fertilizers to replenish soil nutrients, aiming to directly meet the nutritional needs of Chinese fir at various growth stages.

[0003] However, a growing body of research indicates that soil is a complex ecosystem comprised of multiple factors, including physicochemical properties, microbial communities, and enzyme activity, with close interactions among these components. Nutrient availability and cycling processes depend not only on their total quantity but also on the driving force of soil microorganisms (such as bacteria and fungi) and the catalytic regulation of soil enzymes (such as urease and phosphatase). Microorganisms are the "executors" of nutrient transformation, while enzymes are the "catalysts" of biochemical reactions; together, they constitute the intrinsic driving force of soil nutrient transformation.

[0004] The existing management model, dominated by single-agent chemical fertilization, has significant drawbacks: it often focuses only on the direct input of nutrients while neglecting the synergistic improvement of soil biological activity and nutrient cycling efficiency. This extensive management approach makes it difficult to maintain the diversity and stability of soil microbial communities, potentially leading to suppressed soil enzyme activity, broken nutrient cycling chains, and problems such as low fertilizer utilization efficiency and forest land degradation. In other words, current technology lacks a means to systematically and synergistically regulate soil "abiotic" factors (nutrients) and "biotic" factors (microorganisms, enzymes), thus failing to fundamentally achieve the continuous optimization of Cunninghamia lanceolata plantation productivity. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a multi-factor synergistic-driven method for optimizing the productivity of Chinese fir plantations. This method aims to solve the problems of low fertilizer utilization, imbalance of soil ecosystem functions, and difficulty in continuously improving forest productivity caused by neglecting the synergistic effect of soil biological factors and nutrient management in existing technologies.

[0006] A multi-factor synergistic method for optimizing the productivity of Chinese fir plantations includes the following steps:

[0007] S1. Comprehensive Soil Environment Diagnosis: Conduct comprehensive monitoring of soil at different growth stages of Chinese fir plantations, assess soil physicochemical properties, nutrient status and biological activity characteristics, and identify key limiting factors.

[0008] S2. Multi-factor synergistic regulation strategy planning: Based on the soil environment diagnosis results and combined with the growth requirements of Chinese fir, a multi-factor synergistic regulation strategy framework covering soil nutrients, microbial community and enzyme activity is formulated.

[0009] S3. Implementation of precise regulation of soil nutrients: According to the strategic plan, targeted nutrient supplementation and adjustment are carried out on the soil of Chinese fir plantations to meet the nutrient needs of different growth stages.

[0010] S4. Targeted optimization of microbial community and enzyme activity: Optimize the structure of soil microbial community, enhance the activity of key enzymes, and promote nutrient cycling and utilization through biological or non-biological means.

[0011] S5. Optimize the dynamic implementation and monitoring of strategies: Apply the planned collaborative regulation strategies to the management practice of Chinese fir plantations, and continuously monitor the implementation effect and stand response;

[0012] S6. Effect Evaluation and Strategy Iteration and Optimization: Regularly evaluate the effect of the optimization strategy on the growth of Chinese fir and the improvement of the soil environment, and dynamically adjust the strategy based on the evaluation results to achieve continuous improvement in productivity.

[0013] Preferably, step S1 specifically includes:

[0014] S11. Regularly collect soil samples from Chinese fir plantations at different ages;

[0015] S12. Determine the physicochemical indicators of soil pH, organic matter content, total nitrogen, total phosphorus and available potassium.

[0016] S13. Analyze the diversity of soil microbial community structure and enzyme activity levels;

[0017] S14. Conduct a comprehensive assessment of soil environmental quality and identify nutrient limiting factors and their dynamic changes.

[0018] Preferably, in step S2, the specific measures include:

[0019] S21. Determine the interactions between soil nutrients, microbial communities, and enzyme activity;

[0020] S22. Identify key factors and synergistic pathways affecting the growth of Chinese fir;

[0021] S23. Develop a comprehensive strategy that includes nutrient management, microbial regulation, and enzyme activity enhancement.

[0022] Preferably, in step S3, the specific measures include:

[0023] S31. Based on the soil nutrient diagnosis results, determine the application rates and proportions of nitrogen, phosphorus, and potassium;

[0024] S32. Improve soil fertility by using a combination of organic and chemical fertilizers;

[0025] S33. For specific growth stages or nutrient-limited conditions, implement localized intensive fertilization or foliar spraying.

[0026] Preferably, in step S4, the specific measures include:

[0027] S41. Inoculate with beneficial microbial agents;

[0028] S42. Adjust soil pH and moisture to promote microbial activity;

[0029] S43. Add bioactivators to enhance the activity of key enzymes and accelerate nutrient conversion. Key enzymes include urease and phosphatase.

[0030] Preferably, in step S5, the specific measures include:

[0031] S51. The established coordinated control strategies will be applied to the management of Chinese fir plantations as planned.

[0032] S52. Regularly monitor changes in soil physicochemical properties, microbial community structure, and enzyme activity;

[0033] S53. Observe the response of the Chinese fir growth indicators, including tree height, diameter at breast height and volume.

[0034] Preferably, in step S6, the evaluation indicators include:

[0035] The increase in the growth of Chinese fir trees;

[0036] The extent of the increase in soil nutrient content;

[0037] The degree of improvement in the diversity of microbial community structure;

[0038] The effect of enhancing enzyme activity levels.

[0039] Preferably, in step S6, the dynamic adjustment of the strategy includes the following methods:

[0040] Adjust the amount of nutrients applied or the timing of fertilization based on the assessment results;

[0041] Optimize the inoculation program for microbial agents or the dosage of bioactivators;

[0042] Improve agricultural management practices, including adjusting stand density and pruning methods.

[0043] Preferably, it also includes S7, constructing a multi-factor synergistic model:

[0044] Quantify the contribution of soil nutrients, microbial biomass, enzyme activity, and stand structure to the productivity of Chinese fir;

[0045] To reveal the interaction mechanisms and key driving factors among multiple factors;

[0046] This provides a theoretical basis for formulating and optimizing coordinated regulation strategies.

[0047] Preferably, step S7 further includes:

[0048] Utilize structural equation modeling (SEM) or machine learning algorithms to construct predictive models;

[0049] The model was validated and optimized using an independent dataset;

[0050] The model was applied to the actual management of Chinese fir plantations to guide precise improvement of productivity.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] By establishing a comprehensive monitoring system covering soil physicochemical properties, microbial community structure, and key enzyme activities, soil limiting factors during the growth of Chinese fir can be accurately identified and dynamically tracked. This systematic diagnosis provides a scientific basis for subsequent targeted interventions, avoiding the problems of blind fertilization or insufficient regulation caused by data gaps in traditional management.

[0053] By combining organic and inorganic fertilizers, inoculating with beneficial microorganisms, and applying bioactivators, the availability and cycling efficiency of soil nutrients were significantly improved. This enabled Chinese fir to receive a balanced supply of nutrients at different growth stages, while also enhancing soil biological activity and promoting the nutrient conversion process driven by key enzymes such as urease and phosphatase, thereby improving the micro-ecological environment of Chinese fir roots.

[0054] By constructing a closed-loop management mechanism of "monitoring-regulation-evaluation-iteration", the optimization strategy can be dynamically adjusted according to the changes in forest stand response and soil indicators. By combining a multi-factor collaborative model constructed with structural equation modeling or machine learning algorithms, a quantitative analysis of the interaction mechanism between Chinese fir growth and soil environment is further realized, providing a sustainable optimization path and theoretical support for long-term productivity improvement. Attached Figure Description

[0055] Figure 1 This is a flowchart of the method of the present invention;

[0056] Figure 2a and Figure 2b This is a diagram showing the main nutrient components of the soil in a pure plantation of Chinese fir (Cunninghamia lanceolata) according to the present invention.

[0057] Figure 3a and Figure 3b This is a diagram showing the abundance of soil microorganisms at the phylum level, as presented in this invention.

[0058] Figure 4a , Figure 4b , Figure 4c and Figure 4d This is a diagram showing the α-diversity of soil fungal microbial communities in this invention.

[0059] Figure 5a , Figure 5b , Figure 5c and Figure 5d This is a diagram showing the α-diversity of soil bacterial microbial communities in this invention.

[0060] Figure 6a and Figure 6b This is a β-diversity diagram of soil microbial communities in this invention;

[0061] Figure 7a , Figure 7b , Figure 7c , Figure 7d , Figure 7e , Figure 7f , Figure 7g , Figure 7h , Figure 7i , Figure 7j and Figure 7k This is a diagram illustrating the changes in enzyme activity in artificial pure soil from Chinese fir trees, as described in this invention.

[0062] Figure 8 This invention provides a stochastic forest model for soil nutrients in a pure plantation of Chinese fir.

[0063] Figure 9 This diagram illustrates the pathway by which nutrient cycling in a pure Chinese fir plantation affects stand volume. Detailed Implementation

[0064] 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.

[0065] like Figure 1 As shown:

[0066] Example 1: A method for optimizing the productivity of Chinese fir plantations driven by multiple factors, comprising the following steps:

[0067] S1. Comprehensive Soil Environment Diagnosis: Conduct comprehensive monitoring of soil at different growth stages of Chinese fir plantations, assess soil physicochemical properties, nutrient status and biological activity characteristics, and identify key limiting factors.

[0068] S2. Multi-factor synergistic regulation strategy planning: Based on the soil environment diagnosis results and combined with the growth requirements of Chinese fir, a multi-factor synergistic regulation strategy framework covering soil nutrients, microbial community and enzyme activity is formulated.

[0069] S3. Implementation of precise regulation of soil nutrients: According to the strategic plan, targeted nutrient supplementation and adjustment are carried out on the soil of Chinese fir plantations to meet the nutrient needs of different growth stages.

[0070] S4. Targeted optimization of microbial community and enzyme activity: Optimize the structure of soil microbial community, enhance the activity of key enzymes, and promote nutrient cycling and utilization through biological or non-biological means.

[0071] S5. Optimize the dynamic implementation and monitoring of strategies: Apply the planned collaborative regulation strategies to the management practice of Chinese fir plantations, and continuously monitor the implementation effect and stand response;

[0072] S6. Effect Evaluation and Strategy Iteration and Optimization: Regularly evaluate the effect of the optimization strategy on the growth of Chinese fir and the improvement of the soil environment, and dynamically adjust the strategy based on the evaluation results to achieve continuous improvement in productivity.

[0073] As shown above, this method first identifies key limiting factors by comprehensively monitoring soil conditions at different growth stages of Chinese fir plantations. Based on this, a multi-factor synergistic regulation strategy framework is formulated in conjunction with the growth requirements of Chinese fir, covering comprehensive management objectives for soil nutrients, microbial communities, and enzyme activity.

[0074] Subsequently, by precisely implementing soil nutrient regulation and targeted optimization of microbial communities and enzyme activity, the efficiency of nutrient cycling and utilization was improved. The optimized strategy was continuously monitored during dynamic implementation to ensure that management measures matched the needs of different growth stages.

[0075] Ultimately, by regularly evaluating the effectiveness of the strategy in improving the growth of Chinese fir and the soil environment, dynamic adjustments and iterative optimizations are achieved. This closed-loop management process effectively promotes the continuous improvement of the productivity of Chinese fir plantations.

[0076] Example 2: This example is basically the same as the previous example, except that step S1 specifically includes:

[0077] S11. Regularly collect soil samples from Chinese fir plantations at different ages;

[0078] S12. Determine the physicochemical indicators of soil pH, organic matter content, total nitrogen, total phosphorus and available potassium.

[0079] S13. Analyze the diversity of soil microbial community structure and enzyme activity levels;

[0080] S14. Conduct a comprehensive assessment of soil environmental quality and identify nutrient limiting factors and their dynamic changes.

[0081] Specifically, in step S2, the specific measures include:

[0082] S21. Determine the interactions between soil nutrients, microbial communities, and enzyme activity;

[0083] S22. Identify key factors and synergistic pathways affecting the growth of Chinese fir;

[0084] S23. Develop a comprehensive strategy that includes nutrient management, microbial regulation, and enzyme activity enhancement.

[0085] Specifically, in step S3, the specific measures include:

[0086] S31. Based on the soil nutrient diagnosis results, determine the application rates and proportions of nitrogen, phosphorus, and potassium;

[0087] S32. Improve soil fertility by using a combination of organic and chemical fertilizers;

[0088] S33. For specific growth stages or nutrient-limited conditions, implement localized intensive fertilization or foliar spraying.

[0089] Specifically, in step S4, the specific measures include:

[0090] S41. Inoculate with beneficial microbial agents;

[0091] S42. Adjust soil pH and moisture to promote microbial activity;

[0092] S43. Add bioactivators to enhance the activity of key enzymes and accelerate nutrient conversion. Key enzymes include urease and phosphatase.

[0093] Specifically, in step S5, the specific measures include:

[0094] S51. The established coordinated control strategies will be applied to the management of Chinese fir plantations as planned.

[0095] S52. Regularly monitor changes in soil physicochemical properties, microbial community structure, and enzyme activity;

[0096] S53. Observe the response of the Chinese fir growth indicators, including tree height, diameter at breast height and volume.

[0097] Specifically, in step S6, the evaluation indicators include:

[0098] The increase in the growth of Chinese fir trees;

[0099] The extent of the increase in soil nutrient content;

[0100] The degree of improvement in the diversity of microbial community structure;

[0101] The effect of enhancing enzyme activity levels.

[0102] Specifically, in step S6, the methods for dynamically adjusting the strategy include:

[0103] Adjust the amount of nutrients applied or the timing of fertilization based on the assessment results;

[0104] Optimize the inoculation program for microbial agents or the dosage of bioactivators;

[0105] Improve agricultural management practices, including adjusting stand density and pruning methods.

[0106] Specifically, this also includes S7 and constructing a multi-factor synergistic model:

[0107] Quantify the contribution of soil nutrients, microbial biomass, enzyme activity, and stand structure to the productivity of Chinese fir;

[0108] To reveal the interaction mechanisms and key driving factors among multiple factors;

[0109] This provides a theoretical basis for formulating and optimizing coordinated regulation strategies.

[0110] Specifically, step S7 also includes:

[0111] Utilize structural equation modeling (SEM) or machine learning algorithms to construct predictive models;

[0112] The model was validated and optimized using an independent dataset;

[0113] The model was applied to the actual management of Chinese fir plantations to guide precise improvement of productivity.

[0114] As shown above, this method, by regularly collecting soil samples of different forest ages, systematically measuring physicochemical indicators, and analyzing microbial and enzyme activities, achieves dynamic assessment of soil environmental quality and precise identification of limiting factors. Based on this, the interaction relationships among soil nutrients, microbial communities, and enzyme activity are further clarified, and a comprehensive strategy including nutrient management, microbial regulation, and enzyme activity enhancement is formulated.

[0115] During implementation, precise ratios of nitrogen, phosphorus, and potassium nutrients, combined application of organic and chemical fertilizers, and the use of beneficial microorganisms, soil environment regulation, and bio-activators effectively improved soil fertility and nutrient conversion efficiency. Throughout the strategy's implementation, continuous monitoring of soil properties, microbial structure, enzyme activity, and Chinese fir growth indicators ensured the effectiveness and adaptability of the management measures.

[0116] Based on the evaluation results of multi-dimensional indicators, fertilization programs, microbial management measures, and stand structure were dynamically adjusted, achieving continuous optimization of the strategy. By constructing a multi-factor synergistic model, the contribution of each factor to productivity was quantified, revealing the key driving mechanisms and providing theoretical basis and practical guidance for the precise improvement of Cunninghamia lanceolata plantation productivity.

[0117] Experimental Example: Research Methods Based on Comprehensive Soil Environmental Diagnosis

[0118] Located in Fengshushan Forest Farm, Jingdezhen City, Jiangxi Province, this area has a mid-subtropical monsoon climate, with 1800-2000 hours of sunshine annually, 1650-1850 mm of annual precipitation, an average annual relative humidity of 79%, and an average annual temperature of 17.8℃. The experimental plot is at an altitude of 150m, with a landform of low mountains and hills. The soil type is red soil formed by the weathering of slate and granite, with a soil depth of up to 60cm. The vegetation type is mainly evergreen broad-leaved forest, with species including Chinese fir, Gleichenia linearis, Woodwardia japonica, Maesajaponica, Lindera aggregata, Urena lucidum, Rubus buergeri, and Smilax china.

[0119] In August 2022, three sample plots (5 years, 15 years, 20 years, and 30 years) were established in each of the young, middle-aged, near-mature, and mature Chinese fir plantations, totaling 12 plots. Each plot was 600 m² (30 m × 20 m projected area). Individual tree surveys were conducted in these 12 plots, and growth indicators such as tree species, diameter at breast height (DBH), tree height, branch height, and crown width were measured and recorded. Within each Chinese fir plantation plot, five sampling points were selected using the quincunx sampling method. After removing surface litter, soil samples (0–5 cm in diameter) were extracted using a soil drill (5 cm inner diameter) and placed in labeled sealed bags.

[0120] The samples were stored in a dry ice sealed box and brought back to the laboratory. After removing tree roots and impurities, all soil samples were sieved through a 2 mm sieve. One portion of the soil samples was stored at -80℃ for soil DNA extraction and microbial sequencing, while the other portion was air-dried to constant weight, ball-milled, and then sieved to <0.15 mm for soil physicochemical index analysis.

[0121] The measured indicators include (all results are expressed as the amount or mass of matrix hydrolysis or oxidation products produced per unit time and per unit mass of soil):

[0122] Soil pH (water-to-soil ratio 5:1), organic carbon (OC), dissolved organic carbon (DOC), carbon monoxide (ROC), total nitrogen (TN), nitrate nitrogen (NO3⁻-N), ammonium nitrogen (NH4⁺-N), hydrolyzable nitrogen (HN), total phosphorus (TP), available phosphorus (AP); microbial biomass carbon (MBC), nitrogen (MBN), phosphorus (MBP); soil enzyme activities: sucrose, β-1,4-glucosidase, cellulase, polyphenol oxidase. Henol Oxidase, urease, acid phosphatase, alkaline phosphatase, catalase, cellobiohydrolase, L-leucine aminopeptidase, and N-acetyl-β-D-glucosidase, etc.

[0123] Microbial community structure: Sequencing analysis of bacterial and fungal community composition:

[0124] DNA extraction and sequencing:

[0125] Microbial sequencing analysis of soil samples was performed by Beijing Biomarker Technologies Corporation. Total DNA was extracted from the samples using the DNeasy PowerSoil kit (QIAGEN, Netherlands) according to the manufacturer's instructions. DNA integrity and concentration were assessed using agarose gel electrophoresis combined with a Qubit 2.0 fluorometer and a NanoDrop ND-1000 spectrophotometer (Ingenieur, USA). The abundance of bacteria and fungi in the soil was determined using qPCR with primer sets 338F / 806R (5'-ACTCCTACGGGAGGCAGCA-3'; 5'-GGACTACHVGGGTWTCTAAT-3') and ITS4 / ITS5 (5'-TCCTCCGCTTATTGATATGC-3'; 5'-GGAAGTAAAAGTCGTAACAAGG-3'). The reactions were performed on an ABI 7500 (Applied Biosystems, USA). Plasmid DNA was obtained from clones using a small-scale preparation kit (Qiagen, Germantown, MD, USA). Standard curves were generated by amplifying the target region of the clone into the plasmid using a series of dilutions, with amplification efficiencies ranging from 91% to 100% and R² values ​​greater than 0.99. Each 25 μL reaction volume contained 12.5 μL of SYBR Premix ExTaq™ (Dalian Takara Bio Inc.), 0.5 μL of each primer (10 mM), and 1–10 nm of template DNA. PCR conditions were as follows: 95 °C pre-denaturation for 3 min, followed by 35 cycles (95 °C denaturation for 40 s, 54 °C annealing for 30 s, 72 °C extension for 40 s), and a final extension at 83 °C for 10 s. Each replicate qPCR assay was performed triplicate, and melting curve analysis was used to assess amplification specificity.

[0126] The volume of a single Chinese fir tree was calculated using a binary volume empirical formula (as shown below). The height of each tree was measured within a pure Chinese fir forest plot.

[0127]

[0128] In the formula: D is the volume of a single Chinese fir tree; H is the diameter at breast height (DBH); and D is the tree height.

[0129] Principal component analysis (PCA) and random forest model identified total nitrogen, ammonium nitrogen, and total phosphorus as the key limiting factors.

[0130] Data processing and analysis were performed in R software (v4.3.2). Analysis of variance (ANOVA) was used to test for differences in growth index data, soil nutrient data, and microbial biomass data. Principal component analysis (PCA) was performed using the R packages "FactoMineR (v2.6)" and "factoextra (v1.0.6)" to determine the dominant factors of soil nutrients: first, principal components with eigenvalues ​​greater than 1 were screened, and then the key contributing factors of each principal component were extracted (significance level p<0.05). Nonmetric multidimensional scaling (NMDS) was performed using the R package "vegan (v2.6-6)" based on the Bray-Curtis distance algorithm to analyze the characteristics of microbial community composition; similarity analysis (ANOSIM) and multivariate permutation analysis of variance (PERMANOVA) were combined to test the significance of community differences.

[0131] Based on existing literature and previous research, the growth of Chinese fir (Cunninghamia lanceolata) may be influenced by soil properties, enzyme activity, and microbial community. Following this theoretical framework, a partial least squares structural equation model (PLS-SEM) was constructed using the R package "plspm (v0.5.1)". Through variable selection and optimization, the mechanism of action of each factor on stand productivity was revealed. The reliability of latent variables was assessed using the mean variance extracted (AVE), and the model fit (GoF) verified the overall validity. All variables had AVE values ​​exceeding 0.5, CR values ​​exceeding 0.7, and R² values ​​exceeding 0.4, indicating that the measurement model has strong convergent validity.

[0132] The following results were obtained using the method described above:

[0133] Growth characteristics of Chinese fir plantations of different ages:

[0134] The growth dynamics of Chinese fir plantations showed significant differences with stand age (Table 1). The total stand volume increased with age, reaching its highest value in mature stands (15.66 ± 1.34 m³). The average volume per tree (m³) and the average annual volume increase (m³ / year) showed an age-related trend, with mature stands having the highest average volume per tree (0.115 ± 0.028 m³), ​​while middle-aged stands had the largest annual increase (0.84 ± 0.19 m³ / year), indicating that Chinese fir has high productivity in its early growth stages. Notably, the annual increase in near-mature stands (0.33 ± 0.14 m³ / year) was lower than that in middle-aged and mature stands. Morphological characteristics (diameter at breast height (DBH) and tree height) tended to stabilize after the middle-aged stage. The average DBH increased by approximately 185% from young to middle-aged stands, after which no significant difference was observed. Similarly, the tree height reached 8.45±0.52 m in the middle-aged forest, and then grew slowly (11.11±1.48 m in the mature forest), showing a typical gradual growth pattern of pure forests of the same age (p<0.05).

[0135] Table 1. Growth characteristics of pure plantations of Chinese fir:

[0136] Lin Ling Total volume Average volume Average annual growth in volume Average thoracic diameter Average tree height Standard age (yr) （m³） （m³） （m³） (cm) （m） 5a 0.11±0.04b 0.002±0.001c 0.02±0.01b 3.86±0.33b 3.22±0.15b 20a 12.65±2.86a 0.051±0.009bc 0.84±0.19a 11.01±0.67a 8.45±0.52a 30a 9.10±0.41a 0.098±0.021b 0.33±0.14ab 13.67±1.18a 10.17±0.40a 40a 15.66±1.34a 0.115±0.028a 0.53±0.04ab 14.06±1.24a 11.11±1.48a

[0137] Characteristics of soil nutrients and microbial biomass in plantations of Chinese fir of different ages:

[0138] Soil nutrient content is a key factor determining forest stand productivity. The soil nutrient characteristics of Chinese fir plantations exhibit a significant stage-specific variation pattern with stand age (Table 2). Soil pH and available phosphorus content showed no significant differences throughout the stand's lifespan. Total organic carbon, soluble organic carbon, carbon monoxide, and nitrate nitrogen contents initially increased and then decreased with stand age, peaking in the near-mature stage. Total nitrogen, ammonium nitrogen, hydrolyzable nitrogen, and total phosphorus contents showed an increasing trend (p < 0.05).

[0139] Table 2. Soil physicochemical properties of pure Chinese fir plantation land:

[0140] Lin Ling pH Total organic carbon (OC) Soluble Organic Carbon (DOC) Carbon monoxide ROC Total nitrogen (TN) Nitrate nitrogen NO3--N Ammonium nitrogen NH4+-N Hydrolyzed nitrogen (HN) Total Phosphorus TP Quick-acting phosphorus AP Standard age (yr) (g / kg) (mg / kg) (g / kg) (g / kg) (mg / kg) (mg / kg) (mg / kg) (g / kg) (mg / kg) 5a 5.04±0.04a 22.96±1.94b 33.47±1.53b 8.02±0.16b 1.83±0.05b 1.02±0.41b 21.19±2.89b 142.75±4.05b 0.41±0.03b 2.74±0.21a 15a 5.41±0.12a 22.13±0.52b 33.32±0.61b 9.46±0.49b 2.12±0.06ab 1.35±0.64b 35.52±0.72a 165.83±7.39ab 0.38±0.04b 3.12±0.33a 20a 4.88±0.10a 32.87±1.75a 42.73±2.22a 18.56±0.54a 2.51±0.15a 7.50±0.47a 36.36±6.27a 200.58±8.05a 0.42±0.02b 3.60±0.11a 30a 5.14±0.14a 26.43±1.28b 27.01±2.33b 10.35±0.96b 2.46±0.23a 3.10±1.42b 37.82±0.71a 174.00±10.88ab 0.70±0.13a 3.72±0.23a

[0141] Based on the principal component analysis results, it was found that ( Figure 2a and Figure 2b The first principal component (PC1) explained 51.29% of the variance, and the second principal component (PC2) contributed 21.26%, together reflecting 72.55% of the soil environmental heterogeneity. The physicochemical properties of soils in near-mature and mature forests differed significantly from those in other forest ages. This was mainly reflected in the significantly higher contents of ammonium nitrogen, hydrolyzable nitrogen, total nitrogen, and total phosphorus in the soils compared to other forest ages. Soils in young forests showed a strong positive correlation with pH and available phosphorus, while soils in near-mature forests were highly correlated with nitrate nitrogen, carbon monoxide, and total organic carbon. Principal component analysis showed that the PC1 axis was mainly driven by carbon and nitrogen cycle parameters (SOC, ROC, NO3--N), while the PC2 axis was closely related to the gradient changes in pH and AP (p < 0.05).

[0142] Figure 2a and Figure 2b Note: pH: acidity / alkalinity; NO3--N: nitrate nitrogen; NH4+-N: ammonium nitrogen; HN: hydrolyzable nitrogen; TN: total nitrogen; AP: available phosphorus; TP: total phosphorus; DOC: soluble organic carbon; ROC: carbon monoxide; OC: total organic carbon.

[0143] Soil microbial biomass in Chinese fir plantations showed significant changes with stand age (Table 3). Microbial biomass (MBC) initially increased and then decreased with age, peaking in the near-mature stage (487.33±22.09 mg / kg), significantly higher than other age stages. MBC content decreased in the mature stage (374.5±0.29 mg / kg), but remained significantly higher than that in young forests (1.71 times higher) and middle-aged forests (1.76 times higher). MBN content showed no significant difference among age groups, but gradually increased from young forests (32.90±6.00 mg / kg) to near-mature forests (60.00±5.10 mg / kg). Notably, after reaching its highest value in near-mature forests, MBN content decreased again to 39.49±7.57 mg / kg in the mature stage. MBP content showed a similar pattern to MBC, slightly decreasing in the mature stage (21.19±4.13 mg / kg). The MBP content in middle-aged forests (18.02±0.97 mg / kg) was at an intermediate level (p<0.05).

[0144] Table 3. Soil microbial biomass in monoculture plantations of Chinese fir:

[0145] Lin Ling Microbial biomass carbon Microbial biomass nitrogen Microbial biomass phosphorus Standard age (yr) (mg / kg) (mg / kg) (mg / kg) 5a 218.83±4.12c 32.90±6.00a 9.52±2.85b 15a 212.36±13.91c 35.38±5.10a 18.02±0.97ab 20a 487.33±22.09a 60.00±5.10a 25.65±2.81a 30a 374.5±0.29b 39.49±7.57a 21.19±4.13ab

[0146] Characteristics of soil microbial communities in Chinese fir plantations of different ages:

[0147] Abundance of dominant microbial species in forest soils of different ages (phylum and order level):

[0148] Ascomycota, Basidiomycota, and Sporotymycota are the main dominant fungal groups in the soil of Cunninghamia lanceolata plantations (at the phylum level, the standard for defining a dominant fungal group is a relative abundance >5%). Figure 3a and Figure 3b Ascomycota dominated all stands, with a relative abundance exceeding 80%. Basidiomycota also showed high abundance in all stands, especially in mature forests, where its relative abundance approached 20%. With increasing stand age, the abundance of Ascomycota decreased, while the abundance of Basidiomycota and Sporotyphidia increased. Proteobacteria, Acidobacteria, Actinobacteria, and Bacteroidetes were the main dominant bacterial groups in the soil of *Cunninghamia lanceolata* plantations. Proteobacteria dominated all stands, with a relative abundance exceeding 40%. Acidobacteria showed high abundance in all stands, especially in mature forests, where its relative abundance approached 30%. With increasing stand age, the abundance of Actinobacteria decreased; the abundance of Bacteroidetes increased; the abundance of Proteobacteria and Acidobacteria showed no significant change (p < 0.05).

[0149] Soil microbial diversity in forest land of different ages:

[0150] With increasing forest age, the Chao1 index and ACE index of soil fungal α diversity showed an "N"-shaped increasing trend. Figure 4a , Figure 4b , Figure 4c and Figure 4d In mature forests, the Chao1 and ACE indices were significantly higher than those of other forest ages, indicating the highest fungal species richness. The Shannon index showed an increasing trend, peaking in mature forests, where fungal community diversity was highest. The Simpson index showed no significant difference across the entire stand. Overall, increasing forest age led to a corresponding increase in soil fungal microbial α diversity (p < 0.05).

[0151] With increasing forest age, the Chao1 index, ACE index, and Shannnon index of soil bacterial α diversity indices showed a trend of first decreasing and then increasing. Figure 5a , Figure 5b , Figure 5c and Figure 5d All of these factors peaked during the mature forest stage, at which time bacterial species richness, community diversity, and evenness were highest. The Simpson index showed no significant difference across the entire stand. Overall, increasing stand age resulted in the lowest soil bacterial microbial α diversity in middle-aged forests (p < 0.05).

[0152] Nonmetric multidimensional scaling (NMDS) analysis was performed on fungal and bacterial communities in the soil based on Bray-Curtis distance, and the results are as follows: Figure 6a and Figure 6b As shown in the figure, the stress values ​​in the NMDS analysis were 0.1576 and 0.1345, respectively. For soil fungal β-diversity, young forest samples showed tight clustering, while middle-aged, near-mature, and mature forest samples exhibited a gradient distribution, indicating significant changes in fungal community structure with increasing forest age. Mature forest samples showed clear separation from other age groups, suggesting the formation of unique fungal communities at the mature forest stage. In contrast to fungal communities, in soil bacterial β-diversity, the distribution pattern of bacterial community samples showed partial overlap between young and near-mature forest samples, while near-mature and mature forest samples formed relatively independent clusters. This distribution pattern suggests that bacterial community succession may exhibit stage-specific characteristics, with significant transitions occurring between middle-aged and near-mature forests. The stress values ​​(Stress < 0.2) in both fungal and bacterial NMDS analyses indicate that two-dimensional ordination can effectively reflect the community differences in the original data. The stress value of the fungal community was slightly higher than that of the bacterial community, suggesting that the structural variation of the fungal community may be more complex (p < 0.05).

[0153] Characteristics of changes in soil enzyme activity in plantations of Chinese fir of different ages:

[0154] Soil enzyme activity in plantation sites of Chinese fir was significantly affected by forest age. Figure 7a , Figure 7b , Figure 7c , Figure 7d , Figure 7e , Figure 7f , Figure 7g , Figure 7h , Figure 7i , Figure 7j and Figure 7k With increasing stand age, among soil C cycle-related enzymes, the activities of sucrase, catalase, and β-glucosidase showed a trend of first increasing and then decreasing, with significantly higher activities in the near-mature forest stage than in other stages; the activities of cellobiase, cellulase, and phenol oxidase showed no significant difference across all stand ages. Among soil N cycle-related enzymes, the activities of N-acetyl-β-glucosidase and urease showed a trend of first increasing and then decreasing, with significantly higher activities in the near-mature forest stage than in other stages; the activity of L-leucine-based peptidase showed a trend of first decreasing and then increasing, with significantly lower activities in the near-mature forest stage than in other stages. Among soil P cycle-related enzymes, the activity of alkaline phosphatase showed a trend of first decreasing and then increasing, with significantly lower activities in the near-mature forest stage than in other stages; the activity of acid phosphatase showed an increasing trend, with significantly higher activities in the mature forest stage than in other stages (P<0.05). Overall, the activities of soil C and N cycle-related enzymes increased with stand age, while the activities of P cycle-related enzymes showed no clear pattern.

[0155] Figure 7a , Figure 7b , Figure 7c , Figure 7d , Figure 7e , Figure 7f , Figure 7g , Figure 7h , Figure 7i , Figure 7j and Figure 7k Note: Sus: sucrase; CAT: catalase; βG: β-glucosidase; CBH: cellobiase; CEL: cellulase; PO: phenol oxidase; LAP: L-leucine peptidase; NAG: N-acetyl-β-glucosidase; Ur: urease; ALP: alkaline phosphatase; ACP: acid phosphatase.

[0156] Drivers of productivity: Correlation analysis of soil nutrients, microbial community, and enzyme activity:

[0157] According to the analysis of the random forest model, ( Figure 8 The contents of total nitrogen, ammonium nitrogen, total phosphorus, available phosphorus and carbon monoxide in soil nutrients have a significant promoting effect on the accumulation of forest productivity, while nitrate nitrogen has a significant inhibitory effect. This is consistent with the results of principal component analysis of soil nutrients. In near-mature forests and mature forests with higher contents of hydrolyzable nitrogen, ammonium nitrogen and total nitrogen, forest productivity is also correspondingly increased (p < 0.05).

[0158] Soil physicochemical characteristics can significantly affect the relative abundance of dominant soil microbial phyla, among which total nitrogen, hydrolyzable nitrogen, and soluble organic carbon content are the main factors affecting stand productivity. Specifically, total nitrogen content is positively correlated with the relative abundance of Basidiomycota (fungi), Myxococci (bacteria), and Zygomycota (fungi); while the contents of hydrolyzable nitrogen and soluble organic carbon are positively correlated with the relative abundance of Acidobacteria (bacteria) and Verrucous Microbes (bacteria) (p < 0.05).

[0159] The microbial community structure in soil has a significant impact on enzyme activity. Basidiomycetes (fungi) and Acidobacteria (bacteria) are the main phyla affecting total nitrogen, hydrolyzable nitrogen, and soluble organic carbon content, and are both dominant fungal phyla. Specifically, the relative abundance of Basidiomycetes is positively correlated with the activities of acid phosphatase and urease; the relative abundance of Acidobacteria is positively correlated with the activities of L-leucine peptidase, alkaline phosphate, and urease. The activities of N-acetyl-β-glucosidase, catalase, and sucrase are positively correlated with the relative abundance of Actinobacteria and Green Bay Fungi, and Mucor and Ascomycetes of fungi; β-glucosidase activity is positively correlated with the relative abundance of Insectivorae of fungi and Proteobacteria, Cyanobacteria, Bacteroidetes, and Firmicutes of bacteria (p < 0.05).

[0160] Carbon, nitrogen, and phosphorus are important nutrients affecting forest stand productivity. Correlation heatmaps show that total nitrogen content is significantly positively correlated with the relative abundance of *Vibrio* (bacteria) and significantly negatively correlated with *Verruciformis* (bacteria). Total phosphorus content is significantly positively correlated with the relative abundance of *Ascomycota* (fungi) and *Actinomycetes* (bacteria), and significantly negatively correlated with *Basidiomycota*, *Chytridiomycota*, and *Insectivoria* (fungi). Among soil microorganisms, the relative abundance of *Insectivoria* and *Basidiomycota* (fungi) is highly significantly negatively correlated; the relative abundance of *Gloydiomycota*, *Firmwallis* (bacteria) and *Spirulina* (bacteria) is highly significantly positively correlated; the relative abundance of *Bacteroidetes*, *Firmwallis*, and cyanobacteria is highly significantly positively correlated with the relative abundance of *Gloydiomycota* (fungi); and the relative abundance of *Firmwallis* and cyanobacteria is highly significantly positively correlated with the relative abundance of *Bacteroidetes* (p < 0.05).

[0161] Mechanism of the impact of nutrient cycling on stand productivity in Chinese fir plantations Figure 9 ):

[0162] This study used partial least squares structural equation modeling (PLS-SEM) to reveal the multi-factor driving mechanism of productivity formation in subtropical Chinese fir plantations. The overall goodness-of-fit (GOF) of the model was 0.652. This structural equation model reveals a complex, double-edged sword effect of soil nutrients on stand productivity. Soil physicochemical properties themselves exhibit a strong direct promoting effect on productivity (β=0.869), but at the same time, they also produce a significant competitive inhibitory effect by stimulating soil microbial activity: abundant nutrients first significantly increase microbial biomass (β=0.845), and both fungal and bacterial community structure diversity are significantly promoted (explaining about 58% of the variation in fungal communities and about 41.8% of the variation in bacterial communities, respectively). However, these microbial communities compete with forest growth for nutrients, which in turn inhibits forest productivity (β=-0.143). At the same time, nutrients reduce soil enzyme activity (β=-0.803), but with increasing forest age, the activity of carbon cycle-related enzymes is enhanced, while the activity of phosphorus cycle-related enzymes is inhibited. The inhibition of forest soil enzyme activity weakens the soil fungal community structure. This change further shapes a richer bacterial and fungal community structure. These microbial communities, in turn, have a negative impact on forest productivity through competition (β=-0.292, β=-0.342). Ultimately, the change in stand productivity is the net result of the interplay between the direct promoting effect of nutrients and the indirect inhibiting effect produced through the microbial system (p < 0.01).

[0163] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made by those skilled in the art to the above embodiments within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing productivity of Chinese fir plantations driven by multiple factors in coordination, characterized in that, Comprising the following steps: S1, comprehensive diagnosis of soil environment: comprehensive monitoring of soil at different growth stages of Chinese fir plantations, evaluation of soil physical and chemical properties, nutrient status and biological activity characteristics, identification of key limiting factors; S2, multi-factor coordinated regulation strategy planning: based on the results of soil environment diagnosis, combined with the growth needs of Chinese fir, formulate a multi-factor coordinated regulation strategy framework covering soil nutrients, microbial community and enzyme activity; S3, precise regulation of soil nutrients: according to the strategy planning, targeted nutrient supplement and adjustment of Chinese fir plantation soil to meet the nutrient needs of different growth stages; S4, directional optimization of microbial community and enzyme activity: through biological or non-biological means, optimize the structure of soil microbial community, enhance the activity of key enzymes, promote nutrient cycling and utilization; S5, dynamic implementation and monitoring of optimization strategy: apply the planned coordinated regulation strategy to the management practice of Chinese fir plantations, and continuously monitor the implementation effect and stand response; S6, effect evaluation and strategy iteration optimization: regularly evaluate the improvement effect of optimization strategy on Chinese fir growth and soil environment, dynamically adjust the strategy according to the evaluation results, and realize the continuous improvement of productivity.

2. The method of claim 1, wherein the method is for optimizing productivity of Chinese fir plantations driven by multiple factors in coordination. Step S1 specifically includes: S11, regularly collect soil samples of Chinese fir plantations at different stand ages; S12, determination of soil pH value, organic matter content, total nitrogen, total phosphorus and available potassium physical and chemical indexes; S13, analysis of soil microbial community structure diversity and enzyme activity level; S14, comprehensive evaluation of soil environment quality, and clear understanding of nutrient limiting factors and their dynamic change rule.

3. The method of claim 2, wherein the method is for optimizing productivity of Chinese fir plantations driven by multiple factors in coordination. In step S2, the specific measures include: S21, determine the interaction relationship among soil nutrients, microbial community and enzyme activity; S22, identify the key factors and synergistic action path affecting the growth of Chinese fir; S23, develop a comprehensive strategy scheme including nutrient management, microbial regulation and enzyme activity enhancement.

4. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 3, wherein, In step S3, the specific measures include: S31, according to the soil nutrient diagnosis results, determine the application amount and proportion of nitrogen, phosphorus and potassium; S32, use organic fertilizer and chemical fertilizer in combination to improve soil fertility; S33, for specific growth stages or nutrient limitation, implement local intensive fertilization or foliar spraying.

5. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 4, wherein, In step S4, the specific measures include: S41, inoculate beneficial microbial inoculants; S42, adjust soil pH value and humidity to promote microbial activity; S43, add biological activator to enhance the activity of key enzymes, accelerate nutrient transformation, and key enzymes include urease and phosphatase.

6. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 5, wherein, In step S5, the specific measures include: S51, apply the formulated coordinated regulation strategy to the management of Chinese fir plantations according to the plan; S52, regularly monitor the changes of soil physical and chemical properties, microbial community structure and enzyme activity; S53, observe the response of Chinese fir growth indicators, including tree height, diameter at breast height and volume.

7. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 6, wherein, In step S6, the evaluation indicators include: The increase of Chinese fir growth; The improvement range of soil nutrient content; The improvement degree of microbial community structure diversity; The enhancement effect of enzyme activity level.

8. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 7, wherein, In step S6, the dynamic adjustment strategy includes: According to the evaluation results, adjust the amount or period of nutrient application; Optimizing the inoculation scheme of microbial inoculants or the usage of biological activators; Improving agricultural management measures, including adjusting stand density and pruning methods.

9. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 8, wherein, Also includes S7, building a multi-factor synergistic model: Quantifying the contribution of soil nutrients, microbial biomass, enzyme activity, and stand structure to the productivity of Chinese fir; Revealing the interaction mechanisms between multiple factors and the key driving factors; Providing a theoretical basis for developing and optimizing synergistic control strategies.

10. The method of optimizing productivity of Chinese fir plantations driven by multiple factors synergistically of claim 9, wherein, In step S7, also includes: Using structural equation modeling (SEM) or machine learning algorithms to build prediction models; Using independent data sets to validate and optimize the model; Applying the model to actual Chinese fir plantations for precise productivity improvement.

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