Method for predicting survival rate of nursery stock after adversity stress and grading maintenance system

Abstract

The invention discloses a method for predicting the survival rate of seedlings after adversity stress and a grading maintenance system, and belongs to the field of chemical synthesis and chemical catalysis. Comprising the following steps: determining a sampling time window after a to-be-evaluated nursery stock experiences adversity stress; collecting annual branch samples of to-be-evaluated nursery stocks, and measuring the water content of the branches; substituting the measured water content value of the branches into a pre-constructed regression prediction model of the water content and the survival rate of the branches for calculation to obtain the predicted survival rate of the to-be-evaluated nursery stock; according to the numerical interval of the predicted survival rate, the survival grade of the nursery stock is determined, and a corresponding grading maintenance strategy is matched. According to the method, the defects of high subjectivity, response lag and high cost of an existing evaluation method are overcome, the method has the advantages of being easy and convenient to operate, accurate in prediction and high in universality, and a scientific basis can be provided for post-disaster management of forestry planting, ecological restoration and landscaping.

Description

Technical Field

[0001] This invention relates to the fields of forest tree physiology and ecology and forestry cultivation technology, specifically to a method for predicting the survival rate of seedlings after abiotic stress and a graded maintenance system. Background Technology

[0002] In forestry planting, ecological restoration projects, and landscaping management, seedlings often suffer from adverse stresses such as waterlogging and drought, leading to varying degrees of physiological damage and even death. Timely and accurate assessment of the survival potential of affected seedlings is of great significance for formulating scientific post-disaster remedial measures, rationally arranging replanting and resource input, and reducing economic losses.

[0003] Existing methods for assessing seedling survival rates mainly include the following categories:

[0004] 1. Morphological observation method

[0005] A common practice is to visually inspect seedlings for signs of wilting, discoloration, curling, or leaf drop to determine their viability. However, this method has the following drawbacks:

[0006] (1) High subjectivity: Different technicians have different judgment criteria for "severity of wilting" and "whether it is near death", and the evaluation results rely on experience and lack a unified quantitative scale.

[0007] (2) Symptoms appear after physiological damage: wilting, yellowing, and shedding of leaves due to water loss often occur after the plant has already experienced obvious physiological imbalances. For example, under prolonged drought conditions, the root system first shows latent damage such as water loss in fine roots and blockage of vascular bundles, and it usually takes 7 to 15 days for the external symptoms to become obvious; if water is not replenished in time within 10 days after the drought, a large number of fine roots will undergo irreversible necrosis, and even if watering is increased later, the survival rate is difficult to recover to the pre-disaster level. In the case of waterlogging, the root system rapidly declines in an oxygen-deficient environment, but the above-ground leaves may still maintain a relatively "normal" appearance for a short period of time, which can easily lead to misjudgment. As a result, remedial measures are often delayed, missing the best window of opportunity for maintenance.

[0008] (3) Unable to provide quantitative results: Morphological observation can only give a qualitative impression of "good / bad" or "light / heavy", making it difficult to express the survival probability in percentage form, and even more difficult to support subsequent refined water, fertilizer and human resource allocation decisions.

[0009] 2. Growth Indicator Monitoring Method

[0010] Another method involves continuously measuring tree height, diameter at root, and crown width to observe the growth trend of the seedlings over one or more growing seasons, and then inferring their adaptability and survival probability. The main problem with this method is that...

[0011] (1) It takes a very long time: it often takes a full growing season or even longer to reach a reliable conclusion. The evaluation cycle ranges from a few months to a year, which is significantly behind the needs of disaster relief and replanting decisions.

[0012] (2) Difficult to use for rapid early assessment after disaster: Within 1 to 2 months after flooding or drought stress, the morphological growth of seedlings, such as tree height and diameter, does not change much. Short-term repeated measurements are difficult to reflect the potential mortality risk caused by adversity, making it impossible to provide a basis for decision-making during the critical window period.

[0013] (3) Susceptible to interference from early stage differences: Different seedlings have differences in size and baseline growth potential in the early stage of planting. The changes in growth in the later stage are affected by both adverse conditions and initial differences, making it difficult to distinguish. It is an indirect indicator with many lagging and mixed factors.

[0014] 3. Physiological and biochemical index method

[0015] Other studies have used physiological and biochemical indicators such as chlorophyll fluorescence parameters, leaf water content, proline content, soluble sugars, and antioxidant enzyme activity to evaluate the degree of stress damage. While these methods can sensitively reflect physiological states under laboratory conditions, they have significant limitations in practical application.

[0016] (1) High detection cost and complex operation: It often requires special instruments such as fluorescence spectrometers and spectrophotometers, as well as a variety of chemical reagents. The sample preparation and measurement steps are complicated, and the time and economic cost of a single test are high. It is not suitable for high-frequency testing in large-scale forest stands or nurseries.

[0017] (2) Sensitive to environmental conditions and poor repeatability: Parameters such as chlorophyll fluorescence are greatly affected by environmental conditions such as light intensity, temperature and measurement time. It is difficult to standardize and control the field conditions, resulting in poor comparability of data at different times and locations.

[0018] (3) Insufficient universality: The “critical threshold” for different tree species and different stress types varies significantly. It is often necessary to specify appropriate indicators and warning ranges for each tree species separately, making it difficult to form a unified evaluation system across tree species.

[0019] (4) Lack of direct correspondence with “survival rate”: Most studies remain at the description of “strength of physiological response” and have not yet established a quantitative prediction model such as “change in the value of a certain indicator → survival probability in the following year”, which makes it difficult to directly guide post-disaster relief and graded maintenance decisions.

[0020] In summary, existing technologies have the following core shortcomings in assessing the survival rate of seedlings after abiotic stress:

[0021] 1) Highly subjective: Relies on human experience, visual inspection, or vague judgment, lacking objective and unified quantitative standards;

[0022] 2) Time-consuming and slow response: Most methods require a growing season or until symptoms become obvious before conclusions can be drawn, making it difficult to seize the critical rescue window of 10 to 30 days after a disaster.

[0023] 3) High testing costs and high technical barriers: The determination of physiological and biochemical indicators relies on specialized equipment and experimental conditions, which are difficult for grassroots forestry units and ordinary nurseries to afford;

[0024] 4) Poor universality: Different evaluation systems need to be established for different tree species and under different adverse conditions, and there is a lack of a simple and universal tool that can be promoted across multiple tree species.

[0025] Therefore, existing technologies lack a method and system that is easy to operate, low in cost, quick to respond, and capable of quantitatively predicting the survival rate of seedlings of multiple tree species in the early post-disaster window, and can be directly used for graded maintenance decisions. Summary of the Invention

[0026] Based on previous research and existing problems, this invention proposes a method for predicting the survival rate of seedlings after abiotic stress and a graded maintenance system. Using the easily measurable physical quantity of water content (WMC) of one-year-old branches as a "physiological probe", a quantitative regression relationship between WMC and population survival rate (SR) is established within a specific time window after the abiotic stress is relieved. This enables early and objective prediction of the fate of seedlings after disasters and automatically provides graded maintenance strategies based on the prediction results.

[0027] To achieve the above objectives, the present invention provides the following technical solution:

[0028] A method for predicting the survival rate of seedlings after abiotic stress, characterized by the following steps:

[0029] Step S1: Determine the sampling time window after the seedlings to be evaluated have experienced abiotic stress;

[0030] Step S2: Collect one-year-old branch samples from the seedlings to be evaluated and measure their branch moisture content (WMC).

[0031] Step S3: Using the pre-constructed regression prediction model of branch moisture content and survival rate, substitute the measured branch moisture content (WMC) value into the model to calculate the predicted survival rate (SR_pred) of the seedling to be evaluated.

[0032] Step S4: Determine the survival level of the seedlings based on the numerical range of the predicted survival rate (SR_pred) and match the corresponding graded maintenance strategy.

[0033] Preferably, the method for constructing the regression prediction model in step S3 is as follows: Select samples of the same tree species and divide them into a stress group and a control group. Measure the branch water content within the same sampling time window, and statistically analyze the actual survival rate in subsequent growth cycles. Obtain the quantitative relationship between branch water content (WMC) and survival rate through regression fitting.

[0034] Preferably, the regression prediction model is a linear model:

[0035] SR_ pred =a×WMC+b

[0036] Among them, WMC and SR_ pred All are normalized 0-1 decimal forms, where a is the slope coefficient and b is the intercept.

[0037] Preferably, for a mixed forest population containing jujube, sumac, poplar, mulberry, willow and elm, the value of a ranges from 2.20 to 2.30, and the value of b ranges from -0.15 to -0.10.

[0038] Preferably, in step S1, the type of abiotic stress includes at least one of waterlogging stress and drought stress; the sampling time window is from the 15th day to the 30th day after the end of the abiotic stress.

[0039] Preferably, in step S2, the branch sample is the middle section of a one-year-old branch, with a sample length of 5 to 15 cm, and the branch sample is healthy, without obvious diseases, pests, or mechanical damage, avoiding the base, branches, and tender shoots that are not fully lignified at the top.

[0040] It should be further noted that within plants, water storage and transport primarily rely on root absorption, xylem transport, and the water content of organs such as branches, leaves, etc. One-year-old branches, in particular, possess the following characteristics:

[0041] 1. The xylem vessels and water storage tissues directly reflect the root system's water supply capacity and whether the water conduction pathways are unobstructed. When the root system is damaged due to waterlogging and lack of oxygen or drought and loss of water, the water content of the xylem of the branches will decrease accordingly.

[0042] 2. Compared to leaves, one-year-old branches are less affected by short-term environmental fluctuations (such as short-term light, temperature changes, and sudden increases in transpiration). Changes in their water content better reflect the plant's long-term water balance than short-term transpiration.

[0043] 3. As perennial organs, damage to the structure of branches (such as severe vascular embolism or tissue necrosis) often means that the plant has entered an irreversible stage of decline, which is closely related to whether it can sprout normally and survive the following year.

[0044] This invention, through analysis of extensive measured data from multiple tree species, reveals a significant positive correlation between the water content of one-year-old branches and the actual survival rate in the following spring for samples of the same or different tree species within a suitable time window after the relief of abiotic stress. Higher branch water content indicates less damage to the root-stem water conduction system and a higher probability of seedling survival; conversely, a significantly lower branch water content often corresponds to a higher risk of mortality. Therefore, branch water content can serve as a bridge indicator connecting the "current physiological state of water" and the "future survival outcome," and as the core independent variable of the survival rate prediction model of this invention.

[0045] In contrast, leaf water content is more susceptible to short-term environmental fluctuations, while physiological and biochemical indicators (such as proline and enzyme activity) can reflect the strength of stress response, but lack a unified and stable quantitative correlation with the final survival rate, making it difficult to promote on a large scale in the field. Choosing "one-year-old branch water content" as a predictive indicator combines physiological sensitivity, ease of measurement, and stable correlation with survival results.

[0046] Existing technologies typically lack a clear definition of the optimal time point for assessing post-disaster survival rates, often making rough judgments over a considerable period (months or even a growing season) after the disaster, leading to delayed remedial measures. This invention, by continuously tracking branch moisture content and the following year's survival rate at different time points (one week, two weeks, three weeks, four weeks, and beyond) after the removal of adverse stress, systematically compares the stability and predictive ability of WMC–SR regression models established at different sampling time points. The results show that:

[0047] 1. The period from 0 to 15 days after the stress is relieved is the stress fluctuation period: During this period, the seedlings are still in a strong stress response and recovery adjustment process. The branch moisture content fluctuates greatly from day to day depending on the soil moisture content, climate conditions, etc. The WMC of the same seedling measured on different days varies greatly. The regression model established using the data in this period has poor fitting accuracy and predictive stability.

[0048] 2. The period of 15–30 days after the stress is relieved is the physiologically relatively stable period: During this stage, most seedlings have completed the initial reconstruction of water balance, and the water content of branches gradually tends to stabilize. The values ​​more accurately reflect the degree of substantial damage caused by the stress. Regression models established using data from this stage have the highest correlation coefficients, the smallest residuals, and better applicability across plots and tree species.

[0049] 3. Thirty days after the stress is relieved, some weak seedlings have begun to die: For severely damaged plants, if effective water conduction and new root growth cannot be restored within one month after the stress is relieved, the water content of their branches will remain at a low level and gradually turn into tissue necrosis; at this time, even if remedial measures are implemented, the actual survival rate will be limited, and the rescue value will be lost.

[0050] Taking into account both the physiological representativeness of the branch moisture content measurement and the time range within which maintenance measures remain effective, this invention defines the 15th to 30th day after the relief of adverse stress as a unified sampling and evaluation time window. This time window is the intersection of the "relatively stable physiological state period" and the "management remedial window period," which is also an important highlight that distinguishes this invention from the prior art.

[0051] The preferred method for measuring and calculating the moisture content of branches is as follows:

[0052] Weigh the fresh weight (FW) of the branch sample.

[0053] Dry the sample at 103℃~105℃ until constant weight, and weigh the dry weight (DW).

[0054] Calculate using the formula WMC=[(FW-DW) / FW]×100%.

[0055] Preferably, in step S4, the matching criteria for the maintenance strategy are:

[0056] When the predicted survival rate (SR_pred) is ≥80%, routine maintenance measures shall be implemented;

[0057] When 50% ≤ predicted survival rate (SR_pred) < 80%, implement enhanced maintenance measures;

[0058] When the predicted survival rate (SR_pred) is less than 50%, rescue measures should be taken or the seedlings should be replaced.

[0059] Preferably, enhanced maintenance measures include at least one of increasing watering frequency and applying rooting agents; rescue measures include at least one of heavy pruning and soil improvement.

[0060] This invention also proposes a graded maintenance system for seedlings after abiotic stress, comprising:

[0061] The data acquisition module is used to obtain the fresh weight and dry weight data of branch samples of seedlings under stress.

[0062] The moisture content calculation module is used to calculate the moisture content of branches based on the collected fresh weight and dry weight data;

[0063] The survival rate prediction module stores the aforementioned regression prediction model, which is used to output the predicted survival rate based on the branch moisture content.

[0064] The maintenance decision module is used to output corresponding maintenance strategies based on the predicted survival rate.

[0065] Compared with existing technologies, this invention provides a method for predicting the survival rate of seedlings after abiotic stress and a graded maintenance system, which has the following beneficial effects:

[0066] (1) This invention measures the branch moisture content during the critical window period of 15 to 30 days after the relief of adverse stress, and uses the established regression model to predict the survival rate of the following year. Compared with the traditional method based on morphological symptoms or growth in a growing season, the effective assessment time can be advanced by 3 to 6 months, providing sufficient time for rescuing endangered seedlings and arranging replanting.

[0067] (2) This invention uses the water content of one-year-old branches, an indicator with clear physiological significance, as the core variable to quantitatively characterize the degree of water balance disruption caused by adversity, avoiding the subjectivity of purely empirical visual assessment, and transforming the vague judgment of "whether it can survive" into a repeatable numerical prediction.

[0068] (3) Only an electronic balance and an oven are needed to complete the test. There is no need for expensive fluorescence instruments, spectrophotometers and chemical reagents. The on-site sampling and sample processing process is simple and suitable for promotion and application in grassroots forestry stations and general nurseries.

[0069] (4) This invention not only provides numerical predictions of survival rate, but also directly maps them to graded maintenance strategies. The high, medium and low categories correspond to different watering frequencies, rooting agent application and pruning, and soil improvement measures, so that the evaluation results can be quickly transformed into specific management actions.

[0070] (5) The WMC-SR prediction model constructed in this invention shows significant correlation (R) in multiple common tree species such as jujube, poplar, and willow. ² >0.6), and can be extended to other tree species through parameter adjustment; at the same time, the unified 15-30 day evaluation time window and standardized sampling and measurement methods facilitate the establishment of a universal evaluation system across different regions and tree species. Attached Figure Description

[0071] Figure 1 This is a bar chart comparing the water content (WMC) of seedling branches in the flood-affected group and the control group in an embodiment of the present invention.

[0072] Figure 2 This is a linear regression fitting diagram of branch moisture content (WMC) and seedling survival rate (SR) in an embodiment of the present invention. Detailed Implementation

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] This embodiment proposes a method for predicting the survival rate of seedlings after abiotic stress. The specific method is as follows:

[0075] 1. Select target tree species or combinations of multiple tree species, and set up an abiotic stress group (such as natural waterlogging or artificial drought treatment) and a normal control group; within a time window of 15 to 30 days after the stress is relieved, collect one-year-old branch samples according to the prescribed method and measure the branch water content (WMC); continue to observe until the budding period of the following year, and count the actual survival rate (SR) of each treatment group; establish a predictive model between SR and WMC through regression analysis.

[0076] Specifically, six tree species were selected: jujube (Ziziphus jujuba), sumac (Rhus typhina), poplar (Populustomentosa), mulberry (Morus alba), willow (Salix babylonica), and elm (Ulmus pumila).

[0077] Group design:

[0078] Stress group: Select plots that have experienced natural waterlogging stress, and select 3 replicate plots for each tree species, with 20 trees in each plot.

[0079] Control group: Select adjacent plots that are at higher elevations and not affected by flooding, and set up the same number of replicates.

[0080] Sampling was conducted 25 days after the floodwaters were drained. Healthy, one-year-old branches without obvious pests, diseases, or mechanical damage were selected from the main branches of each seedling, avoiding the base, branching points, and incompletely lignified shoots at the tips. A 5–15 cm section was cut from the middle of the one-year-old branch as a sample; in this embodiment, a section approximately 10 cm long was preferred. The fresh weight (FW) was measured using an electronic balance with an accuracy of 0.01 g. The samples were then dried in a forced-air drying oven at 103–105°C until constant weight was achieved. In this embodiment, the drying temperature was 105°C for approximately 48 hours, and the dry weight (DW) was measured.

[0081] WMC(%) = [(FW−DW) / FW] × 100%

[0082] Sampling and parameter selection criteria:

[0083] To minimize interference from non-adverse factors such as pests, diseases, and mechanical damage on the results of branch moisture content measurements, the branch sampling in this embodiment follows these principles: Healthy one-year-old branches are selected from the main branches of the seedling as sample sources, ensuring the branches are free of obvious lesions, insect holes, cracks, or rotten tissue. Simultaneously, samples are taken from the base of branches, pruning cuts, and incompletely lignified shoots at the branch tips, avoiding these areas; samples are taken only from the middle section of one-year-old branches. Comparative experiments with random sampling revealed that sampling according to these principles significantly reduces the variation in branch moisture content among duplicate samples within the same plant, improving sample representativeness and repeatability, and allowing the measured branch moisture content to better reflect the overall moisture status of the seedling under adverse stress.

[0084] The length range of the branch samples, 5–15 cm, was determined through preliminary experiments. In these experiments, one-year-old branch segments of varying lengths (approximately 3 cm, 5 cm, 10 cm, 15 cm, and 20 cm) were used as samples. The moisture content of the branches was measured using the same method, and the variation among replicate samples of the same length was compared. The results showed that when the sample length was less than 5 cm, the electronic balance weighing error accounted for a larger proportion of the total mass due to the smaller sample mass, leading to significantly increased fluctuations in the calculated branch moisture content. When the sample length was greater than 15 cm, the differences in internode length and lignification degree within the same sample increased, resulting in significant heterogeneity in the internal moisture content of the branch and greater differences between different samples from the same plant. Considering measurement accuracy, ease of operation, and sample representativeness, this invention preferably uses a branch sample length of 5–15 cm; in this embodiment, a length of approximately 10 cm was selected.

[0085] The drying temperature of the branch samples was controlled between 103 and 105°C. This temperature range was referenced from the drying conditions commonly used in the determination of moisture content of wood materials (approximately 103 ± 2°C) and determined in conjunction with the preliminary experimental results of this invention. In the preliminary experiment, the branch samples were dried at different temperatures of 95°C, 100°C, 103°C, 105°C, and 110°C, and the time required to reach constant weight and the changes in the appearance of the samples were recorded. The results showed that under the conditions of 103–105°C, the branch samples could reach constant weight within a reasonable time. Under the conditions of this embodiment, it took approximately 48 hours, and the mass change between two consecutive weighings (0.5 hours apart) was less than 0.01 g, with no obvious charring on the sample surface. When the drying temperature was below 103°C, the time required to reach constant weight was significantly prolonged, and some samples may have residual bound water, resulting in a lower measured moisture content of the branches. When the drying temperature was above 105°C, some thin branches showed darkening of color or slight charring on the surface, which may have caused thermal degradation of organic components, leading to a lower measured dry weight.

[0086] This invention limits the drying temperature of branch samples to 103–105°C, which improves the efficiency of the measurement while ensuring the accuracy and stability of the moisture content determination. Constant weight can be determined using the following standard: when the change in sample mass between two consecutive weighings (0.5 h apart) is less than 0.01 g, it is considered to have reached constant weight.

[0087] SR statistics: Survival status is investigated in the following spring. A single plant is considered to be alive if at least one terminal bud sprouts or leaflets unfold.

[0088] SR (%) = (Number of surviving plants / Total number of plants) × 100%

[0089] 2. Analyze and model the collected data.

[0090] Specifically, the collected data is summarized in Table 1 below:

[0091] Table 1. Mean WMC and SR values ​​of different tree species in the flood-affected group and the normal group.

[0092] species Processing group WMC mean (%) SR (%) jujube flood 36.47 70 torch flood 27.24 42 poplar flood 37.97 53 mulberry flood 39.90 40 willow flood 52.90 100 elm flood 46.83 100 jujube normal 39.30 83 torch normal 38.84 83 poplar normal 49.20 100 mulberry normal 41.20 100 willow normal 51.37 100 elm normal 46.60 90

[0093] (Note: According to the t-test, the WMC of all tree species in the flooded group was significantly lower than that in the control group (P<0.05))

[0094] The least squares method was used to perform linear regression fitting on the flood-affected group data. Let WMC be the independent variable (in decimal form, such as 36.47% denoted as 0.3647), and SR be the dependent variable (in decimal form).

[0095] The general model obtained by fitting is:

[0096] SR_ pred =2.245×WMC−0.133

[0097] Model statistical metrics: R 2 =0.624, P=0.0022, indicating a highly significant positive correlation between the two.

[0098] Based on the mean data of the six tree species, the flooded group, and the normal group shown in Table 1, a total of 12 data points (WMC, SR) were obtained. First, using all 12 data points as the sample set, a linear regression was performed using the least squares method. The water content of one-year-old branches (WMC) was set as the independent variable (decimal form), and the survival rate (SR) was set as the dependent variable (decimal form). The initial model parameters were obtained as a0=2.245 and b0=−0.133, with corresponding determination coefficients R²=0.624 and P=0.0022.

[0099] To verify the stability of the model and its parameters, and to determine the general parameter range applicable to mixed forest communities of multiple tree species, the original data underwent further optimization analysis as follows:

[0100] (1) Model Form Comparison: Linear, quadratic polynomial, and logarithmic models were used to fit the same dataset, and the coefficient of determination (R²) and root mean square error (RMSE) of each model were compared. The results show that the R² of the linear model has reached 0.624. Although the R² of the quadratic and logarithmic models is slightly improved, the improvement is small, and they introduce additional parameters, increasing the model complexity and hindering its widespread application. Considering both interpretability and simplicity, this invention preferably uses the linear model SR = a × WMC + b.

[0101] (2) Leave-one-species cross-validation: To examine the model's universality across different tree species, cross-validation was performed on a "tree species" basis. Specifically, one tree species (including all data from both the flooded and normal groups of that species) was left out of the six tree species each time as the validation set, while the data from the remaining five tree species were used as the training set. A set of parameters (a_i, b_i) was fitted using the training set data, and then the SR of the left-out tree species was predicted using this set of parameters, and the prediction error was calculated. The results of the six cross-validations showed that the a_i obtained from each fitting were all distributed between 2.20 and 2.30, and the b_i were all distributed between -0.15 and -0.10, with the corresponding average relative prediction errors all less than 10%, indicating that the model parameters have good stability and transferability under different tree species combinations.

[0102] (3) Random resampling analysis: Further random resampling (bootstrap) was employed to draw samples with replacement from the aforementioned 12 groups (WMC, SR) of data, and linear fitting was repeated multiple times to statistically analyze the distribution of slope a and intercept b. The results showed that, at a 95% confidence level, the confidence interval for slope a was approximately 2.20–2.30, and the confidence interval for intercept b was approximately −0.15–−0.10. This result is basically consistent with the parameter fluctuation range obtained from leave-one-tree-species cross-validation.

[0103] Based on the above analysis, this embodiment uses a=2.245 and b=−0.133 as the generally recommended parameters for a mixed forest community composed of jujube, sumac, poplar, mulberry, willow, and elm, and considers a∈[2.20, 2.30] and b∈[−0.15, −0.10] as reasonable parameter fluctuation ranges. It should be emphasized that in practical applications, for different regions or specific tree species combinations, the parameters can be fine-tuned within the above range based on local experimental data, while maintaining the model structure unchanged.

[0104] 3. Substitute the measured branch moisture content (WMC) value into the model calculation to obtain the predicted survival rate (SR_pred) of the seedlings to be evaluated.

[0105] 4. Based on the predicted survival rate (SR_pred) within its numerical range, determine the survival grade of the seedlings and match the corresponding graded maintenance strategy.

[0106] Specifically, the matching criteria for maintenance strategies are as follows:

[0107] When the predicted survival rate (SR_pred) is ≥80%, the root and branch water conduction systems of these seedlings are relatively lightly damaged and have a strong self-recovery ability. Conventional maintenance measures can be adopted, such as:

[0108] Water according to the local conventional irrigation system, such as watering once every 10 to 15 days during the growing season or supplementing water as needed depending on soil moisture.

[0109] Maintain the original fertilization plan, and apply a small amount of nitrogen and phosphorus compound fertilizer during the peak growing season;

[0110] Routine pest and disease monitoring and control are conducted without the need for special emergency measures.

[0111] When 50% ≤ predicted survival rate (SR_pred) < 80%, these seedlings have suffered some degree of water physiological damage. Implementing enhanced maintenance measures can significantly improve the survival rate. These measures include:

[0112] Increase watering frequency: During the post-disaster recovery period (e.g., within 1-2 months), increase the watering frequency to about once every 7 days to ensure that the soil around the roots is moist but not waterlogged; after drought stress, use small amounts of water frequently, while after waterlogging stress, focus on loosening the topsoil and promoting drainage.

[0113] Apply rooting agent: Drench the root zone with rooting agent solution, such as naphthaleneacetic acid (NAA) 10-20 mg / L or indolebutyric acid (IBA) 20-50 mg / L, once every 10-15 days, for 2-3 consecutive times, to promote the development of new roots and enhance water absorption capacity.

[0114] Foliar nutrition supplementation: Spray foliar fertilizer containing nitrogen, phosphorus, potassium and trace elements, such as 0.2% to 0.3% water-soluble fertilizer prepared according to the instructions, once every 15 days to alleviate the insufficient nutrient supply caused by short-term root damage.

[0115] When the predicted survival rate (SR_pred) is less than 50%, the moisture content of the branches of such seedlings is significantly low, the water conduction system and root system are severely damaged, and the survival risk is high. The choice between rescuing or replacing the seedlings should be made based on their actual value.

[0116] For individuals with high landscape or economic value, rescue measures may be taken, including:

[0117] Heavy pruning: Remove obviously dead branches and most leaves, leaving only the main branches to reduce transpiration load;

[0118] Soil improvement and loosening: Loosen the soil around the roots and add decomposed organic matter or sandy loam to improve aeration and drainage.

[0119] Combine rooting agent irrigation with moderate survival rate and appropriate water and fertilizer management, observe for one growing season before deciding whether to retain the plant.

[0120] For seedlings of average value and in large quantities, a replanting plan can be planned in advance based on the forecast results, and dead or severely weakened individuals can be removed in a timely manner to reduce ineffective investment later.

[0121] In addition, this invention also proposes a graded maintenance system for seedlings after abiotic stress, the system comprising:

[0122] The data acquisition module is used to obtain the fresh weight and dry weight data of branch samples of seedlings under stress.

[0123] The moisture content calculation module is used to calculate the moisture content of branches based on the collected fresh weight and dry weight data;

[0124] The survival rate prediction module stores the aforementioned regression prediction model, which is used to output the predicted survival rate based on the branch moisture content.

[0125] The maintenance decision module is used to output corresponding maintenance strategies based on the predicted survival rate.

[0126] Application Example 1:

[0127] 1. Experimental Materials and Grouping

[0128] Several Chinese scholar trees and several ash trees of similar size, both one-year-old or two-year-old seedlings, were selected from the nursery. Following the principles described in Example 1, a waterlogging stress group and a normal control group were established:

[0129] Flood stress group: Located in areas with severe water accumulation and continuous flooding;

[0130] Normal control group: adjacent plots located at higher elevations with good drainage and no significant water accumulation.

[0131] 2. Data Collection

[0132] (1) Measurement of water content (WMC) of branches

[0133] On the 25th day after the floodwaters were drained, healthy one-year-old branches without obvious pests, diseases, or mechanical damage were selected from the main branches of each sample tree, avoiding the base, branching points, and incompletely lignified shoots at the tips. A section approximately 10 cm in length was cut from the middle section as a sample. The fresh weight (FW) was measured using an electronic balance with an accuracy of 0.01 g. The samples were then dried in a forced-air drying oven at 103–105℃ until constant weight, and the dry weight (DW) was measured according to the formula.

[0134] WMC(%) = [(FW−DW) / FW] × 100%

[0135] Calculate the moisture content of the branches.

[0136] (2) Survival rate (SR) statistics

[0137] In the spring of the following year, during the budding period, each seedling in each treatment group was inspected. Those with bud sprouting at the top or leaflets unfolding were recorded as surviving. The survival rate was calculated according to the formula SR(%) = (number of surviving seedlings / total number of seedlings) × 100%.

[0138] The mean moisture content of one-year-old branches in each treatment group and the corresponding actual survival rate are shown in Table 2.

[0139] Table 2. WMC and SR data of the flooded group and the normal group of Sophora japonica and Fraxinus chinensis.

[0140] species Processing group WMC mean (%) SR (%) Chinese scholar tree flood 37.82 66 Chinese scholar tree normal 42.15 80 white wax flood 43.43 85 white wax normal 45.82 100

[0141] (3) Prediction and error analysis of the general model

[0142] The general prediction model established based on data from six tree species in the example is adopted:

[0143] SR_ pred =2.245×WMC−0.133

[0144] Among them WMC and SR_ pred All values ​​are expressed in decimal form, with coefficients 2.245 and -0.133 corresponding to a≈2.245 and b≈-0.133 given in the specification.

[0145] Substituting the WMC values ​​from Table 2 into the general model described above, we obtain the predicted survival rates SR_ for each treatment group of Sophora japonica and Fraxinus chinensis. pred The results were compared with the measured survival rate (SR), and the results are shown in Table 3.

[0146] Table 3. Prediction results of the general model for the survival rate of Sophora japonica and Fraxinus chinensis.

[0147] species Processing group WMC (%) Measured SR (%) <![CDATA[Predict SR_ pred (%)]]> Absolute error (percentage points) Chinese scholar tree flood 37.82 66.1 71.6 ≈5.5 Chinese scholar tree normal 42.15 80.3 81.3 ≈1.0 white wax flood 43.43 85.0 84.2 ≈0.8 white wax normal 45.82 100.0 89.6 ≈10.4

[0148] As can be seen from Table 3:

[0149] 1) For the two tree species, Sophora japonica and Fraxinus chinensis, the measured survival rates of both the flooded group and the normal group increased significantly with the increase of branch moisture content. This is consistent with the pattern observed in this invention on tree species such as jujube, staghorn sumac, poplar, mulberry, willow, and elm. This further proves that the moisture content of one-year-old branches can reflect the degree of physiological water damage to the plant after adverse stress and is closely related to the survival results in the following year.

[0150] 2) When using the general model of this invention (a≈2.245, b≈−0.133) for prediction, the absolute error between the predicted survival rate and the measured survival rate of each treatment group of Sophora japonica and Fraxinus chinensis is mostly controlled within about ±10 percentage points, and the error of the normal Sophora japonica group and the flooded Fraxinus chinensis group is less than 1 percentage point.

[0151] 3) Based on the data of Sophora japonica, Fraxinus chinensis and the six tree species in the examples, the WMC-SR linear model constructed by this invention shows good stability and universality under different tree species and plot conditions, which verifies the reliability of the general parameter range of this invention (a is about 2.20 to 2.30, b is about −0.15 to −0.10, in decimal form).

[0152] Application Example 2:

[0153] 1. Application Background

[0154] In July 2024, a nursery experienced flooding due to heavy rain, affecting multiple varieties including staghorn sumac and poplar. The method of this invention was used for evaluation 30 days after drainage was completed.

[0155] 2. Operating Procedures

[0156] Step 1: Randomly select 20 samples of torch tree and 20 samples of poplar trees from the disaster-stricken area.

[0157] Step 2: Determine the branch moisture content according to the method described in Example 1. Specifically, select healthy one-year-old branches without obvious pests, diseases, or mechanical damage from the main branches of each seedling, avoiding the base, branching points, and incompletely lignified shoots at the tips. Cut a branch segment approximately 10 cm long from the middle section as a sample, weigh the fresh weight (FW), and dry it in a forced-air drying oven at 103–105℃ until constant weight, then weigh the dry weight (DW). Calculate the branch moisture content using the formula WMC(%) = [(FW−DW) / FW] × 100%.

[0158] The average weight of the staghorn sumac sample group was 10.50g, and the average weight of the sample group was 7.58g.

[0159] WMC 火炬 =(10.50−7.58) / 10.50≈27.81%(0.2781).

[0160] The average weight of the poplar sample group was 12.00g, and the average weight of the dried poplar (DW) was 7.40g.

[0161] WMC 杨树 =(12.00−7.40) / 12.00≈38.33%(0.3833).

[0162] Step 3: Substitute the data into the general model for prediction.

[0163] Predicted survival rate of staghorn sumac:

[0164] SR 火炬 =2.245×0.2781−0.133≈0.491 (i.e. 49.1%).

[0165] Poplar tree survival rate prediction:

[0166] SR 杨树 =2.245×0.3833−0.133≈0.727 (i.e. 72.7%).

[0167] 3. Decision-making and implementation of graded maintenance

[0168] Based on the grading criteria set by this invention (see Table 4), the system automatically generates the following decisions:

[0169] Table 4. Decision-making table for graded maintenance

[0170] Evaluation object Predicted SR value Determine the level Decision-making measures Torch Tree 49.1%(<50%) Low survival rate Rescue / Abandonment: It is recommended to heavily prune high-value seedlings (remove leaves and keep branches) and improve soil aeration; for low-value seedlings, it is recommended to remove them directly and prepare for replanting. poplar 72.7%(50%-80%) Medium survival level Enhanced care: The condition is recoverable. It is recommended to water once a week, apply a rooting agent (such as NAA) to the roots, and spray with foliar fertilizer.

[0171] 4. Effect Verification

[0172] Follow-up examination the following spring:

[0173] The actual survival rate of the staghorn trees was 45%, which was only +4.1% different from the predicted value of 49.1%. The prediction was accurate and ineffective investment was avoided.

[0174] The actual survival rate of the poplar trees was 75%, which was -2.3% lower than the predicted value of 72.7%. After enhanced maintenance, the survival rate was slightly higher than the predicted value, proving that the graded maintenance was effective.

[0175] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. However, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions claimed by the present invention.

Claims

1. A method for predicting the survival rate of seedlings after abiotic stress, characterized in that, Includes the following steps: Step S1: Determine the sampling time window after the seedlings to be evaluated have experienced abiotic stress; Step S2: Collect one-year-old branch samples from the seedlings to be evaluated and measure their branch moisture content (WMC). Step S3: Using the pre-constructed regression prediction model of branch moisture content and survival rate, substitute the measured branch moisture content (WMC) value into the model to calculate the predicted survival rate (SR_pred) of the seedling to be evaluated. Step S4: Determine the survival level of the seedlings based on the numerical range of the predicted survival rate (SR_pred) and match the corresponding graded maintenance strategy.

2. The method for predicting the survival rate of seedlings after abiotic stress according to claim 1, characterized in that, The method for constructing the regression prediction model in step S3 is as follows: Select samples of the same tree species and divide them into a stress group and a control group. Measure the branch water content within the same sampling time window, and count the actual survival rate in the subsequent growth cycle. Obtain the quantitative relationship between branch water content (WMC) and survival rate through regression fitting.

3. The method for predicting the survival rate of seedlings after abiotic stress according to claim 2, characterized in that, The regression prediction model is a linear model: SR_ pred =a×WMC+b Among them, WMC and SR_ pred All are normalized 0-1 decimal forms, where a is the slope coefficient and b is the intercept.

4. The method for predicting the survival rate of seedlings after abiotic stress according to claim 3, characterized in that, For mixed forest communities containing jujube, sumac, poplar, mulberry, willow and elm, the value of a ranges from 2.20 to 2.30, and the value of b ranges from -0.15 to -0.

10.

5. The method for predicting the survival rate of seedlings after abiotic stress according to claim 1, characterized in that, In step S1, the type of abiotic stress includes at least one of waterlogging stress and drought stress; the sampling time window is from the 15th day to the 30th day after the end of the abiotic stress.

6. The method for predicting the survival rate of seedlings after abiotic stress according to claim 1, characterized in that, In step S2, the branch sample is the middle section of a one-year-old branch, with a sample length of 5-15 cm. The branch sample should be vigorous, without obvious diseases, pests, or mechanical damage, and should avoid the base, branches, and tender shoots that are not fully lignified at the top.

7. The method for predicting the survival rate of seedlings after abiotic stress according to claim 3 or 4, characterized in that, The method for measuring and calculating the moisture content of branches is as follows: Weigh the fresh weight (FW) of the branch sample. Dry the sample at 103℃~105℃ until constant weight, and weigh the dry weight (DW). Calculate using the formula WMC=[(FW-DW) / FW]×100%.

8. The method for predicting the survival rate of seedlings after abiotic stress according to claim 7, characterized in that, Step S4, the matching criteria for maintenance strategies are: When the predicted survival rate (SR_pred) is ≥80%, routine maintenance measures shall be implemented; When 50% ≤ predicted survival rate (SR_pred) < 80%, implement enhanced maintenance measures; When the predicted survival rate (SR_pred) is less than 50%, rescue measures should be taken or the seedlings should be replaced.

9. The method for predicting the survival rate of seedlings after abiotic stress according to claim 8, characterized in that, Strengthening maintenance measures include increasing watering frequency and applying at least one of the following: rooting agents; rescue measures include heavy pruning and soil improvement.

10. A graded maintenance system for seedlings after abiotic stress, characterized in that, include: The data acquisition module is used to obtain the fresh weight and dry weight data of branch samples of seedlings under stress. The moisture content calculation module is used to calculate the moisture content of branches based on the collected fresh weight and dry weight data; The survival rate prediction module internally stores the regression prediction model as described in any one of claims 1, 3, and 4, which is used to output a predicted survival rate based on the branch moisture content. The maintenance decision module is used to output corresponding maintenance strategies based on the predicted survival rate.

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