CN-121997294-A - Survival rate prediction method and grading maintenance system for seedlings after adversity stress

CN121997294ACN 121997294 ACN121997294 ACN 121997294ACN-121997294-A

Abstract

The invention discloses a method for predicting survival rate of seedlings after adversity stress and a grading maintenance system, belonging to the fields of chemical synthesis and chemical catalysis. The method comprises the steps of determining a sampling time window of a seedling to be evaluated after the seedling to be evaluated is subjected to stress, collecting annual branch samples of the seedling to be evaluated, measuring the moisture content of branches, substituting a pre-constructed regression prediction model of the moisture content and the survival rate of the branches into the model to calculate, obtaining the predicted survival rate of the seedling to be evaluated, determining the survival grade of the seedling according to a numerical range of the predicted survival rate, and matching with a corresponding grading maintenance strategy. The method solves the defects of strong subjectivity, lag response and high cost of the existing assessment method, has the characteristics of simple and convenient operation, accurate prediction and strong universality, and can provide scientific basis for forestry planting, ecological restoration and post-disaster management of landscaping.

Inventors

WANG JIAQI
MA NING
GENG GUANYU
HAN MINGLI
LU JINBANG
CHEN JIAN
YANG GUANWEI
ZHANG JIANLONG

Assignees

天津青川科技发展有限公司

Dates

Publication Date: 20260508
Application Date: 20260120

Claims (10)

1. The method for predicting the survival rate of the seedlings after adversity stress is characterized by comprising the following steps: Step S1, determining a sampling time window of the seedling to be evaluated after being subjected to adversity stress; S2, collecting an annual branch sample of the seedling to be evaluated, and measuring the water content (WMC) of the branch; S3, substituting the measured water content (WMC) value of the branches into a model calculation by using a regression prediction model of the water content and the survival rate of the branches, and obtaining a prediction survival rate (SR_pred) of the seedlings to be evaluated; And S4, determining the survival grade of the nursery stock according to the numerical interval of the predicted survival rate (SR_pred), and matching with a corresponding grading maintenance strategy.
2. The method for predicting survival rate after stress of seedlings according to claim 1, wherein the method for constructing the regression prediction model in the step S3 is characterized in that similar tree species samples are selected and divided into a stress group and a control group, the water content of branches is measured in the same sampling time window, actual survival rate is counted in a subsequent growth period, and the quantitative relation between the water content of branches (WMC) and the survival rate is obtained through regression fitting.
3. The method for predicting survival rate after stress of seedlings according to claim 2, wherein the regression prediction model is a linear model: SR_ pred =a×WMC+b Wherein, WMC and SR_ pred are both normalized 0-1 small form, a is slope coefficient, b is intercept.
4. The method for predicting survival rate after stress of seedlings according to claim 3, wherein the value range of a is 2.20-2.30 and the value range of b is-0.15 to-0.10 for a mixed tree group comprising jujube, torch, poplar, mulberry, willow and elm.
5. The method for predicting the survival rate of seedlings after adversity stress according to claim 1, wherein in the step S1, the adversity stress type comprises at least one of water logging stress and drought stress, and the sampling time window is 15 th to 30 th days after the adversity stress is finished.
6. The method for predicting survival rate after stress of seedlings according to claim 1, wherein in the step S2, the branch sample is a middle section of annual branches, the length of the branch sample is 5-15 cm, the branch sample grows robustly, has no obvious plant diseases and insect pests and mechanical damage, and avoids tender shoots which are not fully lignified at the base, the branch and the top.
7. The method for predicting the survival rate of the seedlings after stress according to claim 3 or 4, wherein the method for measuring and calculating the water content of the branches is as follows: Weighing Fresh Weight (FW) of the shoot sample; drying the sample at 103-105 ℃ to constant weight, and weighing Dry Weight (DW); calculated as wmc= [ (FW-DW)/FW ] ×100%.
8. The method for predicting the survival rate of seedlings after stress according to claim 7, wherein in step S4, the matching criteria of the maintenance strategy are: when the predicted survival rate (SR_pred) is more than or equal to 80 percent, executing conventional maintenance measures; When the predicted survival rate (SR_pred) is more than or equal to 50 percent and less than 80 percent, executing the reinforced maintenance measures; When the predicted survival rate (sr_pred) <50%, rescue measures or replacement of seedlings are performed.
9. The method for predicting the survival rate of seedlings after adversity stress of claim 8, wherein the step of enhancing maintenance includes at least one of increasing watering frequency and applying rooting agent, and the step of rescuing includes at least one of heavy pruning and soil improvement.
10. The utility model provides a hierarchical maintenance system behind seedling adverse stress which characterized in that includes: the data acquisition module is used for acquiring fresh weight and dry weight data of the branch samples of the seedlings to be evaluated after stress; the water content calculating module is used for calculating the water content of the branches according to the collected fresh weight and dry weight data; The survival rate prediction module is internally stored with the regression prediction model according to any one of claims 1, 3 and 4 and is used for outputting the prediction survival rate according to the water content of the branches; and the maintenance decision module is used for outputting a corresponding maintenance strategy according to the predicted survival rate.

Description

Survival rate prediction method and grading maintenance system for seedlings after adversity stress Technical Field The invention relates to the technical field of forest physiology ecology and forestry cultivation, in particular to a survival rate prediction method and a grading maintenance system after stress of seedlings in adversity. Background In forestry planting, ecological restoration engineering and landscaping management, seedlings are subjected to stress such as waterlogging, drought and the like, so that physiological damage and even death of different degrees are caused. The survival potential of the disaster-stricken nursery stock is timely and accurately estimated, and the method has important significance for making scientific post-disaster remedial measures, reasonably arranging the supplementary planting and resource investment and reducing economic losses. The existing seedling survival rate assessment method mainly comprises the following steps: 1. Morphology observation method The common practice is to judge whether the nursery stock is still alive or not by visually observing appearance symptoms such as leaf wilting, color changing, curling or falling. The method has the following defects: (1) The subjective performance is strong, the judgment standards of different technicians on the wilting severity degree and the dying condition are inconsistent, and the evaluation result depends on experience and lacks of uniform quantization scale. (2) Symptoms appear later than physiological damage, namely, leaf loss and yellowing and abscission often occur after the plant has obvious physiological disorder. For example, under continuous drought conditions, the root system is first damaged by recessive damage such as water loss of fine roots and embolism of a catheter, and usually, appearance symptoms are only obvious after 7-15 days, but once water is not timely supplied within 10 days after drought occurs, irreversible necrosis can occur on a large number of fine roots, and even if watering is increased in the later period, the survival rate is difficult to recover to the level before disaster. For waterlogging, the root system rapidly decays in an anoxic environment, but the upper leaves of the ground can still keep a relatively 'normal' appearance for a short period of time, and erroneous judgment is easy to cause. This results in the remedial action often lagging, missing the optimal curing window. (3) The quantitative result cannot be provided, namely, morphological observation only can give qualitative impressions of good/bad and light/heavy, the possibility of survival is difficult to express in percentage form, and the follow-up refined water and fertilizer and manpower configuration decision is more difficult to support. 2. Growth index monitoring method Another type of method is to continuously measure the growth of tree height, ground diameter, crown width and the like, observe the growth trend of the seedlings in one or more growing seasons, and deduce the adaptation condition and survival possibility according to the growth trend. The main problems of this type of method are: (1) The time is very long, and a reliable conclusion can be formed after a complete growing season or even longer time, the evaluation period is different from a few months to one year, and the requirements for post-disaster rescue and planting decision making are obviously delayed. (2) The method is difficult to be used for early-stage rapid judgment after disaster, namely, the morphological growth quantity of the seedlings such as height, ground diameter and the like is not changed greatly within 1-2 months after waterlogging or drought stress occurs, and the potential death risk caused by adverse circumstances is difficult to be reflected by short-term repeated measurement, so that decision basis cannot be given in a critical window period. (3) The method is easy to be interfered by early-stage difference, the sizes of different seedlings at the initial stage of planting and the background growth vigor are different, the later-stage growth quantity change is influenced by adverse conditions and initial difference, and the seedlings are difficult to distinguish, so that the method is an indirect index with more hysteresis and confounding factors. 3. Physiological and biochemical index method Other studies use physiological and biochemical indicators such as chlorophyll fluorescence parameters, leaf water content, proline content, soluble sugars, antioxidant enzyme activity, etc. to evaluate the extent of stress injury. Although such methods can reflect physiological states more sensitively under laboratory conditions, there are significant limitations in practical popularization: (1) The method has the advantages of high detection cost and complex operation, and often needs special instruments such as a fluorescent instrument, a spectrophotometer and the like, and various chemical reagents, has complex sample