A method, system, and readable medium for screening remediation measures for degraded grasslands

Abstract

The present application belongs to the field of ecological environment governance and restoration technology, and relates to a method, system and readable medium for screening restoration measures of degraded grassland, comprising: setting a single measure group, a full combination measure group and a plurality of excluded combination measure groups; respectively measuring the ecosystem function indexes of the three measure groups; calculating the effect value of the single measure group, the full combination measure group and the excluded combination relative to the control group according to the measured effect value, and judging whether there is a strong recovery measure; comparing the theoretical effect value of the full combination measure group with the measured effect value, and judging whether there is a synergistic effect among the measures; if there is, comparing the theoretical effect value of the excluded combination measure group with the measured effect value, obtaining a difference value, and judging whether there is a measure that can play a strong synergy; and screening one or more measure combinations for restoring degraded grassland. The present application overcomes the defects of high cost and inability to analyze complex interactions of traditional factorial experiment design, and can provide scientific guidance for the precise restoration of degraded grassland and other ecosystems.

Description

Technical Field

[0001] This invention relates to a method, system, and readable medium for screening degraded grassland restoration measures, belonging to the field of ecological environment governance and restoration technology. Background Technology

[0002] Unreasonable human use and exacerbated climate change have led to varying degrees of grassland degradation. Restoring degraded grasslands is crucial for maintaining stable grassland productivity. Currently, the restoration of degraded grasslands largely relies on a combination of interventions. However, complex interactions, such as synergistic, antagonistic, or cumulative effects, may exist between different interventions, influencing the final restoration outcome of the combined measures. Traditional factorial experimental designs struggle to systematically analyze these interactions, and the cost of conducting field control experiments rises sharply with the number of interventions. Therefore, selecting highly efficient restoration measures that possess both strong restoration effects and strong synergistic effects in combination is key to improving grassland restoration efficiency and reducing restoration costs. Summary of the Invention

[0003] To address the aforementioned problems, the purpose of this invention is to provide a method, system, and readable medium for screening degraded grassland restoration measures. This overcomes the shortcomings of traditional analyses, such as high experimental design costs and the inability to resolve complex interactions. The invention provides a systematic, efficient, and economical framework for screening restoration measures, offering scientific guidance for the precise restoration of degraded grasslands and other ecosystems.

[0004] To achieve the above objectives, the present invention proposes the following technical solution: a method for screening degraded grassland restoration measures, comprising the following steps: setting up a single-measure group, a full-combination measure group, and multiple exclusion combination measure groups; measuring the ecosystem function indicators of the single-measure group, the full-combination measure group, and the exclusion combination; calculating the effect values ​​and confidence intervals of the single-measure group, the full-combination measure group, and the exclusion combination relative to the control group based on the ecosystem function indicators, and determining whether there are strong restoration measures; comparing the theoretical effect value and the measured effect value of the full-combination measure group to determine whether there is a synergistic effect among the measures; when there is a synergistic effect among the measures, comparing the theoretical effect value and the measured effect value of the exclusion combination measure group to obtain the difference and confidence interval, and determining whether there are measures that exert a strong synergistic effect; and screening out one or more combinations of measures for restoring degraded grassland with strong restoration measures and strong synergistic effects based on the effect values ​​and differences.

[0005] Furthermore, the criteria for selecting one or more combinations of measures to restore degraded grasslands are: the lower limit of the confidence interval of the effect value of a single measure group is greater than 0; and the confidence interval of the difference does not contain 0 and is a positive value.

[0006] Furthermore, the remediation measures in the measures group include, but are not limited to, at least two of the following: irrigation, fertilization, microbial inoculation, removal of surrounding vegetation, and soil loosening and rakeing.

[0007] Furthermore, the ecosystem function indicators include at least two of the following: plant growth indicators, soil physicochemical property indicators, and biodiversity indicators.

[0008] Furthermore, the effect size and confidence interval of the single-measure group, the full-combination measure group, and the exclusion combination relative to the control group are calculated using the nonparametric Bootstrap resampling method; the theoretical effect size of the full-combination measure group is calculated using a null model, which includes additive models, multiplicative models, and / or dominant models.

[0009] Furthermore, the formula for calculating the effect value is as follows:

[0010] , in, For processing group The average value of a certain response index, This represents the average value of the response index for the control group.

[0011] Furthermore, the theoretical effect value of the exclusion combination measures group The calculation formula is: , in, This represents the measured effect value for the entire combination of measures. For the first The effect value of a single measure group.

[0012] Furthermore, the difference The calculation formula is: , in, To exclude the measured effect values ​​of the combined measures group.

[0013] This invention also discloses a system for screening degraded grassland restoration measures, comprising: a measure group setting module for setting single measure groups, full combination measure groups, and multiple exclusion combination measure groups; a functional index measurement module for measuring the ecosystem function indicators of the single measure groups, full combination measure groups, and exclusion combinations; an effect value calculation module for calculating the effect values ​​and confidence intervals of the single measure groups, full combination measure groups, and exclusion combinations relative to the control group based on the ecosystem function indicators, and determining whether there are strong restoration measures; a synergistic effect judgment module for comparing the theoretical effect value and the measured effect value of the full combination measure group to determine whether there is a synergistic effect among the measures; a difference calculation module for comparing the theoretical effect value and the measured effect value of the exclusion combination measure group when there is a synergistic effect among the measures, obtaining the difference and confidence interval, and determining whether there are measures that exert a strong synergistic effect; and a measure screening module for screening one or more measure combinations that have strong restoration measures and strong synergistic effects for restoring degraded grassland based on the effect values ​​and differences.

[0014] The present invention also discloses a computer-readable storage medium storing a computer program, which is executed by a processor to implement the method for screening degraded grassland restoration measures as described in any of the preceding claims.

[0015] The technical solution of the present invention has at least the following technical effects or advantages: (1) The technical method in this invention is universal and can be applied to the selection of restoration measures for different degraded grasslands. It is of great significance to improve grassland restoration efficiency and reduce restoration costs. It is suitable for large-scale promotion on degraded natural grasslands.

[0016] (2) This invention combines effect value estimation with zero model comparison to provide a statistically robust and efficient method for identifying measures; through the “exclusion combination” design, it can effectively analyze the interaction between measures under a limited experimental scale, and can simultaneously identify measures with strong individual effects and strong synergistic effects, which is especially suitable for the optimal configuration of multiple measures.

[0017] (3) This invention overcomes the shortcomings of traditional factorial experimental design, such as high cost and inability to resolve complex interactions, and can provide scientific guidance for the precise restoration of degraded grasslands and other ecosystems. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for screening degraded grassland restoration measures in one embodiment of the present invention; Figure 2 This is a framework diagram of the screening experiment design in one embodiment of the present invention; Figure 3 This is a flowchart of the screening and statistical analysis of remedial measures in one embodiment of the present invention; Figure 4 This is a schematic diagram showing the effects of different restoration measures and their combinations on the growth of degraded grassland plants in one embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of this invention, specific embodiments are described in detail. However, it should be understood that the specific embodiments are provided solely for the purpose of better understanding this invention and should not be construed as limiting it. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0020] To address the shortcomings of existing technologies, such as high cost of factorial experimental design and inability to analyze complex interactions, this invention proposes a method, system, and readable medium for screening degraded grassland restoration measures. The method includes the following steps: setting up single-measure groups, full-combination measure groups, and multiple exclusion combination measure groups; measuring ecosystem function indicators for the single-measure groups, full-combination measure groups, and exclusion combinations; calculating the effect values ​​and confidence intervals of the single-measure groups, full-combination measure groups, and exclusion combinations relative to the control group based on the ecosystem function indicators; comparing the theoretical effect value and the measured effect value of the full-combination measure group to determine if there is a synergistic effect among the measures; when there is a synergistic effect among the measures, comparing the theoretical effect value and the measured effect value of the exclusion combination measure group to obtain the difference and confidence interval; and screening one or more combinations of measures for restoring degraded grassland based on the effect values ​​and differences. This invention screens measures that have a stable and significant promoting effect on multiple ecosystem functions based on single-measure experiments. Based on combined experiments and interaction analysis, it identifies measures that make a positive synergistic contribution to the overall restoration effect when multiple measures are implemented in synergy. This overcomes the shortcomings of traditional factorial experimental designs, such as high cost and inability to analyze complex interactions, and can provide scientific guidance for the precise restoration of degraded grasslands and other ecosystems. The following examples illustrate the invention in detail.

[0021] Example 1 This embodiment illustrates a typical degraded grassland in the Tenihe Farmland Research Area (49°28′N, 120°10′E) of Hulunbuir City, Inner Mongolia. This area has a temperate continental monsoon climate with an average annual precipitation of approximately 352 mm and an average annual temperature of approximately -0.6°C. Long-term overgrazing in the experimental area has led to soil structure damage, deterioration of water and fertilizer conditions, and a significant decline in productivity and biodiversity. This embodiment discloses a method for screening degraded grassland restoration measures, such as... Figure 1 , Figure 2 As shown, it includes the following steps: S1 selects several repair measures and sets up a single measure group, a full combination measure group, and a control group.

[0022] The remediation measures in the treatment group include, but are not limited to, at least two of the following: irrigation, fertilization, microbial inoculation, removal of surrounding vegetation, and soil loosening. The control group is the group that did not receive any treatment.

[0023] This embodiment selects five restoration measures aimed at promoting the establishment of native leguminous plant *Alfalfa*, including: Irrigation: Supplement with 30 L·m³ of water weekly. - ², to alleviate drought stress.

[0024] Fertilization: Based on the soil basal level, apply nitrogen, phosphorus, and potassium fertilizer (8 g Nm³) in a single application. - ², 3.6 g P m - ², 9 g Km - ²).

[0025] Insect-proof netting: Use 60-mesh nylon netting to protect seedlings from insect damage.

[0026] Microbial inoculation: Inoculate with a mixture of rhizobium and arbuscular mycorrhizal fungi to promote symbiotic nitrogen fixation and nutrient absorption.

[0027] Removal of surrounding vegetation: removing competing vegetation by digging ditches.

[0028] All control groups employed a fully randomized block design, with a total of 5 blocks (replicated). Within each block, 7 experimental plots (3m × 5m) were randomly assigned, with a 2-meter interval between plots. A total of 35 experimental plots were created. The 7 experimental plots corresponded to 5 single-control groups, 1 combined control group, and 1 control group.

[0029] S2 measures ecosystem function indicators for single-measure groups, combined-measure groups, and control groups.

[0030] Ecosystem function indicators include, but are not limited to, at least one of plant growth indicators, soil physicochemical properties indicators, and biodiversity indicators. In this embodiment, the target species, alfalfa, was used as the core to measure its growth response. Ecosystem function indicators included: Growth rate: Plant height was measured periodically, and the average daily growth was calculated. Aboveground biomass: After the experiment, the aboveground parts of the plants in the quadrats were harvested, dried, and weighed. Underground biomass and root length: The soil was excavated, the root system was cleaned, root length was scanned and analyzed, and then dried and weighed.

[0031] S3 calculates the effect size and confidence interval of the single-measure group and the combined-measure group relative to the control group based on ecosystem function indicators.

[0032] like Figure 3As shown, the nonparametric Bootstrap resampling method was used to calculate the effect size and confidence interval of the single-measure group and the combined-measure group relative to the control group. In this embodiment, the Bootstrap resampling was performed 1000 times. Specifically, for each indicator, an indicator result with replacement was drawn from the single-measure group, the combined-measure group, and the control group with the same number of replicates as the original sample, and their mean was calculated; the above process was repeated 1000 times.

[0033] The formula for calculating the effect size is: , in, For processing group The average value of a certain response index, This represents the average value of the response index for the control group.

[0034] By comparing the effect values ​​of different single-measure groups, remediation measures with strong recovery effects were screened. The strength of the individual recovery effect was determined by comparing whether the 95% confidence interval of each single measure's effect value was greater than 0. The analysis showed that the effect value of the "removal of surrounding plants" measure had significantly higher lower limits of the 95% confidence intervals for multiple growth indicators (aboveground biomass, belowground biomass, and root length) than 0, indicating that it has a strong individual recovery effect and can be identified as a key measure.

[0035] S4 compares the theoretical effect value with the measured effect value of the entire combination of measures to determine whether there is a synergistic effect among the measures.

[0036] The theoretical effect value of the entire combination of measures was calculated using a null model, which includes additive, multiplicative and / or dominant models.

[0037] The calculation formula for the additive model is:

[0038] The formula for calculating the multiplicative model is:

[0039] The formula for calculating the dominance model is:

[0040] in, This represents the theoretical effect value for the entire combination of measures. For the first i The effect size of a single measure group m This represents the total number of measures.

[0041] Three null models (additive, multiplicative, and dominant) were used to predict the theoretical effect of the combined treatment. The observed effect of the combined treatment was compared with the predicted effect of the three models, and the overlap of their 95% confidence intervals was used for judgment. The 95% confidence interval of the observed effect of the combined treatment on aboveground biomass indicators was significantly higher than the upper confidence interval of the predicted values ​​of the three null models. This indicates that the combined implementation of the five measures produced a significant positive synergistic effect, which far exceeded the simple summation or maximum effect of the individual measures.

[0042] When there is a synergistic effect among the measures, S5 compares the theoretical effect value of the excluded combined measures group with the measured effect value to obtain the difference and confidence interval.

[0043] Excluding the theoretical effect value of the combined measures group The calculation formula is: , in, This represents the measured effect value for the entire combination of measures. For the first The effect value of a single measure group.

[0044] Difference The calculation formula is: , in, To exclude the measured effect values ​​of the combined measures group.

[0045] S6 uses effect values ​​and differences to screen out one or more combinations of measures that have strong restoration effects and strong synergistic effects for restoring degraded grasslands.

[0046] like Figure 4 As shown, it discloses the relationship between ecosystem function indicators and aboveground biomass, belowground biomass, root length, and plant growth rate of single-measure groups, combined-measure groups, and control groups. The criteria for selecting one or more combinations of measures for restoring degraded grasslands are: the lower limit of the confidence interval for the effect value of the single-measure group is greater than 0; and the confidence interval for the difference value does not contain 0 and is positive.

[0047] Comprehensive assessment of key measures: In this embodiment, "removal of surrounding vegetation" was identified as a key remediation measure due to its strong individual restoration effect. The combined measures demonstrated a significant synergistic effect, proving the value of applying multiple measures in combination.

[0048] While traditional full factorial designs can identify strong individual effects and verify the existence of combined synergies, they cannot quantify the specific contribution of each measure to the overall synergistic effect, i.e., they cannot distinguish which measure(s) primarily drive the synergistic effect. This result highlights the necessity and superiority of the method of this invention.

[0049] Example 2 Based on the same inventive concept, this embodiment discloses a system for screening degraded grassland restoration measures, comprising: The measure group setting module is used to set up single measure groups, full combination measure groups, and multiple exclusion combination measure groups; The functional index measurement module is used to measure the ecosystem functional indicators of single measure groups, full combination measure groups, and excluded combinations. The effect value calculation module is used to calculate the effect value and confidence interval of the single measure group, the full combination of measures group, and the exclusion combination relative to the control group based on the ecosystem function indicators. The synergy effect assessment module compares the theoretical effect value of the entire combination of measures with the measured effect value to determine whether there is a synergy effect among the measures. The difference calculation module is used to compare the theoretical effect value and the measured effect value of the exclusion combination measure group when there is a synergistic effect between the measures, and to obtain the difference and confidence interval. The measure screening module is used to screen one or more combinations of measures to restore degraded grasslands based on the effect value and the difference.

[0050] Example 3 Based on the same inventive concept, this embodiment discloses a computer-readable storage medium storing a computer program, which is executed by a processor to implement the method for screening degraded grassland restoration measures as described above.

[0051] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific embodiments of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims

1. A method for screening restoration measures for degraded grasslands, characterized in that, Includes the following steps: Set up single-measure groups, full-combination measure groups, and multiple exclusion combination measure groups; The ecosystem function indicators of the single-measure group, the full combination of measures group, and the excluded combination were measured; Based on the ecosystem function indicators, calculate the effect values ​​and confidence intervals of the single-measure group, the full combination of measures group, and the exclusion combination relative to the control group to determine whether there are strong recovery measures. Compare the theoretical effect value with the measured effect value of the entire combination of measures to determine whether there is a synergistic effect among the measures; When there is a synergistic effect among the measures, the theoretical effect value of the excluded combined measures group is compared with the measured effect value to obtain the difference and confidence interval, and to determine whether there are measures that play a strong synergistic role. Based on the effect values ​​and differences, one or more combinations of measures that have strong restoration effects and strong synergistic effects are selected for restoring degraded grasslands.

2. The method for screening degraded grassland restoration measures as described in claim 1, characterized in that, The criteria for selecting one or more combinations of measures to restore degraded grasslands are: the lower limit of the confidence interval of the effect value of a single measure group is greater than 0; and the confidence interval of the difference does not contain 0 and is a positive value.

3. The method for screening degraded grassland restoration measures as described in claim 1, characterized in that, The remediation measures in the measures group include at least two of the following: irrigation, fertilization, microbial inoculation, removal of surrounding vegetation, and soil loosening.

4. The method for screening degraded grassland restoration measures as described in claim 1, characterized in that, The ecosystem function indicators include at least one of the following: plant growth indicators, soil physicochemical properties indicators, and biodiversity indicators.

5. The method for screening degraded grassland restoration measures as described in claim 1, characterized in that, The effect size and confidence interval of the single-measure group, the full-combination measure group, and the exclusion combination relative to the control group were calculated using the nonparametric Bootstrap resampling method. The theoretical effect size of the full-combination measure group was calculated using a null model, which includes additive, multiplicative, and / or dominant models.

6. The method for screening degraded grassland restoration measures as described in claim 5, characterized in that, The formula for calculating the effect value is: ， in, For processing group The average value of a certain response index, This represents the average value of the response index for the control group.

7. The method for screening degraded grassland restoration measures as described in claim 5, characterized in that, The theoretical effect value of the exclusion combination measures group The calculation formula is: ， in, This represents the measured effect value for the entire combination of measures. For the first The effect value of a single measure group.

8. The method for screening degraded grassland restoration measures as described in claim 5, characterized in that, The difference The calculation formula is: ， in, To exclude the measured effect values ​​of the combined measures group.

9. A system for screening restoration measures for degraded grasslands, characterized in that, include: The measure group setting module is used to set up single measure groups, full combination measure groups, and multiple exclusion combination measure groups; The functional index measurement module is used to measure the ecosystem functional indicators of the single measure group, the full combination of measures group, and the excluded combination; The effect value calculation module is used to calculate the effect value and confidence interval of the single measure group, the full combination of measures group, and the exclusion combination relative to the control group based on the ecosystem function indicators, and to determine whether there are strong recovery measures. The synergy effect assessment module compares the theoretical effect value of the entire combination of measures with the measured effect value to determine whether there is a synergy effect among the measures. The difference calculation module is used to compare the theoretical effect value and the measured effect value of the exclusion combination of measures when there is a synergistic effect between the measures, obtain the difference and confidence interval, and determine whether there are measures that play a strong synergistic role. The measure screening module is used to screen one or more measures combinations that have strong restoration measures and strong synergistic effects for restoring degraded grasslands based on the effect value and difference value.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method for screening degraded grassland restoration measures as described in any one of claims 1-8.

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