A rapid detection method for cold tolerance identification of walnut germplasm resources

Abstract

The invention relates to the technical field of walnut planting, and discloses a rapid detection method for cold resistance identification of walnut germplasm resources. The method comprises the following steps: first, selecting a plurality of walnut samples of different varieties, sterilizing the samples, placing the samples in environments of different temperatures for testing, setting the test time, and recording the test results to form a sample test data set; second, calculating the sample survival rate according to the sample test data set; third, calculating the comprehensive survival rate according to the sample survival rate; fourth, extracting samples from the walnut sample test data set to measure the relative conductivity of the samples; fifth, calculating the final cold resistance in combination with the relative conductivity of the samples and the sample survival rate; and sixth, selecting high-quality walnut germplasm resources according to the final cold resistance; and combining the comprehensive survival rate and the relative conductivity to dynamically monitor the physiological changes of walnut trees at low temperatures, thereby more comprehensively evaluating the cold resistance of the walnut trees.

Description

Technical Field

[0001] The invention relates to the technical field of walnut planting, in particular to a rapid detection method for identifying the cold resistance of walnut germplasm resources. Background Art

[0002] Walnuts are suitable for growing in deep, loose, fertile sandy loam. Deep plowing is required before sowing, and sufficient base fertilizer should be applied to provide adequate nutrition. With the increasing demand of consumers for healthy food, the market demand for walnuts, as a nut rich in nutrients, has steadily increased. Walnuts can not only be eaten directly, but also processed into a variety of products, such as kernel oil, walnut milk, etc., which increases its market added value. Walnut planting is not just a branch of agricultural production, it is related to a wide range of social and economic activities and ecological environment construction. For farmers and enterprises engaged in walnut planting, understanding and mastering advanced planting techniques, market trends and policy orientations will help improve production efficiency and economic benefits, while also providing consumers with more high-quality walnut products.

[0003] Different varieties of walnuts have different requirements for cold resistance. In order to allow walnuts to grow in areas with lower temperatures and to ensure the survival rate and fruiting rate of walnuts in low temperature areas, it is necessary to identify the cold resistance of walnut germplasm resources. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a rapid detection method for identifying the cold resistance of walnut germplasm resources. It has the advantages of combining comprehensive survival rate and relative conductivity, and can dynamically monitor the physiological changes of walnut trees under low temperatures, thereby more comprehensively evaluating their cold resistance, more comprehensively analyzing the relationship between temperature and cold resistance, and more accurately judging the cold resistance limit of varieties.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a rapid detection method for identifying the cold resistance of walnut germplasm resources, comprising the following steps:

[0008] Step 1: Select multiple walnut samples of different varieties, disinfect them, and place them in different temperature environments for testing. Set the experimental time and record the experimental results to form a sample experimental data set. ;

[0009] Step 2: Based on the sample experimental data set Calculate the sample survival rate;

[0010] Step 3: Calculate the comprehensive survival rate based on the sample survival rate;

[0011] Step 4: extract samples from the walnut sample experimental data set to measure the relative conductivity of the samples;

[0012] Step 5: Calculate the final cold resistance by combining the relative conductivity of the sample with the survival rate of the sample;

[0013] Step six: Select high-quality walnut germplasm resources based on their ultimate cold resistance.

[0014] Preferably, in step 1, the sample experimental data set The expression is:

[0015] in, and These are the first and last sample experimental data at low temperature; and These are the first and last sample experimental data at extremely low temperatures; and The experimental data for the first and last samples at cold temperatures; and The first and last sample experimental data under extremely cold temperature; The representative sample experimental data is Species; upper right Represents the experimental time of the corresponding sample experimental data; the upper right Represents a specific temperature value.

[0016] Preferably, in step 2, the calculation formula for the sample survival rate is as follows:

[0017] In the above formula, The sample experimental data set Middle The sample survival rate of the experimental data of samples; It is the specific number of walnut germplasm that survived at low temperature in the sample experimental data; It is the specific number of walnut germplasm that survived under extremely low temperature in the sample experimental data; It is the specific number of walnut germplasm that survived under cold temperature in the sample experimental data; is the specific number of walnut germplasm that survived under extremely cold temperatures in the sample experimental data; 10 is the specific number of experiments in each group of different temperatures in the sample experimental data.

[0018] Preferably, in step 3, the calculation formula of the comprehensive survival rate is as follows:

[0019] In the above formula, The sample experimental data set Middle The comprehensive survival rate of experimental samples under different test cycles; The sample experimental data set Middle The survival rate of experimental samples in a short experimental period; The sample experimental data set Middle The survival rate of experimental samples in the mid-term experimental period; The sample experimental data set Middle The survival rate of experimental samples in the long-term experimental period; The sample experimental data set Middle The survival rate of experimental samples during the ultra-long experimental period.

[0020] Preferably, in step 4, the calculation steps of relative conductivity are as follows:

[0021] S1, extract samples at different temperature cycles and different experimental times;

[0022] S2. Restoring the sample at room temperature for 1 hour to gradually restore the sample temperature to room temperature;

[0023] S3. Conductivity measurement of the recovered sample and calculate relative conductivity.

[0024] Preferably, the specific steps of S3 are as follows:

[0025] S3.1. Soak the sample for 24 hours;

[0026] S3.2. After soaking, use a conductivity meter to measure the initial conductivity of the sample soaking solution and mark it as ;

[0027] S3.3. After measuring the initial conductivity, place the vial containing the soaked sample in water and heat it to a temperature above 100°C and maintain it for 20 minutes until the sample is completely dead;

[0028] S3.4. Wait for the sample to cool naturally and measure the conductivity of the soaking solution again. ;

[0029] S3.5. Calculate the relative conductivity by using the initial conductivity and the conductivity of the immersion solution.

[0030] Preferably, in S3.1, the specific method of soaking the sample is:

[0031] Take one gram of sample and place it in a clean vial. Pour 20 ml of deionized water to completely immerse the sample and then seal it.

[0032] Preferably, in S3.5, the calculation formula of relative conductivity is as follows:

[0033] In the above formula, For the Relative conductivity of the experimental samples; The conductivity of the immersion solution of the experimental sample is measured at low temperature, extremely low temperature, cold temperature and extremely cold temperature respectively; The conductivity of the immersion solution of the experimental sample is measured in a short-term experimental period, a medium-term experimental period, a long-term experimental period, and an ultra-long-term experimental period; are the initial conductivity of the experimental sample at low temperature, extremely low temperature, cold temperature, and extremely cold temperature respectively; is the initial conductivity of the experimental sample in the short-term experimental period, medium-term experimental period, long-term experimental period and ultra-long-term experimental period respectively.

[0034] Preferably, in step 5, the final cold resistance is calculated as follows:

[0035] In the above formula, is the comprehensive survival rate under normal conditions, is the relative conductivity under normal conditions.

[0036] Preferably, in step six, during the selection of high-quality walnut germplasm resources, the sample with the highest final cold resistance is marked as a high-quality walnut germplasm resource, and a report on high-quality walnut germplasm resources is generated.

[0037] Compared with the prior art, the present invention provides a rapid detection method for identifying the cold resistance of walnut germplasm resources, which has the following beneficial effects:

[0038] 1. The present invention selects walnut samples of multiple different varieties and conducts experiments at different temperatures. Since different walnut varieties have genetic differences, these differences may cause them to respond differently to low temperature stress. By conducting experiments on multiple varieties at different temperatures, the cold resistance of each variety can be more comprehensively understood, providing a scientific basis for breeding varieties with strong cold resistance. By calculating the comprehensive survival rate of samples at different temperatures, the survival of different walnut tree varieties under the same low temperature conditions can be intuitively compared, thereby screening out varieties with strong cold resistance, comprehensively evaluating the cold resistance of peach germplasm resources, and providing strong support for variety selection, cultivation management, industrial upgrading and scientific research. Combined with the comprehensive survival rate and relative conductivity, the physiological changes of walnut trees at low temperatures can be dynamically monitored, thereby more comprehensively evaluating their cold resistance, more comprehensively analyzing the relationship between temperature and cold resistance, and more accurately judging the cold resistance limit of varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figure 1 A rapid detection method for identifying the cold resistance of walnut germplasm resources comprises the following steps:

[0042] Step 1: Select multiple walnut samples of different varieties, sterilize them, and place them in different temperature environments for testing. Set the experimental time and record the experimental results to form a sample experimental data set. ;

[0043] Sample Experimental Dataset The expression is:

[0044] in, and are the first and last sample experimental data at low temperature, and The first and last sample experimental data at extremely low temperature, and The first and last sample experimental data at cold temperature, and The first and last sample experimental data under extremely cold temperature, subscript The representative sample experimental data is Species, upper right Represents the experimental time of the corresponding sample experimental data, the upper right Represents specific temperature values. In the sample experimental data under low temperatures, the specific temperature values ​​are 0℃, 1℃, 2℃, 3℃, 4℃, and 5℃ respectively. In the sample experimental data under extremely low temperatures, the specific temperature values ​​are -1℃, -2℃, -3℃, ..., and -10℃ respectively. In the sample experimental data under cold temperatures, the specific temperature values ​​are -11℃, -12℃, -13℃, ..., and -20℃ respectively. In the sample experimental data under extremely cold temperatures, the specific temperature values ​​are -21℃, -22℃, -23℃, ..., and -30℃ respectively.

[0045] By selecting walnut samples of different varieties and conducting experiments at different temperatures, we can gain a more comprehensive understanding of the cold resistance of each variety by conducting experiments on multiple varieties at different temperatures, as different walnut varieties have genetic differences, which may lead to different responses to low temperature stress. This provides a scientific basis for breeding varieties with strong cold resistance.

[0046] Step 2: Based on the sample experimental data set Calculate the sample survival rate;

[0047] The calculation formula of sample survival rate is as follows:

[0048] In the above formula, The sample experimental data set Middle The sample survival rate of the experimental data of samples, is the specific number of walnut germplasm that survived at low temperatures in the sample experimental data, is the specific number of walnut germplasm that survived at extremely low temperatures in the sample experimental data. is the specific number of walnut germplasm that survived under cold temperatures in the sample experimental data. is the specific number of walnut germplasm that survived under extremely cold temperatures in the sample experimental data, and 10 is the specific number of experiments in each group of different temperatures in the sample experimental data;

[0049] Step 3: Calculate the comprehensive survival rate based on the sample survival rate;

[0050] The calculation formula of comprehensive survival rate is as follows:

[0051] In the above formula, The sample experimental data set Middle The comprehensive survival rate of experimental samples under different test cycles, The sample experimental data set Middle The survival rate of experimental samples in a short experimental period, The sample experimental data set Middle The survival rate of experimental samples in the mid-term experimental period, The sample experimental data set Middle The survival rate of experimental samples in the long-term experimental period, The sample experimental data set Middle The survival rate of experimental samples in the ultra-long experimental period;

[0052] By calculating the comprehensive survival rate of samples at different temperatures, we can visually compare the survival of different walnut tree varieties under the same low temperature conditions, thereby screening out varieties with stronger cold resistance, comprehensively evaluating the cold resistance of peach germplasm resources, and providing strong support for variety selection, cultivation management, industry upgrading and scientific research;

[0053] Step 4: extract samples from the walnut sample experimental data set to measure the relative conductivity of the samples;

[0054] The steps for calculating relative conductivity are as follows:

[0055] S1, extract samples at different temperature cycles and different experimental times;

[0056] S2. Restoring the sample at room temperature for 1 hour to gradually restore the sample temperature to room temperature;

[0057] S3, measuring the conductivity of the recovered sample and calculating the relative conductivity;

[0058] S3.1. Soak the sample for 24 hours;

[0059] The specific method of immersing the sample is as follows:

[0060] Take one gram of sample and place it in a clean vial. Pour 20 ml of deionized water to completely immerse the sample and then seal it.

[0061] S3.2. After soaking, use a conductivity meter to measure the initial conductivity of the sample soaking solution and mark it as ;

[0062] S3.3. After measuring the initial conductivity, place the vial containing the soaked sample in water and heat it to a temperature above 100°C and maintain it for 20 minutes until the sample is completely dead;

[0063] S3.4. Wait for the sample to cool naturally and measure the conductivity of the soaking solution again. ;

[0064] S3.5, calculating the relative conductivity by the initial conductivity and the conductivity of the immersion solution;

[0065] The calculation formula of relative conductivity is as follows:

[0066] In the above formula, For the The relative conductivity of the experimental samples, The conductivity of the soaking liquid of the experimental sample was measured at low temperature, extremely low temperature, cold temperature, and extremely cold temperature. The conductivity of the immersion solution of the experimental sample is measured in the short-term experimental period, the medium-term experimental period, the long-term experimental period, and the ultra-long-term experimental period. is the initial conductivity of the experimental sample at low temperature, extremely low temperature, cold temperature, and extremely cold temperature, is the initial conductivity of the experimental sample in the short-term experimental period, the medium-term experimental period, the long-term experimental period, and the ultra-long-term experimental period;

[0067] Identifying the cold resistance of peach germplasm resources through relative conductivity can not only quickly and accurately evaluate the cold resistance of varieties, but also provide strong support for variety selection, cultivation management and scientific research;

[0068] Step 5: Calculate the final cold resistance by combining the relative conductivity of the sample with the survival rate of the sample;

[0069] The final cold tolerance is calculated as follows:

[0070] In the above formula, is the comprehensive survival rate under normal conditions, is the relative conductivity under normal conditions;

[0071] By combining comprehensive survival rate and relative conductivity, the physiological changes of walnut trees under low temperatures can be dynamically monitored, so as to more comprehensively evaluate their cold resistance, more comprehensively analyze the relationship between temperature and cold resistance, and more accurately determine the cold resistance limit of varieties;

[0072] Step 6. Mark the sample with the highest cold resistance as a high-quality walnut germplasm resource and generate a report on high-quality walnut germplasm resources.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A rapid detection method for identifying the cold resistance of walnut germplasm resources, characterized in that: The following steps are involved: Step 1: Select multiple walnut samples of different varieties, sterilize them and place them in different temperature environments for testing, set the experimental time, and record the experimental results to form a sample experimental data set SYSJ; Step 2: Calculate the sample survival rate based on the sample experimental data set SYSJ; the calculation formula for the sample survival rate is as follows: In the above formula, YBCH i is the sample survival rate of the i-th sample experimental data in the sample experimental data set SYSJ; D v JD is the specific number of walnut germplasm that survived at low temperature in the sample experimental data; v is the specific number of walnut germplasm that survived at extremely low temperatures in the sample experimental data; H v is the specific number of walnut germplasm that survived under cold temperature in the sample experimental data; JH v is the specific number of walnut germplasm that survived under extremely cold temperatures in the sample experimental data; 10 is the specific number of experiments in each group of different temperatures in the sample experimental data; Step 3: Calculate the comprehensive survival rate based on the sample survival rate; the calculation formula for the comprehensive survival rate is as follows: In the above formula, ZHCH i is the comprehensive survival rate of the i-th experimental sample in the sample experimental data set SYSJ under different experimental cycles; is the survival rate of the i-th experimental sample in the sample experimental data set SYSJ in the short-term experimental period; is the survival rate of the i-th experimental sample in the sample experimental data set SYSJ during the mid-term experimental period; is the survival rate of the i-th experimental sample in the sample experimental data set SYSJ during the long-term experimental period; is the survival rate of the i-th experimental sample in the sample experimental data set SYSJ during the ultra-long experimental period; Step 4: Extract samples from the walnut sample experimental data set to measure the relative conductivity of the samples; the calculation steps of the relative conductivity are as follows: S1, extract samples of different temperature cycles and different experimental times; S2, restore the extracted sample at room temperature for 1 hour to gradually restore the sample temperature to room temperature; S3, measuring the conductivity of the recovered sample and calculating the relative conductivity; The specific steps of S3 are as follows: S3.

1. Soak the sample for 24 hours; S3.

2. After soaking, use a conductivity meter to measure the initial conductivity of the sample soaking solution and mark it as HY c ; S3.

3. After measuring the initial conductivity, place the vial containing the soaked sample in water and heat it to a temperature above 100°C, and keep it for 20 minutes until the sample is completely dead; S3.

4. Wait for the sample to cool naturally, and measure the conductivity of the soaking liquid again and mark it as HY z ; S3.

5. Calculate the relative conductivity by the initial conductivity and the conductivity of the immersion solution. The calculation formula of the relative conductivity is as follows: In the above formula, XDDD i is the relative conductivity of the i-th experimental sample; The comprehensive conductivity of the immersion solution of the test sample at low temperature, very low temperature, cold temperature and extremely cold temperature; The comprehensive conductivity of the immersion solution of the experimental sample in the short-term experimental period, the medium-term experimental period, the long-term experimental period, and the ultra-long-term experimental period; is the comprehensive initial conductivity of the experimental sample at low temperature, extremely low temperature, cold temperature, and extremely cold temperature; is the comprehensive initial conductivity of the experimental sample in the short-term experimental period, the medium-term experimental period, the long-term experimental period, and the ultra-long-term experimental period; Step 5. Calculate the final cold resistance by combining the relative conductivity of the sample with the survival rate of the sample. The calculation method of the final cold resistance is as follows: In the above formula, is the comprehensive survival rate under normal conditions; is the relative conductivity under normal conditions; Step six: Select high-quality walnut germplasm resources based on their ultimate cold resistance.

2. The rapid detection method for cold resistance identification of walnut germplasm resources according to claim 1, characterized in that: In step 1, the expression of the sample experimental data set SYSJ is: in, and are the first and last sample experimental data at low temperature, and These are the first and last sample experimental data at extremely low temperatures; and The experimental data for the first and last samples at cold temperatures; and These are the first and last sample experimental data under extremely cold temperature; the subscript n represents that there are n types of sample experimental data; the superscript s represents the experimental time of the corresponding sample experimental data; and the superscript w represents the specific temperature value.

3. The rapid detection method for cold resistance identification of walnut germplasm resources according to claim 1, characterized in that: In S3.1, the specific method of soaking the sample is: Take one gram of sample and place it in a clean vial, pour in 20 ml of deionized water to completely soak the sample and then seal it.

4. The rapid detection method for cold resistance identification of walnut germplasm resources according to claim 1, characterized in that: In step six, during the selection of high-quality walnut germplasm resources, the sample with the highest final cold resistance is marked as a high-quality walnut germplasm resource, and a report on high-quality walnut germplasm resources is generated.

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