Screening method of rice varieties with high yield and low methane emission

Abstract

The invention discloses a method for screening high-yield low-methane-emission rice varieties, which comprises the following steps: 1) counting the root diameter in the late tillering stage of rice, the content of malic acid and succinic acid in the root, and the proportion of ventilation tissues in the cross sectional area in the booting stage, and recording; 2) measuring the naphthylamine oxidation intensity of the root system at the booting stage; 3) measuring the pith cavity diameters of the first, second and third internodes of the main stem in the full heading stage; and 4) calculating the methane emission index Y = 30.748-25.436 * 1-1.152 * 2-1.58 * 3-0.779 * 4 + 7.385 * 5 + 51.953 * 6-2.579 * 7 by using a formula, wherein the rice with Y less than 15 kg / t is the target rice with high yield and low methane emission. According to the method, the correlation between methane emission and key phenotypic characters is established, key indexes are screened, and the regression model is established for the first time, so that the high-yield and low-methane-emission rice varieties are screened by observing apparent characters and simple and easy-to-operate detection indexes, the labor intensity can be reduced, the high-yield and low-emission rice varieties can be quickly selected, and the method is suitable for large-scale popularization and application. The rice field greenhouse gas emission is reduced, and application and popularization are easy.

Description

Technical Field

[0001] The present application relates to the field of rice breeding, and in particular to a method for screening high-yield and low-methane-emission rice. Background Art

[0002] Among all food crops, rice emits the highest greenhouse gas emissions per unit of yield, more than three times that of wheat and corn. Methane emissions are the primary source of greenhouse gas emissions from rice paddies, accounting for approximately 90%. The amount of methane emissions is primarily determined by the rice plant and paddy soil environment. Therefore, under equivalent cultivation conditions, selecting high-yield, low-methane-emitting rice varieties can significantly reduce agricultural greenhouse gas emissions.

[0003] Current research confirms that under identical cultivation conditions, methane emissions vary among different rice varieties. Rice plants can influence methane emissions by affecting three processes: methane production, oxidation, and transport. First, rice plants provide substrate for methanogens in the form of root exudates and litter. Second, rice plants secrete oxygen into the rhizosphere through their aerenchyma, creating an aerobic environment for methanotrophs. Furthermore, rice plants also serve as a major channel for methane transport. Numerous studies have shown that 60–90% of methane produced in rice soil is released into the atmosphere through the plant's aerenchyma. Therefore, studying the responses of relevant rice plant traits to methane emissions will help achieve a synergistic approach to increasing rice yields and reducing methane emissions from rice fields.

[0004] Furthermore, screening for high-yield, low-methane-emission varieties requires monitoring methane emissions throughout the entire growth period, which is time-consuming and labor-intensive. Therefore, developing a simple and rapid screening technology to improve breeding efficiency has become a pressing technical challenge in this field. Summary of the Invention

[0005] To address the above problems, the present invention provides a method for screening high-yield, low-methane rice using morphological and physiological traits. This method can reduce labor intensity, shorten screening time, and accelerate the breeding process of high-yield, low-methane-emission rice.

[0006] Specifically, the above invention object is achieved through the following solutions:

[0007] A method for screening high-yield and low-methane-emission rice varieties, comprising the following specific steps:

[0008] (1) Rice cultivation

[0009] Each variety is planted in 3 plots, with 200 plants planted in each plot, and the planting management is in accordance with local large-scale production requirements.

[0010] (2) Root diameter detection

[0011] Sampling was done at the end of tillering stage, and soil blocks with roots were dug out with the plant as the center to ensure that the root system was not damaged and the sampling was complete; 3 plants were taken from each plot, and the soil was washed clean with a water gun, taking care not to damage the root system. 50 roots were cut from each plant along the base, and the root diameter (unit: mm) was counted, and the average value of the 3 plots was taken.

[0012] (3) Root exudate detection

[0013] Sampling was done at the end of tillering, and soil blocks with roots were dug out with the plant as the center to ensure that the root system was not damaged and the sampling was complete; 3 plants were taken from each plot, and the roots were placed in deionized water in the dark after washing. The plants were cultured under 3000LEX light for 4 hours, and the root secretions were filtered and collected, and freeze-dried into powder. The malic acid and succinic acid contents were quantitatively determined by HPLC (the units of content were μg / gDW / h). The samples were dissolved and filtered in deionized water before being put into the machine, and the average value of the 3 plots was taken.

[0014] (4) The proportion of root aerenchyma to root cross-sectional area

[0015] Sampling was performed during the rice booting stage. Rooted soil was excavated from the center of the plant, ensuring that the root system was intact and sampled completely. Three plants were sampled from each plot. The soil was cleaned with a water gun, taking care not to damage the root system. Three mature roots with intact root tips were selected from each plant. Cross-sections were taken 2 cm from the root tip. The area of ​​the aerenchyma and the cross-sectional area of ​​the root system were calculated. The ratio of aerenchyma to cross-sectional area was then calculated and the average value of the three plots was taken.

[0016] (5) Intersegmental medullary cavity diameter detection

[0017] At the heading stage, plants with uniform growth were selected, and 3 plants were taken from each plot. The main stem was selected for measurement. The medullary cavity diameter (unit: mm) of the first, second and third internodes of each main stem was measured. The diameter was measured with a vernier caliper 2 cm above each node, and the average value of the 3 plots was taken.

[0018] (6) Calculation of methane emission index

[0019] The measured root diameter (x1), the medullary cavity diameters of the first, second, and third internodes of the main stem (x2, x3, and x4), the malic acid (x5) and succinic acid content (x6) of the root exudates, and the proportion of the aerenchyma to the cross-sectional area (x7) were substituted into the following formula (6) to calculate the methane emission index (Y).

[0020] Y=30.748-25.436x1-1.152x2-1.58x3-0.779x4+7.385x5+51.953x6-2.579x7 (6);

[0021] (7)Judgment

[0022] According to the definition and determination results of high-yield and low-methane-emission rice, the rice variety with a methane emission index Y less than 15 kg / t is determined to have a lower methane emission amount than other rice varieties during the whole growth period, and is a low-methane-emission rice variety.

[0023] Further, the rice variety is preferably a japonica rice variety in the middle and lower reaches of the Yangtze River (such as any one of Yongyou 1540, Nangeng 9108, Nangeng 5718, Nangeng 46, Changnongj 8, and Nangeng 8911).

[0024] Further, the determination of step (7) further includes: selecting a rice variety with Y less than 15 kg / t and a root system oxidation force greater than 50 ug / g / h at the booting stage, which has a lower methane emission amount than other rice varieties during the growth period, and is a low-methane-emission rice variety; the root system oxidation force at the booting stage refers to selecting plants with consistent growth for root system oxidation force determination, digging root soil blocks with plants as the center to ensure that the root system is not damaged and the sample is complete; taking 3 plants from each plot, washing the root system, and then cutting the root system of the same weight to determine the root system oxidation force (naphthalene amine oxidation intensity, unit: ug / g / h) by the alpha-naphthalene amine method, and taking the average of 3 plots.

[0025] Further, the determination of step (7) further includes: selecting a rice variety with a yield per mu of more than 650 kg (a conventional high-yield rice identification mark), which is a target high-yield and low-methane-emission rice.

[0026] The present application uses the detection of methane emission amount and related morphological and physiological indicators for many years, finds the trait parameters that are significantly correlated with methane emission for two consecutive years, and then uses the principal components of these parameters as independent variables and the methane emission amount per unit yield as dependent variables to perform regression equation fitting, obtains the regression equation, and then estimates the methane emission amount by measuring the specific indicator value. At the same time, the numerical threshold of other indicators of low-methane-emission varieties is used to establish a standard for evaluating high-yield and low-methane-emission rice.

[0027] In the embodiments of the present application, 6 rice varieties widely promoted in the middle and lower reaches of the Yangtze River are selected, and their morphologies and types are quite different, which are Yongyou 1540, Changnongj 8, Nangeng 9108, Nangeng 5718, Nangeng 46, and Nangeng 8911. The yield per mu of each variety is more than 650 kg. The methane emission flux and total amount of each variety during the whole growth period are monitored, and the trait parameters of each variety, such as root system diameter, root system aeration organization area, root system secretion content, root system oxidation force, and internode pith cavity diameter, are measured at the peak period of methane emission, i.e., the late tillering stage, the booting stage, and the full heading stage.

[0028] Through years of repeated experiments, this application is the first to establish a correlation between methane emissions and key phenotypic traits, screen key indicators, and establish a regression model, thereby obtaining high-yield, low-methane-emission rice varieties by observing phenotypic traits and simple and easy-to-operate detection indicators. Compared with the traditional method of measuring methane emissions throughout the entire growth period, it can not only reduce labor intensity, but also quickly select high-yield, low-emission rice varieties, reduce greenhouse gas emissions from rice fields, and is easy to promote and apply. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The statistical results of methane emission flux and unit production of different varieties in Example 1 are as follows;

[0030] Among them, A and B are the statistical results of methane emission flux and methane emissions per unit output in 2023, respectively; C and D are the statistical results of methane emission flux and methane emissions per unit output in 2024, respectively.

[0031] Figure 2 These are the statistical results of root diameters of different varieties in Example 2.

[0032] Figure 3 The statistical results of the cross-sectional area ratio of root aerenchyma of different varieties are shown in Figure 2.

[0033] Figure 4 The statistical results of the intersegmental medullary cavity diameters of different varieties;

[0034] Among them, AC is the statistical results of the medullary cavity diameters of the first, second, and third internodes of the main stem.

[0035] Figure 5 is the content of root exudates of different varieties;

[0036] Among them, A and B are the test results of malic acid and succinic acid content respectively.

[0037] Figure 6 The following are the test results of root oxidative capacity of different varieties;

[0038] Among them, A and B are the test results of naphthylamine oxidation intensity in 2023 and 2024 respectively.

[0039] Figure 7 The statistical results of the correlation analysis between various indicators and methane emissions.

[0040] Among them, A and B are the analysis results of 2023 and 2024, respectively, including total CH4 methane emissions; Fmax maximum methane emission flux; CH4 / P unit production methane emissions; RAD root diameter; RV root volume; FI-MCD, SI-MCD, TI-MCD first, second, and third internode medullary cavity diameters; MA malic acid; SA succinic acid; AR root cross-sectional aeration tissue area ratio. DETAILED DESCRIPTION

[0041] The rice varieties involved in the examples are all conventional commercial varieties provided by Jiangsu Academy of Agricultural Sciences.

[0042] Example 1: Different types of rice varieties were selected to monitor and collect statistics on their methane emissions and yield characteristics throughout their growth period.

[0043] The experiments involved in this embodiment were conducted in 2023 and 2024 at the Nanjing Comprehensive Experimental Station of the National Rice Industry System of the Jiangsu Academy of Agricultural Sciences in Nanjing, Jiangsu Province. The varieties selected were Yongyou 1540, Nanjing 5718, Nanjing 9108, Changnongjing No. 8, Nanjing 46, and Nanjing 8911, which are widely promoted for rice production in the middle and lower reaches of the Yangtze River.

[0044] Each variety was planted in one plot with three replicates, randomly arranged, and each plot had an area of ​​12.25 m 2 Plant spacing was 20 cm x 25 cm. Seedlings were sown on May 9th, raised in paddy fields, and transplanted on June 12th, with one seedling planted per hole. Base fertilizer was 40 kg / mu of compound fertilizer (N-16%, P2O5-8%, K2O-6%). Tillering fertilizer was applied twice: 7.5 kg / mu of urea on June 19th and 5 kg / mu on June 27th. Head fertilizer was applied twice: 15 kg / mu of special compound fertilizer (N-16%, P2O5-0%, K2O-16%) on July 25th (for early-maturing varieties Nanjing 9108, Nanjing 5718, and Changnongjing 8) and on August 5th (for late-maturing varieties Yongyou 1540, Nanjing 46, and Nanjing 8911). Field methane emissions were measured starting 12 days after transplanting, every three days in the water-bearing layer and every seven days in the dry layer during the baking and maturity stages. Field methane was collected using a static chamber method, and methane concentrations were analyzed using a 7890A (GC, Agilent) gas chromatograph.

[0045] At maturity, five plants were harvested from each plot, with 15 plants from each variety analyzed for yield component analysis. Yield was measured across the entire plot, with three plots from each variety, and the moisture content was calibrated to the standard moisture content of 14.5%. The test results are shown in Table 1 below:

[0046] Table 1 Yield of different varieties and their component factors

[0047]

[0048] The analysis found that the maximum effective ear of Changnongjian 8, no significant difference in effective ear of Nanjing 5718, Nanjing 46, Nanjing 8911, Yongyou 1540 (Table 1). The most grains per ear of Yongyou 1540, followed by Nanjing 8911, Changnongjian 8, the least grains per ear, but the highest seed setting rate was 96.43%. The lowest seed setting rate of Yongyou 1540 was 83.25%.

[0049] In terms of thousand-grain weight, the maximum thousand-grain weight of Nanjing 5718 was 31.71g, the minimum thousand-grain weight of Yongyou 1540 was 22.26g, and the thousand-grain weight was in the order of Nanjing 5718 > Nanjing 46 > Changnongjian 8 > Nanjing 9108 > Nanjing 8911 > Yongyou 1540.

[0050] There were significant differences in single plant yield among different varieties. The maximum single plant yield of Yongyou 1540 was 54.22g, the minimum single plant yield of Nanjing 9108 was 40.05g, and the single plant yield was in the order of Yongyou 1540 > Nanjing 5718 > Changnongjian 8 > Nanjing 8911 > Nanjing 46 > Nanjing 9108.

[0051] The yield per mu of Yongyou 1540 was the largest, reaching 974.6Kg, and the yield per mu of Nanjing 9108 was the smallest, reaching 697.0Kg. The yield per mu was in the order of Yongyou 1540 > Nanjing 46 > Changnongjian 8 > Nanjing 8911 > Nanjing 5718 > Nanjing 9108. But the yield per mu of the six varieties was more than 650kg, which belonged to high yield varieties.

[0052] In order to ensure the accuracy of the experiment, different rice varieties were monitored for methane emission during the whole growth period in 2023 and 2024. The methane emission flux of different varieties increased first and then decreased during the whole growth period, showing a single peak trend, and there were significant differences among varieties. The maximum emission flux of Yongyou 1540, Nanjing 5718, Nanjing 9108 and Nanjing 46 appeared at 45d after transplanting, and the maximum emission flux of Changnongjian 8 and Nanjing 8911 appeared at 55d after transplanting (as shown in Figure 1 The maximum emission flux of Yongyou 1540 was 23.12mg.m -2 .h -1 , the maximum emission flux of Nanjing 9108 was 32.12mg.m -2 .h -1 , the maximum emission flux of Nanjing 5718 was 34.71mg.m -2 .h -1 , the maximum emission flux of Nanjing 46 was 29.37mg.m -2 .h -1The maximum emission flux of Changnongjing No. 8 is 35.48 mg.m -2 .h -1 The maximum emission flux of Nanjing 8911 is 39.91 mg.m -2 .h -1 .

[0053] The total emission during the entire growth period was calculated using the emission flux of each variety, and then the methane emission per unit yield of each variety was obtained. The statistical results are as follows: Figure 1 shown.

[0054] Depend on Figure 1 Statistics show that Changnongjing No. 8 rice had the highest methane emissions per unit of production in both 2023 and 2024, reaching 18.45 kg / t and 21.68 kg / t, respectively. Yongyou 1540 had the lowest methane emissions per unit of production in both years, at 6.27 kg / t and 6.73 kg / t, respectively. Emissions per unit of production for other varieties fell between these two levels.

[0055] By comprehensively analyzing the yield and its component factors, as well as the total emissions and maximum emission flux during the entire growth period, rice varieties with unit yield methane emissions 40% lower than that of Changnongjing No. 8 were identified as high-yield, low-methane-emitting rice varieties. Yongyou 1540, Nanjing 5718, Nanjing 46, and Nanjing 9108 were preliminarily determined to be high-yield, low-methane-emitting rice varieties.

[0056] Through the above-mentioned monitoring of methane emissions throughout the entire growth period, it was found that each variety had an emission peak at the end of tillering and the stage from booting to full heading, and the trend of the emission peak was consistent with the trend of the total emission. Therefore, the level of the maximum emission flux can indirectly reflect the level of the total emission. The determination of later morphological and physiological indicators mainly selected the end of tillering, the stage from booting, and the stage from full heading.

[0057] Example 2 Screening of Detection Indicators for High-Yield and Low-Methane-Emission Rice

[0058] In this example, key morphological and physiological indicators of different rice varieties (including root diameter at peak tillering, root aerenchyma area, root exudate content, root oxidative capacity, and stem medullary cavity diameter at full heading) were measured, and the correlation between each indicator and methane emissions and yield was analyzed to obtain preliminary screening indicators.

[0059] The relevant morphological and physiological indicators were sampled and measured during the same growth period in 2023 and 2024, and the field water and fertilizer management was the same in both years.

[0060] 1. Root morphology

[0061] At the end of rice tillering, a 20 × 20 × 20 cm root-bearing soil block was dug around the plant. Three plants per plot were collected for each variety, and 50 roots were cut from each plant to calculate the root diameter. In this example, root diameters were scanned using a root scanning system (WinRHIZO Root Analysis System), and the average value of the three plots was used for interspecific comparison.

[0062] From the results of 2023-2024, we can see that (such as Figure 2 The root diameter of the variety with the largest root diameter was Yongyou 1540, and the smallest was Changnongjing 8. The root diameters of Nanjing 9108, Nanjing 5718, Nanjing 46, and Nanjing 8911 were between the two varieties, with no significant difference between the varieties. The root diameters of Nanjing 5718, Nanjing 46, and Nanjing 8911 were significantly smaller than that of Yongyou 1540. The results showed a consistent trend over the two years and good repeatability.

[0063] 2. Root aerenchyma

[0064] At the booting stage, plants with uniform growth were selected, with three plants per plot and nine plants per variety. Roots with intact root tips were selected, and cross-sections were taken 2 cm from the root tips. Microscopic photographs were taken, and the averages of the aerenchyma area and root cross-sectional area were calculated. In this example, image analysis software (Image-Pro Plus 6.0) was used to calculate the aerenchyma area and root cross-sectional area, and the averages were calculated for the three plots.

[0065] Slice statistics results show (such as Figure 3 As shown in the figure, Yongyou 1540 has the largest proportion of aerenchyma area, while Changnongjing 8 has the smallest proportion, indicating some differences between the two results. In 2024, there was no significant difference in the proportion of aerenchyma in the roots of Nanjing 46, Nanjing 5718, and Nanjing 8911. The proportion of aerenchyma in the roots of Nanjing 46 and Nanjing 8911 was significantly higher than that of Changnongjing 8. There was no significant difference in the proportion of aerenchyma between Changnongjing 8, Nanjing 9108, and Nanjing 5718. In 2023, there were no significant differences among the varieties. Yongyou 1540 was significantly higher than Nanjing 8911, but there were no significant differences between the other varieties.

[0066] 3. Comparison of Pith Diameters of Different Rice Varieties

[0067] At the heading stage, 9 plants of each variety (3 plants in each plot) with uniform growth were selected, and the main stem was selected for measurement. The medullary cavity diameters of the first, second and third internodes of each main stem were measured, and the average value of the three plots was taken for interspecific comparison.

[0068] The size of the internode medullary cavity is not only a channel for methane emission but also a channel for oxygen to enter the plant. Figure 4As shown in the figure, Yongyou 1540 has the largest medullary cavity diameter, while Changnongjing 8 has the smallest, showing a consistent trend over the past two years. The 2024 results showed significant variability among varieties. There was no significant difference in the medullary cavity diameter of the first intersegment between Yongyou 1540, Nanjing 9108, Nanjing 5718, and Nanjing 46. However, Yongyou 1540 had the largest medullary cavity diameter, while Changnongjing 8 had the smallest. Nanjing 8911 had a larger medullary cavity diameter than Changnongjing 8 but smaller than Yongyou 1540, Nanjing 9108, and Nanjing 46. The medullary cavity diameters of the second and third intersegments showed similar trends, with Yongyou 1540 having the largest diameter and Changnongjing 8 having the smallest. The medullary cavity diameters of the other varieties ranged between the two.

[0069] 4. Root exudates of different rice varieties

[0070] At the end of tillering, 9 plants of each variety (3 plants per plot) were sampled, and 20×20×20 cm rooted soil blocks were dug. After washing, the roots were placed in dark deionized water. The plants were incubated under 3000LEX light for 6 h. The root exudates were filtered and collected, and freeze-dried into powder. The root exudates were quantitatively determined by conventional HPLC (in this example, the method disclosed in the document "Peng Xue. Effects and mechanisms of exogenous silicon regulating rice root secretion of organic acids to alleviate arsenic stress [D], Shenyang Agricultural University. 2024" was used). The solution was dissolved in deionized water and filtered before loading, and the average values ​​of the three plots for each variety were compared.

[0071] From the test results of 2023-2024, it can be seen that the malic acid and succinic acid contents of Yongyou 1540 are the lowest (e.g. Figure 5 As shown in the table, Changnongjing 8 had the highest malic acid and succinic acid contents. Nanjing 5718 had a malic acid content between the two, but higher than other Nanjing varieties. Nanjing 5718 also had a higher succinic acid content than other Nanjing varieties in 2023, and Nanjing 9108 had a higher succinic acid content than other Nanjing varieties in 2024.

[0072] 5. Root Oxidative Capacity of Different Rice Varieties

[0073] Root oxidative capacity, to a certain extent, reflects the oxidative capacity of plant roots in low-oxygen environments. The stronger the plant's oxidative capacity, the more favorable its rhizosphere environment is for reducing methane emissions. Root oxidative capacity was measured at the booting stage using plants of uniform growth. Three plants were selected from each plot. After washing their roots, roots of equal weight were cut and measured using the α-naphthylamine method.

[0074] From the results of the past two years, we can see that ( Figure 6 As shown in Figure 2, Yongyou 1540 had the highest root oxidative capacity, Changnongjing 8 had the lowest, and the root oxidative capacity of the other varieties was between the two. Among them, Nanjing 8911 had the lowest root oxidative capacity of the other Nanjing varieties.

[0075] 6. Relationship between each index and methane emission

[0076] The 2-year measured values of the above traits were respectively correlated with the total amount of methane emission, the highest methane emission flux, and the methane emission per unit yield, and thus the preliminary screening traits were obtained.

[0077] Through the determination and correlation analysis of the above indexes, it was found (as shown in Figure 7 The parameters significantly correlated with the total annual emission, the highest emission flux, and the emission per unit yield in 2023 and 2024 for two consecutive years were root diameter, the proportion of root aerenchyma in cross-sectional area, first internode pith diameter, second internode pith diameter, third internode pith diameter, and malic acid and succinic acid contents in root exudates. Among them, root diameter, the proportion of root aerenchyma in cross-sectional area, first internode pith diameter, second internode pith diameter, third internode pith diameter were significantly negatively correlated with methane emission, and malic acid and succinic acid contents in root exudates were significantly positively correlated with methane emission. Therefore, through the above test results, these parameters were selected as important traits for screening.

[0078] Construction of regression model in Example 3

[0079] 1. Establishment of regression model

[0080] Through the data analysis of 2023-2024 in Example 2, a total of 7 traits were significantly correlated with methane emission, and the correlation of the data in 2024 was higher than that in 2023. Therefore, the data in 2024 were selected for model establishment in this example.

[0081] Since the number of samples is only 6, principal component analysis is first selected for the 7 traits to reduce the number of independent variables. According to the principal component extraction results, the contribution rate of 95% is selected as the principal component selection standard. As shown in Table 2, the contribution rates of the first three principal components, internode pith factor (PC1), root exudate factor (PC2), and root aerenchyma factor (PC3), were 82.971%, 9.513%, and 4.347%, respectively, and the cumulative variance contribution rate was 96.83%, which indicated that the principal components 1, 2, and 3 could represent 96.83% of the parameter information of the selected significantly correlated traits.

[0082] Table 2 Principal component analysis results

[0083]

[0084] According to the selected principal components, the principal component coefficients of the 7 traits were analyzed by SPSS software, and thus the scores of PC1, PC2, and PC3 were calculated. The specific calculation formula is as follows:

[0085] Principal component score:

[0086]

[0087] PC1, PC2, PC3 are principal component scores, ai is the component score coefficient corresponding to the principal component, and xi is the standardized value of 7 traits.

[0088] xi = (value of each trait - average value of each variety of the trait) / standard deviation of the value of each variety of the trait (4)

[0089] Table 3 Component coefficients of 3 principal components

[0090]

[0091]

[0092] The values of PC1, PC2, and PC3 were obtained, and the methane emission per unit yield was taken as the dependent variable (Y), the principal component 1 score PC1, the principal component 2 score PC2, and the principal component 2 score PC3 were taken as the independent variables, and a linear regression analysis was performed to obtain the regression equation:

[0093] Y = 12.745 - 4.702PC1 + 0.271PC2 + 0.956PC3, with a determination coefficient R 2 = 0.976 (5).

[0094] 2. Verification of the regression equation and final determination of the screening method

[0095] (1) Verification of the regression equation

[0096] The scores of the principal components of the 7 trait indicators in 2024 and 2023 were calculated using the linear regression equation, and the values of the 3 principal components PC1, PC2, and PC3 were obtained. The emission per unit yield was calculated by inputting the values into the regression equation, and the error range between the simulated value and the measured value was checked to verify the accuracy of the regression equation.

[0097] Table 4 Simulated and measured values in 2024

[0098]

[0099] The simulated value of the methane emission per unit yield in 2024 calculated by the linear regression equation was within 2.168% - 14.905% of the actual methane emission during the entire growth period, indicating that the linear regression equation obtained by this method had high accuracy.

[0100] A principal component analysis was also performed on the values ​​of the seven traits for 2023, and methane emissions per unit of yield were calculated using a regression equation. The error between the measured and simulated values ​​was determined. The maximum error was 36.319%, which is larger than the error for 2024. However, the predicted methane emissions per unit of yield was 12.501, which is relatively low. The predicted methane emissions per unit of yield for the high-emission rice variety Changnongjing No. 8 were 20.505, which is consistent with the actual measured trend. This demonstrates that the method of using regression equations to predict methane emissions per unit of yield is reliable.

[0101] Table 5 Simulated and measured values ​​in 2023

[0102]

[0103] For ease of application, the three principal component coefficients are substituted into the regression equation (5): Y = 12.745-4.702PC1+0.271PC2+0.956PC3, and the regression equation with the seven trait values ​​as variables is obtained:

[0104] Y=30.748-25.436x1-1.152x2-1.58x3-0.779x4+7.385x5+51.953x6-2.579x7 (6);

[0105] x1, x2, x3, ..., x7 are the root diameter at the end of tillering, the medullary cavity diameters of the first internode, the second internode, and the third internode at the heading stage, the malic acid and succinic acid contents in root exudates at the end of tillering, and the ratio of root aerenchyma to cross-sectional area at the booting stage. In formula (6), all diameters are in mm.

[0106] (2) Finalization of screening method

[0107] In regression equation (4), the three principal component scores of seven traits were used as independent variables. These traits were significantly correlated with methane emissions in the two-year repeated experiment. However, root activity, the main factor affecting methane emissions, did not form a significant correlation with methane emissions. Varieties with higher root activity generally had lower methane emissions. Varieties with Y values ​​less than 15 calculated by the regression equation included Yongyou 1540, Nanjing 46, Nanjing 5718, Nanjing 9108, and Nanjing 8911. Varieties with methane emissions per unit yield lower than 40% of Changnongjing 8 were selected through a two-year full growth period screening. Therefore, root activity during the booting stage was added as a screening indicator. According to the two-year measurement results, the root activity of Changnongjing 8 and Nanjing 8911 was less than 50 μg / g / h.

[0108] Therefore, the screening method for high-yield and low-methane-emission rice is to measure the root diameter and the emission rate of succinic acid and malic acid in root secretions at the end of tillering; measure the root aerenchyma area and root oxidative capacity at the booting stage; measure the medullary cavity diameters of the first, second, and third internodes at the full heading stage; and calculate the Y value using the regression equation Y=30.748-25.436x1-1.152x2-1.58x3-0.779x4+7.385x5+51.953x6-2.579x7. The Y value is required to be less than 15. At the same time, varieties with an per-acre yield of more than 650 kilograms and a root oxidative capacity greater than 50ug / g / h at the booting stage are considered high-yield and low-methane-emission rice varieties.

Claims

1. A method for screening high-yield and low-methane-emission rice varieties, characterized in that: The specific steps are as follows: (1) Root diameter detection At the end of rice tillering, the root system was collected and the root diameter was recorded as x1; (2) Root exudate detection At the end of rice tillering, roots were collected to determine the malic acid and succinic acid contents in the roots, which were recorded as x5 and x6, respectively; (3) Calculate the ratio of root aerenchyma to root cross-sectional area At the rice booting stage, the root cross section was taken, the area of ​​the aerenchyma and the cross-sectional area of ​​the root were counted, and the ratio of the aerenchyma to the cross-sectional area was calculated, which was recorded as x7; (4) Intersegmental medullary cavity diameter detection At the rice heading stage, the main stem was selected to measure the diameters of the medullary cavity of the first, second, and third internodes, which were recorded as x2, x3, and x4 respectively. (5) Calculation and determination of methane emission index Y=30.748-25.436x1-1.152x2-1.58x3-0.779x4+7.385x5+51.953x6-2.579x7 (6); In formula (6), Y is the methane emission index; rice varieties with Y < 15 kg / t are judged to have lower methane emissions than other rice varieties.

2. The screening method according to claim 1, wherein The cross section in step (3) refers to the cross section obtained by cutting 2 cm away from the root apex.

3. The screening method according to claim 1, wherein The values ​​of x1-x7 are all average values ​​obtained by repeated testing three times.

4. The screening method according to claim 1, wherein Step (5) also includes measuring the naphthylamine oxidation intensity of rice roots during the booting stage, and determining that rice varieties with Y < 15 kg / t and naphthylamine oxidation intensity greater than 50 ug / g / h have lower methane emissions than other rice varieties.

5. The screening method according to claim 2, characterized in that The naphthylamine oxidation strength is determined by the α-naphthylamine method.

6. The screening method according to any one of claims 1 to 5, characterized in that Step (5) also includes determining that rice with an acreage yield greater than 650 kg is a high-yield, low-methane-emission rice variety.

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