High-yield cold-resistant Chinese pennisetum germplasm screening method and application thereof

Abstract

The invention discloses a high-yield cold-resistant pennisetum germplasm screening method and application thereof, and the method is based on 10 yield traits, 4 cold-resistant indexes and 4 low-temperature photosynthetic ability indexes of pennisetum, constructs a multi-dimensional dynamic monitoring system across autumn, winter and spring, and performs high-yield cold-resistant comprehensive evaluation on the pennisetum germplasm, thereby screening out a high-yield cold-resistant breeding material. The invention provides a comprehensive, efficient and accurate high-yield cold-resistant Chinese pennisetum germplasm screening method, through a multi-dimensional dynamic monitoring system crossing autumn, winter and spring, yield traits, cold resistance indexes and low-temperature photosynthetic ability indexes are integrated, and the problems that a traditional method is single in evaluation dimension, disjointed with field reality and insufficient in dynamic adaptability are solved. The comprehensive evaluation system based on the principal component quantitative model not only fills the blank of the high-yield and cold-resistant evaluation model of the pennisetum alopecuroides, but also remarkably improves the breeding efficiency, provides powerful support for cultivating cold-resistant and yield-reduction-free breakthrough varieties, and has important application value and popularization prospect.

Description

Technical Field

[0001] The invention belongs to the technical field of agricultural breeding, and relates to a method for screening high-yield and cold-resistant Pennisetum germplasm, and application of the method in Pennisetum breeding. Background Art

[0002] Pennisetum Pennisetum purpureum Pennisetum spp. is a C4 plant native to Africa with high economic value and broad application prospects. It not only provides sufficient, high-quality feed for ruminant livestock, but also, due to its rapid growth, high yield, and strong tolerance to biotic and abiotic stresses, it is an excellent material for producing biofuels, biochar, ethanol, methane, and paper. In my country, the main varieties cultivated and utilized include American Pennisetum, elephant grass, and hybrid Pennisetum.

[0003] However, low temperatures are the most serious abiotic stress factor hindering the promotion and utilization of Pennisetum. Pennisetum is extremely sensitive to temperature conditions, with optimal daytime growth temperatures between 30 and 35°C. Temperatures below 10°C significantly inhibit its growth. In natural environments, if the latitude of the planting site is high, resulting in a yearly minimum temperature below the plant's tolerance, the plant will struggle to survive and achieve sufficient yields. For example, in Guiyang, China, temperatures dropping below -4°C cause all underground rhizomes of Pennisetum to freeze to death, severely limiting its promotion and application in high-latitude regions. Research data indicates that Pennisetum can produce up to 80 tons of dry matter per hectare per year in tropical regions. However, at 30 degrees north latitude, this yield drops to 45 tons per hectare per year. At 36 degrees north latitude, although some varieties can still survive the winter, dry matter yield drops significantly to 30 tons per hectare per year or even lower.

[0004] Currently, traditional identification and evaluation methods for Pennisetum germplasm screening face numerous bottlenecks. Firstly, evaluation dimensions are relatively limited, with most studies focusing on a single indicator (such as survival rate or electrical conductivity) and a single environment (e.g., a controlled incubator). These studies lack a systematic integration of Pennisetum's growth traits, physiology, and biochemistry, and fail to conduct collaborative multi-environmental investigations and evaluations. Secondly, these traditional methods fail to fully incorporate the dynamic changes in natural low-temperature stress in the field, making it difficult to accurately assess Pennisetum's long-term adaptability, such as its regreening ability and photosynthetic response to low temperatures. Furthermore, the lack of standardized technical standards is a significant issue. The lack of standardized measurement procedures, such as for measuring stem sugar content and standardized sampling for tiller fresh weight, results in poor comparability of the resulting data, hindering effective support for Pennisetum variety selection and regional promotion. More critically, the industry currently lacks a scientifically validated model for evaluating high-yield and cold-tolerance performance in Pennisetum, significantly hindering the progress and efficiency of Pennisetum breeding. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a new method for screening high-yield and cold-resistant Pennisetum germplasm, aiming to comprehensively overcome the existing technical difficulties by constructing a multi-dimensional dynamic monitoring system, clarifying standardized operating specifications, and establishing a multi-module integrated principal component quantitative evaluation model, so as to provide solid and reliable technical support for the efficient breeding and wide application of Pennisetum.

[0006] Through long-term exploration and attempts, as well as multiple experiments and efforts, the inventors have continuously reformed and innovated to solve the above technical problems. The technical solution provided by the present invention is to provide a method for screening high-yield and cold-resistant Pennisetum germplasm. The method is based on 10 yield traits, 4 cold resistance indicators and 4 low-temperature photosynthetic capacity indicators of Pennisetum, and constructs a multi-dimensional dynamic monitoring system across autumn, winter and spring to conduct a comprehensive evaluation of the high yield and cold resistance of Pennisetum germplasm, thereby screening out high-yield and cold-resistant breeding materials.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a comprehensive, efficient and accurate method for screening high-yield and cold-resistant Pennisetum germplasm. Through a multi-dimensional dynamic monitoring system spanning the autumn, winter and spring seasons, the yield traits, cold resistance indicators and low-temperature photosynthetic capacity indicators are integrated, which overcomes the problems of traditional methods such as single evaluation dimension, disconnection with field reality and insufficient dynamic adaptability. At the same time, standardized operating specifications effectively reduce environmental errors and improve data comparability. In addition, the comprehensive evaluation system based on the principal component quantification model not only fills the gap in the high-yield and cold-resistant evaluation model of Pennisetum, but also significantly improves breeding efficiency, provides strong support for the cultivation of breakthrough varieties that are cold-resistant and do not reduce yield, and has important application value and promotion prospects.

[0008] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore: the 10 yield traits include plant height, number of stem nodes, internode length, stem diameter, leaf length, leaf width, number of leaves, fresh weight of single tiller, dry-to-fresh ratio and chlorophyll content.

[0009] Preferably: The plant height is measured by measuring the height from the base of the plant to the naturally extended height of the highest leaf; The determination of the number of stem nodes is to count the number of visible nodes of the main stem, excluding the hidden nodes at the base; The internode length is determined by measuring the length of the 3rd to 4th internode at the base; The stem diameter is measured by measuring the width of the thick surface of the 3rd to 4th nodes of the stem at the base; The leaf length is measured by selecting a complete and healthy leaf in the middle of the plant and measuring the length; The leaf width is measured by selecting a complete and healthy leaf in the middle of the plant to measure the width; The determination of the leaf number is to count the number of leaves from the base of the leaf sheath to the latest expanded leaf on the stem; The fresh weight of a single tiller is determined as follows: the number of tillers in each clump of Pennisetum is counted, and the total fresh weight of tillers is weighed after mowing, where the fresh weight of a single tiller = the total fresh weight of tillers / the number of tillers; The dry-to-fresh ratio is determined as follows: 2 to 3 Pennisetum stalks are cut into pieces and weighed for fresh weight, and then dried and weighed for dry weight. The dry-to-fresh ratio = dry weight / fresh weight × 100%; The chlorophyll content is determined by using a chlorophyll content meter to measure the flat leaf surface in the middle of the leaf.

[0010] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The system comprehensively covers the key indicators of Pennisetum growth, development and yield formation, making the screening process more scientific and accurate, and able to more accurately evaluate the yield potential of Pennisetum, thereby effectively improving the screening efficiency and reliability of high-yield and cold-resistant germplasm.

[0011] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore: the four cold resistance indicators include semi-lethal temperature, malondialdehyde change value, conductivity change value and stem sugar content change value.

[0012] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: By integrating four key cold-resistance indicators, we can more comprehensively and accurately evaluate the physiological response and cold-resistance of Pennisetum in low-temperature environments, effectively improving the reliability and accuracy of the screening results.

[0013] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore: the four low-temperature photosynthetic capacity indicators include changes in chlorophyll content under low-temperature stress, changes in the percentage of green leaves, the number of root-cut seedlings that regreen, and changes in chlorophyll content when regreened.

[0014] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: By monitoring four key low-temperature photosynthetic capacity indicators, the photosynthetic performance and recovery ability of Pennisetum under low-temperature stress can be accurately evaluated, effectively enhancing the comprehensiveness and reliability of the screening results.

[0015] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore: the construction method of the multi-dimensional dynamic monitoring system includes: (1) Yield trait determination: Growth and development traits of Pennisetum were determined during the initial flowering period. The determination indicators included plant height, number of stem nodes, internode length, stem diameter, leaf length, leaf width, number of leaves, fresh weight of single tiller, dry-to-fresh ratio, and chlorophyll content. Each indicator was measured with multiple replicates.

[0016] (2) Determination of cold resistance indexes: When the field temperature drops in autumn and winter, samples are taken when the daily minimum temperature drops to 10℃ and 0℃ respectively. The measured indexes include half-lethal temperature, malondialdehyde, electrical conductivity and stem sugar content. Each index is measured with multiple replicates.

[0017] (3) Determination of low-temperature photosynthetic capacity: During the process of decreasing field temperature in autumn, measurements were carried out when the daily minimum temperature dropped to 10℃ and 0℃, respectively. The measurement indicators included the chlorophyll content under low-temperature stress and the percentage of green leaves. Subsequently, the stubble was cut and left for wintering. When the minimum temperature rose to 10℃ and 15℃ in the following year, the number of root-cut seedlings that turned green were counted and the chlorophyll content of the turned green was measured. Each indicator was measured with multiple replicates.

[0018] Preferably, each indicator is measured in 6 replicates.

[0019] Preferably, the determination of the semi-lethal temperature comprises: taking healthy leaves, treating them at temperature gradients of 2, 0, -2, -4, -6, and -8°C for 2 hours respectively, measuring the electrolyte exudation rate, and calculating the semi-lethal temperature by a logistic regression equation; Preferably, the malondialdehyde change value is the malondialdehyde content measured at the lowest temperature of 0°C minus the malondialdehyde content measured at the lowest temperature of 10°C; The conductivity change value is the relative conductivity measured at the lowest temperature of 0°C minus the relative conductivity measured at the lowest temperature of 10°C; The change in the sugar content of the stems is the sugar content of the stems measured at the lowest temperature of 0°C minus the sugar content of the stems measured at the lowest temperature of 10°C.

[0020] The change of chlorophyll content under low temperature stress is the chlorophyll content measured at the lowest temperature of 0°C in autumn minus the chlorophyll content measured at the lowest temperature of 10°C in autumn; The change in green leaf percentage is the green leaf percentage measured at the autumn minimum temperature of 0°C minus the green leaf percentage measured at the autumn minimum temperature of 10°C; The change in chlorophyll content during greening is the chlorophyll content measured when the lowest temperature in spring is 15°C minus the chlorophyll content measured when the lowest temperature in spring is 10°C; The number of green seedlings with root cutting is the number of green seedlings counted when the minimum temperature in spring rises to 10℃.

[0021] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: This system can systematically and accurately monitor the performance of Pennisetum at key growth stages and under temperature stress, providing reliable data support for the screening of high-yield and cold-resistant germplasm.

[0022] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore: the screening method also includes using the principal component analysis method to perform a weight analysis on the measured yield traits, cold resistance indicators and low-temperature photosynthetic capacity; based on the weight analysis results, a membership function method is used to comprehensively evaluate the Pennisetum samples, and a comprehensive score D value is calculated, thereby achieving a comprehensive evaluation of the high yield and cold resistance of Pennisetum germplasm.

[0023] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The comprehensive evaluation system that combines principal component analysis and membership function method can scientifically quantify the weight of each indicator and integrate multi-dimensional data, effectively improving the accuracy and reliability of Pennisetum germplasm screening.

[0024] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore: the specific steps of the principal component analysis method and the membership function method are as follows: (1) Calculation of principal component scores: The principal component scores are calculated based on the component matrix values ​​of each trait in the principal component and the initial eigenvalues ​​of the principal component.

[0025] (2) Calculation of variance percentage weight: Calculate the variance percentage weight using the principal component scores.

[0026] (3) Model coefficient calculation: Calculate the model coefficient based on the variance percentage weight.

[0027] (4) Calculation of final trait weights: Normalize the model coefficients to obtain the final trait weights.

[0028] (5) Phenotypic data normalization: Normalize the original phenotypic data.

[0029] (6) Calculation of weighted membership function: Based on the normalized phenotypic data and the final trait weight, the weighted membership function value is calculated to obtain the comprehensive score D value.

[0030] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: By specifying the various steps of principal component analysis and membership function method in detail, the scientificity, systematicness and transparency of data processing and comprehensive evaluation are ensured, and the accuracy and credibility of the screening results are further improved.

[0031] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the screening method is suitable for screening Pennisetum germplasm under natural low temperature stress environment in the field, and can accurately reflect the growth and adaptation of Pennisetum under dynamic low temperature stress in natural fields.

[0032] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The adoption of the above-mentioned further technical solutions can make the screening process closely fit the actual production scenario, ensuring that the screened Pennisetum germplasm has excellent high-yield and cold-resistant performance in real field environments, thereby improving the reliability and adaptability of breeding materials in practical applications.

[0033] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the screening method can significantly save time and economic costs, and improve the efficiency of parent selection, offspring screening and variety stabilization in Pennisetum breeding.

[0034] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The method of the present invention effectively reduces the time and resource investment in the breeding process of Pennisetum by optimizing the screening process and improving data accuracy, while accelerating the breeding process and improving breeding efficiency.

[0035] The present invention also provides an application of a high-yield cold-resistant Pennisetum breeding material obtained by screening using the screening method in Pennisetum breeding. The breeding material is used to cultivate a breakthrough Pennisetum variety that is cold-resistant and does not reduce yield.

[0036] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides high-yield and cold-resistant Pennisetum breeding materials to help cultivate breakthrough varieties that are cold-resistant without reducing yields, effectively solving the problem of finding both cold resistance and high yield in existing Pennisetum breeding, and significantly improving the adaptability and yield of Pennisetum in low-temperature areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a correlation analysis diagram of 18 traits in an embodiment of the method for screening high-yield and cold-resistant Pennisetum germplasm of the present invention.

[0039] Figure 2 This is a cluster analysis result diagram of 19 test materials in an embodiment of the method for screening high-yield and cold-resistant Pennisetum germplasm of the present invention.

[0040] Figure 3 This is a statistical chart of the number of green seedlings that have returned after root cutting in an embodiment of the method for screening high-yield and cold-resistant Pennisetum germplasm of the present invention. DETAILED DESCRIPTION

[0041] The following describes the details in conjunction with specific embodiments.

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are 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 making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention.

[0043] In the present invention, unless otherwise specified, all equipment and raw materials can be purchased from the market or are commonly used in the industry. The methods in the following embodiments, unless otherwise specified, are all conventional methods in the art.

[0044] This example uses a multi-dimensional dynamic monitoring system based on 10 yield traits, four cold-resistance indicators, and four low-temperature photosynthetic capacity indicators for Pennisetum, spanning the autumn, winter, and spring seasons. This system allows for a comprehensive evaluation of high yield and cold-resistance for 19 Pennisetum accessions. This system allows for the rapid and efficient selection of one high-yield, cold-resistant breeding material and six low-yield, cold-resistant accessions from Pennisetum germplasm with high accuracy.

[0045] Yield trait determination (10): Growth and developmental traits of Pennisetum were measured at the initial flowering stage to directly reflect the growth status, morphological structure, biomass accumulation and physiological function of the plant. The indicators included plant height (PH), stem node number (SNN), internode length (IL), stem diameter (SD), leaf length (LL), leaf width (LW), leaf number (LN), single tiller fresh weight (STFW), dry to fresh weight ratio (DW / FW) and chlorophyll content (Chl). The measurements were repeated six times.

[0046] The number of stem nodes, etc. should count the number of visible nodes of the main stem, and exclude the hidden nodes at the base; the internode length is measured from the base, and the length of the 3rd-4th internode is measured with a tape measure, and the unit is cm; the stem diameter is measured from the base, and the width of the rough surface of the 3rd-4th stem is measured with a vernier caliper (the stem of Pennisetum is elliptical), and the unit is mm; for leaf length measurement, select a complete and healthy leaf in the middle of the Pennisetum plant, and measure the leaf length with a tape measure, and the unit is cm; for leaf width measurement, select a complete and healthy leaf in the middle of the Pennisetum plant, and measure the leaf width with a vernier caliper, and the unit is mm; the number of leaves is counted from the fully expanded leaves of the base sheath to the latest expanded leaves on the Pennisetum stem; the fresh weight of a single tiller is counted, and the number of tillers in each clump of Pennisetum is counted. Use a sickle to cut the second node from the base, and weigh the total tiller fresh weight with an electronic scale. The fresh weight of a single tiller = total tiller fresh weight / number of tillers.

[0047] ; STFW: single tiller fresh weight (g·tiller⁻¹); W i : fresh weight of the ith tiller (g); n: Total number of tillers (tillers).

[0048] The dry-to-fresh ratio was determined by mowing the grass at the second node from the base with a sickle. 2–3 Pennisetum stalks of uniform growth were chopped and placed in a kraft paper bag. The fresh weight (FW) was then weighed and recorded. The bag was then placed in an oven and dried at 105°C for 30 minutes. The bag was then dried at 80°C to a constant weight, and the dry weight (DW) was measured. The dry-to-fresh ratio = dry weight (DW) / fresh weight (FW) × 100%. A CL-01 chlorophyll meter (Hansatech) was used to determine the chlorophyll content. The measurement site was the middle of the leaf, with the leaf surface flattened to prevent veins from being pinched.

[0049] The results of the 10 yield traits are shown in Table 1.

[0050] Determination of cold resistance index (4): Before the temperature dropped, field samples were taken to determine the half-lethal temperature (LT50) of Pennisetum. As field temperatures dropped in autumn and winter, field sampling began when the minimum temperature dropped to 10°C. Plants were exposed to natural low-temperature stress in the field. Secondary sampling was performed when the minimum temperature dropped to 0°C to measure malondialdehyde (MDA), electrical conductivity (EL), and soluble sugar content (SSC). Six replicates were used.

[0051] For the determination of the half-lethal temperature, mature leaves with uniform growth, health and no disease were collected from the field. After rinsing with tap water, they were rinsed with deionized water 2 to 3 times, and then the residual moisture on the leaf surface was absorbed on clean filter paper. A circular piece sample was punched out from the middle of the Pennisetum leaf with a hole punch, taking care not to take the leaf veins. 0.5 g of the leaf was randomly selected as a sample, and each group was repeated 5 times. The leaves were placed in a cryocirculator for temperature treatment at a temperature gradient of 2, 0, -2, -4, -6, and -8 for 2 hours each. The leaves were removed and immersed in a test tube containing 50 ml of deionized water. The test tube was placed on a shaker for 2-3 hours. After treatment, the leaves were removed and allowed to stand for 20-30 minutes. The frozen conductivity of the leaf samples was measured using a DDS-308A conductivity meter. The leaves were treated in a boiling water bath for 10 minutes, removed and cooled to room temperature, and then the boiling conductivity of the leaf samples was measured. Electrolyte extravasation rate = frozen conductivity / boiling conductivity × 100%. The logistic regression equation for the electrolyte extravasation rate is: , Where y is the relative conductivity under low temperature treatment, x is the treatment temperature, k, a, and b are parameters, k is the maximum limit value of y, b reflects the corresponding relationship between x and y, and a represents the relative position of the curve to the origin. The inflection point of the curve is used as the semi-lethal temperature of Pennisetum: .

[0052] Malondialdehyde (MDA) sampling was performed using the same method as for the semi-lethal temperature sampling. A 0.5g sample of leaves was collected and assayed using a kit from Suzhou Gress Biotechnology Co., Ltd. The MDA content at the lowest temperature of 10°C was recorded as MDA10, and the MDA content at the lowest temperature of 0°C was recorded as MDA0. The MDA change, ΔMDA, was calculated as ΔMDA = MDA0 - MDA10. Conductivity sampling was performed using the same method as for the semi-lethal temperature sampling. A 0.5g sample of leaves was added to 50mL of deionized water, fully immersing the wrapped sample. After standing at room temperature for 24 hours, the initial conductivity (S1) was measured using a DDS-308A conductivity meter. The samples were boiled in a water bath until the plant tissue was completely killed, then cooled to room temperature and the final conductivity (S2) was measured.

[0053] Relative conductivity EL: .

[0054] The relative conductivity at the lowest temperature of 10℃ is recorded as EL10, and the relative conductivity at the lowest temperature of 0℃ is recorded as EL0. The relative conductivity change value ΔEL is calculated, ΔEL = EL0- EL10.

[0055] To determine the sugar content of the stems, select Pennisetum stalks of uniform growth. Cut the stems from the base of the stem to the third or fourth node, remove the leaves, and extract the juice using a juicer. The sugar content is quickly measured using an ATAGO PAL-1 (NFC) handheld digital sugar meter. The sugar content at a minimum temperature of 10°C is designated as SSC10, while the sugar content at a minimum temperature of 0°C is designated as SSC0. The change in sugar content, ΔSSC, is calculated as ΔSSC = SSC0 - SSC10.

[0056] The results of the four cold resistance indexes are shown in Table 2.

[0057] Low temperature photosynthetic capacity assay (4): As field temperatures dropped in autumn, trait measurements began when the minimum temperature dropped to 10°C. Plants were subjected to natural low-temperature stress in the field and then measured again when the minimum temperature dropped to 0°C. Chlorophyll content and green leaf percentage (GLP) were determined during the low-temperature stress period. Plants were then mowed, leaving stubble at 15 cm, and allowed to overwinter naturally. The following year, when the minimum temperature returned to 10°C and 15°C, the number of root-cut seedlings that re-greened (ratoon) was counted and their chlorophyll content was measured. Six replicates were used.

[0058] Chlorophyll content was measured using a CL-01 chlorophyll meter (Hansatech). Measurements were made on the flat, mid-section of the leaf surface to prevent vein pinching. Chlorophyll content was recorded when the minimum temperature in autumn dropped to 10°C as Chl_Fal10, and when the minimum temperature in autumn was 0°C as Chl_Fal0. The change in chlorophyll content in autumn was recorded as ΔChl_Fal, where ΔChl_Fal = Chl_Fal0 - Chl_Fal10. Chlorophyll content was recorded when the minimum temperature in spring returned to green as 10°C as Chl_Spr10, and when the minimum temperature returned to 15°C as Chl_Spr15. The change in chlorophyll content in spring was recorded as ΔChl_Spr, where ΔChl_Spr = Chl_Spr15 - Chl_Spr10. The number of seedlings that returned to green after root cutting after overwintering was manually counted. The number of root-cut seedlings that returned to green when the minimum temperature returned to 10°C in spring was recorded as Ratoon_Spr10. The green leaf percentage was calculated by manually counting the proportion of green leaves to the total number of Pennisetum leaves. Leaves with 50% or more of their area showing yellowing were considered yellow, with the remainder considered green. The green leaf percentage was calculated as the number of green leaves divided by the total number of leaves. The green leaf percentage when the minimum temperature dropped to 10°C in autumn was recorded as GLP10, and the green leaf percentage when the minimum temperature was 0°C in autumn was recorded as GLP0. The change in green leaf percentage in autumn was recorded as ΔGLP, where ΔGLP = GLP0 - GLP10.

[0059] The results of the four low-temperature photosynthetic capacity tests are shown in Table 2.

[0060] The yield traits, cold resistance index, and low-temperature photosynthetic capacity measured were weighted using principal component analysis. Based on the weight analysis results, the membership function method was used to comprehensively evaluate the Pennisetum samples and calculate the comprehensive score D value. Principal component score calculation: , : The component matrix value (loading) of the j-th trait in the k-th principal component; : initial eigenvalue of the kth principal component.

[0061] Calculate the variance percentage weights using the principal component scores: , Calculate model coefficients using variance percentage weights: , m: the number of principal components selected Calculate the final trait weights (normalized): , Phenotypic data normalization: , : The original value of the jth trait of the i-th sample min(Xj) max(Xj): minimum and maximum values ​​of the jth trait Weighted membership function calculation: , : The weight of the j-th trait obtained by the PCA weight method, p: Total number of traits.

[0062] Table 1 Normalized statistical table of phenotypic data of yield traits of 19 Pennisetum accessions Table 2 Normalized statistical table of cold-resistant photosynthetic phenotypic data of 19 Pennisetum accessions The results of the correlation analysis of 18 traits in this example are as follows Figure 1 shown. Figure 1 The correlation between the 18 key traits involved in the present invention is fully demonstrated in the form of a scatter matrix. The horizontal axis and the vertical axis represent different trait indicators, and each scatter plot represents the relationship between two traits. Figure 1 The correlation patterns between various traits can be clearly observed. For example, plant height (PH) and stem diameter (SD) show a significant positive correlation, indicating that stem diameter tends to increase with increasing plant height. Meanwhile, there is a negative correlation between the semi-lethal temperature (LT50) and the change in malondialdehyde (ΔMDA), suggesting that under low-temperature stress, as the degree of cell membrane lipid peroxidation increases, the plant's cold tolerance may decrease accordingly. This intuitive visualization method can help researchers quickly understand the interactions and associations between various traits, providing a strong basis for in-depth analysis of the high-yield and cold-tolerance properties of Pennisetum.

[0063] The cluster analysis results of 19 test materials in this example are as follows Figure 2 shown. Figure 2 The cluster analysis results of 19 Pennisetum test materials are presented in the form of a dendrogram. The horizontal axis represents the test material number, and the vertical axis represents the cluster distance or similarity degree. Figure 2As can be seen from the data, the 19 accessions were divided into several major clusters. For example, accession Pp014 was grouped into one cluster, while accessions Pp071, Pp085, Pp030, and Pp033 were grouped into another, indicating significant differences between these two clusters. This cluster analysis helps quickly identify groups of Pennisetum germplasm with similar traits, providing clear guidance for subsequent breeding material selection and variety improvement.

[0064] Table 3 D values ​​and rankings of 19 Pennisetum accessions for high-yield and cold-resistance evaluation Figure 3 The statistical results of the number of green seedlings after root cutting of some materials overwintering are shown. Figure 3 It is clear that the number of green seedlings that returned to green varies significantly between different accessions. For example, accession Pp014 had a relatively large number of green seedlings, indicating strong recovery after wintering, good cold tolerance, and growth vigor. In contrast, accession Pp010 had a relatively small number of green seedlings, suggesting that it may have suffered significant low-temperature damage during wintering and has a weaker recovery ability. This statistical chart allows researchers to clearly compare the greening abilities of the various accessions tested, thereby selecting Pennisetum germplasm that can quickly resume growth after wintering. This is of great significance for cultivating Pennisetum varieties that can grow stably in low-temperature environments.

[0065] Through the above cluster analysis, Pennisetum germplasms can be divided into high-yielding, medium-yielding, and low-yielding types based on yield, and can also be divided into cold-resistant and sensitive types based on cold tolerance. Based on the comprehensive cluster analysis results, one high-yielding and cold-resistant breeding material, Pp014, and six low-yielding and cold-resistant Pennisetum germplasms, Pp033, Pp144, Pp087, Pp085, Pp030, and Pp071, were quickly selected from the 19 Pennisetum germplasms mentioned above.

[0066] The present invention uses a multi-dimensional dynamic monitoring system spanning the autumn, winter and spring seasons to conduct a full-cycle assessment of Pennisetum from stress response to recovery ability. The combination of yield traits, cold resistance indicators and low-temperature photosynthetic capacity indicators provides a reliable basis for the evaluation of high-yield and cold-resistance of Pennisetum in the field. This method not only greatly reduces time and economic costs, but also makes the data more in line with actual production scenarios through multi-environment collaborative surveys, effectively avoiding the deviation of artificial simulation of stress. It significantly improves the efficiency of parent selection, offspring screening and variety stabilization in Pennisetum breeding, provides strong methodological support for the cultivation of breakthrough varieties that are "cold-resistant without reducing yields", and demonstrates significant application value.

[0067] In the description of the present invention, it should be understood that "-" and "~" represent a range between two values, and the range includes the endpoints. For example, "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0068] In the description of the present invention, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0069] In the description of the invention, the numerical values ​​of time, temperature, ratio and mass involved may be based on actual measurements, standard parameters of equipment, simplified rounding results, or within an acceptable error range, ensuring the practicality and repeatability of the invention.

[0070] In the description of the present invention, the term "about" or "approximately" is used to express the approximate value of a numerical value or range, allowing a certain error to ensure the flexibility and practicality of the description while remaining within an acceptable error range, with the maximum error range not exceeding 10% of the corresponding numerical value or numerical range.

[0071] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. Persons skilled in the art will appreciate that improvements and modifications may be made without departing from the spirit and scope of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for screening high-yield cold-resistant Pennisetum germplasm, characterized in that: The method is based on 10 yield traits, 4 cold resistance indicators and 4 low-temperature photosynthetic capacity indicators of Pennisetum, and constructs a multi-dimensional dynamic monitoring system across autumn, winter and spring to conduct a comprehensive evaluation of the high-yield and cold-resistant characteristics of Pennisetum germplasm, thereby screening out high-yield and cold-resistant breeding materials.

2. The screening method according to claim 1, wherein The 10 yield traits include plant height, number of stem nodes, internode length, stem diameter, leaf length, leaf width, number of leaves, fresh weight of single tiller, dry-to-fresh ratio and chlorophyll content.

3. The screening method according to claim 1, wherein The four cold resistance indicators include semi-lethal temperature, malondialdehyde change value, electrical conductivity change value and stem sugar content change value.

4. The screening method according to claim 1, wherein The four low-temperature photosynthetic capacity indicators include changes in chlorophyll content under low-temperature stress, changes in the percentage of green leaves, the number of root-cut seedlings that regreen, and changes in chlorophyll content when regreened.

5. The screening method according to claim 1, wherein The multi-dimensional dynamic monitoring system is constructed in the following ways: (1) Yield trait measurement: Growth and development traits of Pennisetum were measured at the initial flowering stage. The measured indicators included plant height, number of stem nodes, internode length, stem diameter, leaf length, leaf width, number of leaves, fresh weight of single tiller, dry-to-fresh ratio, and chlorophyll content. Each indicator was measured with multiple replicates. (2) Determination of cold resistance indexes: During the period of decreasing field temperature in autumn and winter, samples were taken when the daily minimum temperature dropped to 10°C and 0°C, respectively. The measured indexes included half-lethal temperature, malondialdehyde, electrical conductivity, and sugar content in the stems. Each index was measured in multiple replicates. (3) Determination of low-temperature photosynthetic capacity: During the process of decreasing field temperature in autumn, measurements were carried out when the daily minimum temperature dropped to 10℃ and 0℃, respectively. The measurement indicators included the chlorophyll content under low-temperature stress and the percentage of green leaves. Subsequently, the stubble was cut and left for wintering. When the minimum temperature rose to 10℃ and 15℃ in the following year, the number of root-cut seedlings that turned green were counted and the chlorophyll content of the turned green was measured. Each indicator was measured with multiple replicates.

6. The screening method according to claim 1, wherein The screening method also includes using the principal component analysis method to perform weight analysis on the measured yield traits, cold resistance indicators and low-temperature photosynthetic capacity. Based on the weight analysis results, the membership function method is used to comprehensively evaluate the Pennisetum samples and calculate the comprehensive score D value, thereby achieving a comprehensive evaluation of the high yield and cold resistance of the Pennisetum germplasm.

7. The screening method according to claim 6, characterized in that The specific steps of the principal component analysis method and the membership function method are as follows: (1) Calculation of principal component scores: The principal component scores are calculated based on the component matrix values ​​of each trait in the principal component and the initial eigenvalues ​​of the principal component; (2) Variance percentage weight calculation: Calculate the variance percentage weight using the principal component scores; (3) Model coefficient calculation: Calculate the model coefficient based on the variance percentage weight; (4) Final trait weight calculation: Normalize the model coefficients to obtain the final trait weight; (5) Phenotypic data normalization: normalize the original phenotypic data; (6) Calculation of weighted membership function: Based on the normalized phenotypic data and the final trait weight, the weighted membership function value is calculated to obtain the comprehensive score D value.

8. The screening method according to claim 1, wherein The screening method is suitable for screening Pennisetum germplasm under a natural low-temperature stress environment in the field, and can accurately reflect the growth and adaptation of Pennisetum under dynamic low-temperature stress in the natural field.

9. The screening method according to claim 1, wherein The screening method can significantly save time and economic costs, and improve the efficiency of parent selection, offspring screening and variety stabilization in Pennisetum breeding.

10. An application of a high-yield cold-resistant Pennisetum breeding material obtained by screening using the screening method according to any one of claims 1 to 9 in Pennisetum breeding, characterized in that: The breeding material is used for breeding a breakthrough Pennisetum variety that is cold-resistant and has no yield reduction.

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