A method for predicting overwintering rate and cold tolerance of brassica napus

Abstract

The application provides a method for predicting overwintering rate and cold resistance of Brassica napus, and belongs to the technical field of rape cultivation. The method comprises the following steps: sowing seeds of Brassica napus to be evaluated in a field; in early December, after the minimum temperature in the field is lower than-4 DEG C and lasts for 5-10 days, the key indicators of Brassica napus are determined; the determined key indicators are substituted into a regression equation of a linear regression model to calculate the predicted overwintering rate of Brassica napus; and the cold resistance of Brassica napus is identified according to the obtained predicted overwintering rate. Compared with the traditional natural overwintering identification, the application can obtain the predicted overwintering rate by only determining 7 key indicators once before winter, and complete the cold resistance grading evaluation, so that the identification period is shortened.

Description

Technical Field

[0001] This invention relates to the field of rapeseed cultivation technology, and in particular to a method for predicting the overwintering rate and cold resistance of winter rapeseed of the Brassica napus type. Background Technology

[0002] Winter rapeseed is an important oilseed crop in my country's winter agriculture, and its yield and quality stability are highly dependent on the overwintering survival rate. With the northward shift of rapeseed production areas and the intensification of climate fluctuations, the impact of low-temperature freezing damage on winter rapeseed overwintering is becoming increasingly prominent. Establishing a scientific, standardized, and repeatable field overwintering cold resistance assessment system has become an urgent need for cold-resistant breeding, variety layout, and production management. Although traditional field natural overwintering assessment can reflect the comprehensive performance of materials in real-world environments, it often requires waiting until the rapeseed turns green before calculating the overwintering rate, resulting in a long cycle. Furthermore, it is greatly affected by year, location, and management conditions, leading to discrete indicators and making it difficult to form a standardized, quantitative, and repeatable evaluation system.

[0003] Studies have shown that the phenotype and physiological state of seedlings before winter play a decisive role in overwintering. Under low temperatures, a compact plant structure with shortened stems reduces the risk of frost damage; hypocotyl and petiole length affect the degree of growth point exposure; root characteristics such as root length, number of lateral roots, and root diameter are related to plant stability and water absorption; aboveground dry weight, underground dry weight, and root-to-shoot ratio reflect nutrient allocation strategies, with underground dominance usually contributing to enhanced cold resistance. Furthermore, membrane conductivity, as a core physiological indicator, can objectively reflect the degree of cell membrane damage, providing an important supplement for phenotypic identification. Given that winter cold resistance in rapeseed is a complex process involving the synergistic effects of multiple traits, encompassing multi-level regulation of morphology, biomass allocation, and cell membrane stability, there is an urgent need for a comprehensive field evaluation method based on multi-indicator fusion that can efficiently, accurately, and rapidly identify the winter cold resistance of Brassica napus varieties. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for predicting the overwintering rate and cold resistance of winter rapeseed, thereby achieving the goal of predicting overwintering cold resistance using key indicator phenotypes during the seedling stage before overwintering.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a method for predicting the overwintering rate and cold resistance of Brassica napus winter rapeseed, comprising the following steps: (1) Sow the seeds of the winter rapeseed of the Brassica napus type to be evaluated in the field; (2) In early December, after the minimum field temperature was below -4℃ for 5 to 10 days, the key indicators of winter rapeseed of the Brassica napus type were measured. The key indicators included: leaf electrical conductivity, chlorophyll content, hypocotyl length, leaf angle, taproot length, growth cone height, and number of leaf lobes. (3) Substitute the key indicators measured in step (2) into the regression equation of the linear regression model to calculate the predicted overwintering rate of winter rapeseed; the regression equation is:

[0006] Where REL is electrical conductivity, Chl is chlorophyll content, HL is hypocotyl height, LIA is leaf angle, PRL is principal root length, GPH is growth cone height, and LLN is leaf number. (4) The cold resistance of winter rapeseed of the Brassica napus type is determined based on the predicted overwintering rate obtained in step (3).

[0007] Preferably, the sowing time in step (1) is from mid-August to early September, and the sowing density is 40 to 60 plants per square meter.

[0008] Preferably, the criteria for judging the cold resistance of winter rapeseed in step (4) are as follows: if the predicted overwintering rate is ≥85%, it is a strongly cold-resistant variety; if 85% > predicted overwintering rate ≥65%, it is a cold-resistant variety; if 65% > predicted overwintering rate ≥45%, it is a weakly cold-resistant variety; if the predicted overwintering rate <45%, it is a non-cold-resistant variety.

[0009] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for identifying the overwintering rate and assessing the cold resistance of winter rapeseed by establishing a linear regression model. After planting the variety to be predicted in the field, its morphological indicators and electrical conductivity are measured before overwintering. The theoretical overwintering rate is then calculated using the model, and the cold resistance of the variety is judged based on the range of the theoretical overwintering rate. The regression model utilizes a diverse and significantly varied set of materials from a wide range of data sources, resulting in a broad applicability and high accuracy in prediction. This method can be used for identifying the cold resistance of rapeseed varieties and evaluating their regional adaptability; it is simple to operate and highly accurate.

[0010] Compared with traditional natural overwintering identification, this invention only requires a one-time measurement of 7 key indicators before winter to calculate the predicted overwintering rate and complete the cold resistance grading evaluation, thus shortening the identification cycle. By integrating multiple indicators, the risk of misjudgment by a single indicator is reduced, and the stability and repeatability of the evaluation are improved. It is suitable for large-scale screening of multiple locations and materials, and can provide rapid and quantifiable technical support for cold-resistant breeding, regional adaptability evaluation of varieties, and production layout. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 Heat dissipation diagram of natural population BLUE value; Figure 2 The results of classifying actual and predicted values ​​in cold-resistant regions; Figure 3 This is a linear fit plot of the actual and predicted values. Detailed Implementation

[0013] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0014] Example 1

[0015] A method for predicting the overwintering rate and cold resistance of winter rapeseed (Brassica napus) includes the following steps: (1) Select plump seeds from the winter rapeseed varieties of the Brassica napus to be evaluated and sow them in the field from mid-August to early September at a density of 50 plants per square meter. The experiment adopted a single-factor randomized block design with two replicates.

[0016] (2) In early December, after the minimum field temperature was below -4℃ for 7 consecutive days, the key indicators (leaf electrical conductivity, chlorophyll content, hypocotyl length, leaf angle, taproot length, growth cone height, and number of leaf lobes) of winter rapeseed in the replicate plots were measured. Three individual plants were measured in each plot. The measurement method is as follows: The electrical conductivity of the blades was measured using a magnetic conductivity meter. Chlorophyll content was measured using a chlorophyll meter; The measurement of leaf angle, number of leaf lobes, and taproot length was performed in accordance with the national standard GB / T 19557.14-2017 Guidelines for Testing the Distinctiveness, Uniformity and Stability of Plant Varieties (Brassica napus type). The height of the growth cone is measured from the cotyledonary node to the apex of the meristem. Hypocotyl length is measured from the cotyledon node to the uppermost edge of the root collar.

[0017] (3) Substitute the key indicators measured in step (2) into the regression equation of the linear regression model to calculate the predicted overwintering rate of winter rapeseed; the regression equation is: Predicated_OWR (Predicted Overwintering Rate) = 27.756 - 1.107 × REL + 0.933 × Chl - 15.681 × HL - 0.516 × LIA + 1.803 × PRL - 0.965 × GPH + 1.037 × LLN (Measured Value); where REL is electrical conductivity, Chl is chlorophyll content, HL is hypocotyl height, LIA is leaf angle, PRL is taproot length, GPH is growth cone height, and LLN is leaf number.

[0018] (4) The cold resistance of winter rapeseed of Brassica napus is determined based on the predicted overwintering rate obtained in step (3). The criteria for judging the cold resistance of winter rapeseed of Brassica napus are as follows: if the predicted overwintering rate is ≥85%, it is a strongly cold-resistant variety; if 85% > predicted overwintering rate ≥65%, it is a cold-resistant variety; if 65% > predicted overwintering rate ≥45%, it is a weakly cold-resistant variety; if the predicted overwintering rate <45%, it is a non-cold-resistant variety.

[0019] Example 2

[0020] Experiments will be conducted in Jingchuan and Tianshui, Gansu Province, and Taibai County, Shaanxi Province, from 2023 to 2024. Experiments will be conducted in Jingchuan and Tianshui, Gansu Province, from 2024 to 2025.

[0021] (1) 289 core natural population germplasm accessions of Brassica napus (from Zhejiang University) from around the world were selected as the research subjects. The experiment adopted a single-factor randomized block design with two replicates; the plot area was 2m². 2 The rows were 2m long and 1m wide, with 5 rows planted per plot, 20cm apart in rows and 10cm apart in plants. The final seedling density was 100 plants per plot. All experiments used manual row sowing and seedling thinning, and fertilization and field management were the same as in the field.

[0022] (2) In early December, when the minimum field temperature was below -4℃ for a week (7 days), the key indicators of winter rapeseed of Brassica napus in the replicate plots were measured. Three plants were randomly selected from each plot. The key indicators were: relative electrical conductivity of leaves (REL), chlorophyll content (Chl), leaf angle (LIA), leaf length (LL), leaf width (LW), petiole length (PL), leaf length (LBL), number of leaf lobes (LLN), aboveground fresh weight (SFW), aboveground dry weight (SDW), taproot length (PRL), number of lateral roots (LRN), root diameter (RD), underground fresh weight (RFW), underground dry weight (RDW), root-to-shoot ratio (dry / fresh) (RSR(DW), RSR(FW)), growth cone height (GPH), and hypocotyl length (HL).

[0023] The determination method is as follows: The electrical conductivity of the blades was measured using a magnetic conductivity meter. Chlorophyll content was measured using a chlorophyll meter; The measurements of leaf angle, leaf length, leaf width, petiole length, blade length, number of leaf lobes, taproot length, number of lateral roots, and root diameter were performed in accordance with the national standard GB / T 19557.14-2017 Guidelines for Testing the Distinctiveness, Uniformity and Stability of Plant Varieties (Brassica napus). The height of the growth cone is measured from the cotyledonary node to the apex of the meristem. Hypocotyl length is measured from the cotyledon node to the uppermost edge of the root collar. The fresh weight of the above-ground and underground parts was directly weighed using an electronic balance; the dry weight of the above-ground and underground parts was directly weighed using an electronic balance after the moisture in the drying oven was dried. The root-to-shoot ratio is calculated using the method of "root-to-shoot ratio = fresh weight of underground part / fresh weight of aboveground part".

[0024] At the same time, the number of plants in the community before winter and after greening up is counted, and the overwintering rate is calculated according to the following formula: Overwintering rate % = Number of plants after greening up / Number of plants before winter × 100%.

[0025] When determining key indicators, conductivity was measured using a magnetic conductivity meter. Chlorophyll content was measured using a chlorophyll meter; The measurement of leaf angle, number of leaf lobes, and taproot length should refer to the Guidelines for Testing Varieties, Uniformity, and Stability of Brassica napus (National Standard of the People's Republic of China). The height of the growth cone is measured from the cotyledonary node to the apex of the meristem. Hypocotyl length is measured from the cotyledon node to the uppermost edge of the root collar.

[0026] (3) The data obtained in (2) were integrated and analyzed. After removing missing or outlier values, 284 sets of standardized data were obtained. The training set and the test set were divided in a 7:3 ratio (200 sets of training data and 84 sets of test data).

[0027] (4) Calculate the BLUE of each trait in the training set divided in (3), and create a heat dissipation map of the BLUE value of the natural population. Figure 1 To clarify the direction and strength of the association between each overwintering / cold resistance trait and the overwintering rate, as well as the interrelationships among traits, IBM SPSS Statistics 27.0.1 was used to calculate and analyze the correlation coefficients between the tested trait indicators and the overwintering rate, and Pearson correlation tests were performed. Trait indicators that reached extremely significant correlations were selected as the main parameters for model establishment, and the results are shown in Table 1. Among them, the correlation between the overwintering rate and electrical conductivity, chlorophyll content, hypocotyl length, leaf angle, taproot length, growth cone height, and number of lobed leaves reached an extremely significant level (p<0.001), with an absolute value of correlation coefficient greater than 0.297.

[0028] To further clarify the accuracy and reliability of trait selection, indicators with correlation coefficients p < 0.001 were further screened by calculating the population heritability of five pilot populations over two years. The population heritability was calculated using the following formula:

[0029] Among them, V G V represents the genetic variance. P This represents the total phenotypic variance.

[0030] The calculation results are shown in Table 2. Traits with calculation results below 0.4 were removed, namely, aboveground dry weight (SDW) and dry weight root-to-shoot ratio (RSR.DW).

[0031] Finally, seven key traits were selected as the key indicators for model establishment: leaf electrical conductivity (REL), chlorophyll content (Chl), hypocotyl length (HL), leaf angle (LIA), taproot length (PRL), growth cone height (GPH), and number of lobed leaves (LLN).

[0032] Table 1. Correlation between phenotypic indicators and overwintering rate

[0033] Table 2 Population heritability of trait indicators

[0034] (5) A linear regression model was established using the seven key indicators related to the overwintering rate obtained from the analysis in (4). The regression equation of the linear regression model is as follows: Predicated_OWR (Predicted Overwintering Rate) = 27.756 - 1.107 × REL + 0.933 × Chl - 15.681 × HL - 0.516 × LIA + 1.803 × PRL - 0.965 × GPH + 1.037 × LLN (Measured Value); where REL is electrical conductivity, Chl is chlorophyll content, HL is hypocotyl height, LIA is leaf angle, PRL is taproot length, GPH is growth cone height, and LLN is leaf number.

[0035] (6) The regression model was validated using 84 sets of data in the test set. The model performance was rigorously evaluated using metrics such as mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²).

[0036] It is noteworthy that principal component regression and linear regression demonstrated superior performance in model construction, with linear regression showing the most significant effectiveness in constructing the overwintering rate regression prediction model. Therefore, the linear regression model was used for subsequent overwintering rate prediction.

[0037] (7) The prediction results were verified according to the method described in (6), and are shown in Table 3. The R² of the linear regression model is 0.7612, indicating that the model can explain 76.12% of the data variation. Its RMSE is 12.76 and MAE is 9.05, indicating that the prediction error is within a reasonable range. The RMSE and MAE are relatively close in value, which shows that the prediction error distribution of the model is relatively uniform and has not been affected by too many extreme outliers, and the prediction stability is good. This shows that the establishment of this model has certain practical value.

[0038] Table 3 Model Evaluation Results

[0039] (8) Substitute the seven trait indicators of the 84 sets of data in the test set into the established regression model to calculate the predicted overwintering rate, and compare it with the actual overwintering rate measured in the actual region. Figure 2 Of the 84 varieties in the test set, 34 were considered non-cold-resistant with an actual overwintering rate of less than 35%, compared to the predicted rate of 33, with 29 falling somewhere in between. 26 were considered weakly cold-resistant with an actual overwintering rate between 35% and 60%, compared to the predicted rate of 29, with 16 falling somewhere in between. 23 were cold-resistant with an actual overwintering rate between 60% and 85%, compared to the predicted rate of 22, with 15 falling somewhere in between. A total of 60 varieties (71.4%) had actual and predicted values ​​within the same cold-resistant region. This indicates a small discrepancy between the actual and predicted values, and a relatively small error distribution, further demonstrating the applicability of this model.

[0040] (9) Linear fitting line ( Figure 3 Table 4 shows the positive correlation between actual and predicted values. The 95% confidence band surrounds the fitted line, representing the uncertainty of the regression line itself; the 95% prediction band is wider, representing the range of uncertainty when predicting a single new observation. Most actual observation points fall within the prediction band, which is basically consistent with the model fitting results, further supporting the reliability of the model.

[0041] Table 4. Equations of the linear fitting curves

[0042] (10) The cold resistance of winter rapeseed varieties of Brassica napus is judged based on the predicted overwintering rate: if the predicted overwintering rate is ≥85%, it is a strong cold-resistant variety; if 85% > predicted overwintering rate ≥65%, it is a cold-resistant variety; if 65% > predicted overwintering rate ≥45%, it is a weak cold-resistant variety; if the predicted overwintering rate <45%, it is a non-cold-resistant variety.

[0043] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting the overwintering rate and cold resistance of winter rapeseed (Brassica napus), characterized in that, Includes the following steps: (1) Sow the seeds of the winter rapeseed of the Brassica napus type to be evaluated in the field; (2) In early December, after the minimum field temperature was below -4℃ for 5 to 10 days, the key indicators of winter rapeseed of the Brassica napus type were measured. The key indicators included: leaf electrical conductivity, chlorophyll content, hypocotyl length, leaf angle, taproot length, growth cone height, and number of leaf lobes. (3) Substitute the key indicators measured in step (2) into the regression equation of the linear regression model to calculate the predicted overwintering rate of winter rapeseed; the regression equation is: Where REL is electrical conductivity, Chl is chlorophyll content, HL is hypocotyl height, LIA is leaf angle, PRL is principal root length, GPH is growth cone height, and LLN is leaf number. (4) The cold resistance of winter rapeseed of the Brassica napus type is determined based on the predicted overwintering rate obtained in step (3).

2. The method according to claim 1, characterized in that, The sowing time mentioned in step (1) is from mid-August to early September; the sowing density is 40 to 60 plants per square meter.

3. The method according to claim 1, characterized in that, In step (4), the criteria for judging the cold resistance of winter rapeseed of the Brassica napus type are as follows: if the predicted overwintering rate is ≥85%, it is a strongly cold-resistant variety; if 85% > the predicted overwintering rate is ≥65%, it is a cold-resistant variety; if 65% > the predicted overwintering rate is ≥45%, it is a weakly cold-resistant variety; if the predicted overwintering rate is <45%, it is a non-cold-resistant variety.

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