CN112362803B

CN112362803B - Application of LY9348 in high-throughput screening of high NUE rice varieties

Info

Publication number: CN112362803B
Application number: CN202011001533.3A
Authority: CN
Inventors: 吴贤婷; 朱仁山; 龚龑; 彭漪; 方圣辉
Original assignee: Wuhan University WHU
Current assignee: Wuhan University WHU
Priority date: 2020-09-22
Filing date: 2020-09-22
Publication date: 2022-04-22
Anticipated expiration: 2040-09-22
Also published as: CN112362803A

Abstract

The invention relates to application of nitrogen-related traits of LY9348 in high-throughput screening of high NUE rice varieties and a method for screening the high NUE rice varieties. The invention discloses a LY9348 with high NUE characteristic, discloses a nitrogen-related trait of LY9348, and a method for high-throughput screening of high NUE rice varieties by using LY9348 and the nitrogen-related trait thereof. Based on the content of the invention, a person skilled in the art can monitor the vegetation index or the nitrogen content calculated from the vegetation index in the rice growth process in a large-scale and high-throughput manner by using an unmanned aerial vehicle and a remote sensing technology, and then easily screen the rice variety with high NUE, thereby overcoming the dependence of high nitrogen fertilizer in the field of rice planting at the present stage, reducing the cost and reducing the environmental damage caused by the application of the high nitrogen fertilizer. The invention is a new attempt from traditional breeding to modern breeding, and is a further exploration towards intelligent agriculture and precision agriculture.

Description

Application of LY9348 in high-throughput screening of high NUE rice varieties

Fund support

The research and development of the project are subsidized by the national rice industry system (CARS-01-07).

Technical Field

The invention relates to the field of intelligent agriculture, in particular to application of LY9348 in high-throughput screening of high NUE rice varieties.

Background

Nitrogen (N) is an essential nutrient for plant growth and plays an important role in photosynthesis, energy conversion, structural composition, and biosynthesis. The plants absorb N from the soil, and the N is transported to specific organs through a vascular system and accumulated into nitrogen-containing compounds, and then the nitrogen-containing compounds are decomposed and migrated to target organs to participate in various life activities of the plants, so that the nutrition balance in the plants is maintained.

In grain production, the application of high nitrogen fertilizer is one of the key factors influencing yield. However, in recent years, with the increase in nitrogen fertilizer application, the yield of food crops has not increased correspondingly, but has reached a plateau. From 1980 to 2010, the nitrogen fertilizer application amount in China is increased by 512%, and the grain yield is only increased by 65%. Excessive nitrogen fertilizer application not only increases costs, but also results in reduced nitrogen utilization and nitrogen loss. The overflow of field nitrogen fertilizer into soil and water also causes serious environmental problems. According to estimates, if nitrogen utilization efficiency is increased by 1%, the annual fertilizer cost worldwide can be reduced by $ 23 billion.

Rice is one of the important food crops in the world and provides food for nearly half of the global population. The rice yield of China is the highest in the world, but the average nitrogen fertilizer application amount in the rice field of China is 180-year old 209kg/hm²Far above the world average level of 105kg/hm²And the actual use efficiency is only about 30-35%. By optimizing field management to improve the Nitrogen Utilization Efficiency (NUE) of rice, the application amount of Nitrogen fertilizer can be reduced to 150-165kg/hm². However, to achieve maximum yield potential (10-15 Mt/hm)²) Most super rice varieties need up to 300kg/hm²The amount of nitrogen fertilizer applied. Therefore, the problem of nitrogen utilization efficiency cannot be fundamentally solved by reducing the application amount of the nitrogen fertilizer from the field management point of view alone. Breeders would like to be able to screen rice varieties with high NUE to completely solve this problem.

Many efforts have been made in the field of rice breeding to select high NUE breeding, but progress has been slow to date. The main obstacle is that there is no simple and easy way to capture the nitrogen content variation in rice and further characterize nitrogen utilization efficiency. Because, nitrogen content changes in rice are manifested both at the spatial level (canopy morphology changes) and at the temporal level (developmental changes throughout the reproductive cycle, such as the switch between vegetative and reproductive growth).

As the uptake and utilization of nitrogen in rice are complex comprehensive behaviors and relate to various life activities, at present, a breeder cannot find a certain gene or gather a gene group to serve as a molecular marker of high NUE. According to the traditional method, a rice sample is collected at a specific growth stage (such as heading stage or milk stage) to measure the nitrogen content so as to characterize the NUE, on one hand, the nitrogen utilization rate cannot be accurately characterized, on the other hand, the method is time-consuming and labor-consuming and cannot be used for high-throughput screening.

Therefore, on the one hand, there is a need to find plants with high NUE traits and, on the other hand, new methods for describing high NUE traits that are suitable for high throughput screening.

With the development of remote sensing technology and unmanned aerial vehicles and the improvement of camera resolution, the combination of the remote sensing technology and the camera is gradually used for agricultural production and research. Lukas Prey et al placed the spectrometer approximately 1m above the wheat canopy, and measured the reflectance of the canopy and used to evaluate the corresponding physiological data. Zheng Heng Biao and so on of the Nanjing agriculture university use an unmanned aerial vehicle carried camera to shoot images of crops, and analyze and evaluate physiological parameters of the crops based on the images, and show that certain correlation exists between some physiological parameters and VI calculated by the images shot by the unmanned aerial vehicle. However, these studies have focused on exploring measurement methodologies for estimating relevant physiological parameters using VI, and there is currently no study on the application of these measurement methodologies to the exploration of a description of NUE phenotypes and how to screen rice varieties with high NUE.

Thus, there is a need for a rice variety with standard high NUE traits and for screening high NUE rice varieties for traits related to that variety.

Disclosure of Invention

In order to solve the problems, the research process of the group finds that the rice variety LY9348 has the characteristic of high NUE, and particularly has higher nitrogen uptake efficiency and nitrogen conversion efficiency compared with the male parent, the female parent and other rice varieties, namely, more nitrogen can be accumulated in the body under the condition of low nitrogen fertilizer application, and the yield is not influenced by the low nitrogen fertilizer application.

Based on the research, the invention provides the application of the nitrogen-related character of LY9348 in high-throughput screening of high NUE rice varieties.

The invention also provides a method for screening the high NUE rice variety, which comprises the step of using the nitrogen-related character of LY9348 as a screening standard.

In a preferred embodiment, the nitrogen-related trait is the vegetation index or nitrogen content of LY9348 for two or more periods of fertility.

Preferably, the vegetation index is an NDRE value.

Preferably, the nitrogen content is a nitrogen content calculated using the NDRE value.

The high-NUE rice variety is screened by using the NDRE value or the nitrogen content obtained by calculating the NDRE value, an unmanned aerial vehicle is combined with a remote sensing technology to obtain an image of a rice planting area, the NDRE value is calculated according to the image, and the nitrogen content is further calculated. The NDRE values or nitrogen content are then compared to determine whether the rice variety to be screened has a high NUE.

In a specific embodiment, the method comprises the steps of:

s1: acquiring nitrogen-related traits of LY9348 in multiple growth periods under a specific environment;

s2: acquiring nitrogen-related characters of the rice variety to be screened in a corresponding growth period under a corresponding environment;

s3: and comparing the nitrogen-related traits of the rice variety to be screened with the nitrogen-related traits of LY9348, and when the nitrogen-related traits of the rice variety to be screened are the same as or higher than the nitrogen-related traits of LY9348, determining that the rice variety to be screened is a high NUE rice variety.

The invention discloses a LY9348 with high NUE characteristic, discloses a nitrogen-related trait of LY9348, and a method for high-throughput screening of high NUE rice varieties by using LY9348 and the nitrogen-related trait thereof. Based on the content of the invention, a person skilled in the art can monitor the vegetation index or the nitrogen content calculated from the vegetation index in the rice growth process in a large-scale and high-throughput manner by using an unmanned aerial vehicle and a remote sensing technology, and then easily screen the rice variety with high NUE, thereby overcoming the dependence of high nitrogen fertilizer in the field of rice planting at the present stage, reducing the cost and reducing the environmental damage caused by the application of the high nitrogen fertilizer. The invention is a new attempt from traditional breeding to modern breeding, and is a further exploration towards intelligent agriculture and precision agriculture.

Drawings

FIG. 1 shows the nitrogen content variation over 6 growth periods for the growth of LY9348 and its parents (LH4B, CH9348) and two additional rice varieties R8108 and LY8 rice plants grown in the same test field, determined using different methods.

FIG. 2 is a comparison of the agronomic performance of FLY4H and LY9348 in a nitrogen application field trial wherein A is the number of grains per ear; b is the setting percentage (%); c is the rice yield per plant (g); d is NUE (rice yield per kg nitrogen). n ═ 30, denotes P < 0.05, 0.01 and 0.001, respectively.

Fig. 3 is RGB and NDRE images of 6 growth stages of 51 rice varieties collected using an unmanned aerial vehicle, wherein: a is RGB image of 6 birth periods, a is TS period, b is JS period, c is PIS period, d is BS period, e is FHS period, f is MRS period; b is an NDRE image of 6 birth periods, a is TS period, B is JS period, c is PIS period, d is BS period, e is FHS period, and f is MRS period.

FIG. 4 is a graph of 6 fertility data relationships for 51 rice varieties and constructed regression models, in which: a is the relationship between SPAD and NDRE in 6 growth periods, and n is 306; b is a regression model between SPAD and NDRE constructed by 5 fertility data except TS stage, and R is²Is more than 0.81, n is 255; c is the relationship between N% and NDRE for 6 growth periods, N306; b is a regression model between SPAD and NDRE constructed by 5 fertility data except TS stage, and R is²＞0.61，n＝255。

FIG. 5 is a graph of the relationship between SPAD, N% and NDRE during FHS, where: a is the relationship between SPAD and NDRE in all rice varieties (n is 51); b is the relationship between SPAD and NDRE in early-maturing (EM) rice cultivars (n ═ 34); c is the relationship between SPAD and NDRE in late-maturing (LM) rice cultivars (n ═ 17); d is the relationship between N% and NDRE in all rice varieties (N ═ 51); e is the relationship between N% and NDRE in early-maturing (EM) rice varieties (N ═ 34); f is the relationship between N% and NDRE in late-maturing (LM) rice cultivars (N ═ 17). P < 0.001.

Fig. 6 is a non-linear regression model between N% × LAI and NDRE constructed from data from 6 growth stages of 42 rice varieties grown in south hai.

Fig. 7 is a comparison of nitrogen content variation curves measured using EQA method (a) and estimated using model i (b) and model ii (c), respectively, from images acquired by unmanned aerial vehicle and calculated NDRE values for the full growth cycle of 51 rice varieties.

Detailed Description

1. Nitrogen utilization characteristics of Lopa excellent 9348(LY9348)

LY9348 is the hybrid rice variety bred by the sterile line 'Lopa Red 4B' and the restorer line 'Chenghui 9348' in 2016, examined and rated by the committee for examining the variety of crops in Hubei province, and the number of the variety examined and rated is 'Omega examined rice 2016014'.

1.1 curves of nitrogen content of LY9348

LY9348 and its parents (LH4B, CH9348) and two additional rice varieties R8108 and LY8H were planted in a test field, plant samples were taken during the 6 growth periods of the rice plants, and the nitrogen content changes of these 5 rice varieties were determined using quantitative elemental analysis (EQA). The 6 growth stages included: tillering Stage (TS), Jointing Stage (JS), ear differentiation stage (PIS), Booting Stage (BS), heading stage (FHS) and milk stage (MRS).

The EQA method comprises the following steps: selecting 3 plants, collecting functional leaves, oven drying at 80 deg.C to constant weight, grinding, sieving with 100 mesh sieve, and detecting nitrogen content. The average of the 3 plant data was used as the accurate leaf nitrogen content value for the corresponding rice variety.

As a result, LY9348 maintained a higher nitrogen content from the start of the JS period and this state continued until the end of the MRS period, as shown in fig. 1. Since both the male and female parents exhibit a much lower nitrogen content. This indicates that the higher nitrogen accumulation capacity of LY9348 is not inherited from the parent, but rather results from a combination of heterosis. Since these 5 varieties were grown in the same field, and were subjected to the same water and fertilizer management, higher nitrogen accumulation in LY9348 demonstrated higher nitrogen uptake efficiency (NUpE).

1.2 field nitrogen application amount tracking test

A field nitrogen application rate following experiment was performed, testing LY9348 and FLY4(CK) for 4 nitrogen applications (0kg/ha, 120kg/ha, 180kg/ha and 240 kg/ha). Seeding in the water in 12 and 10 months in 2018, and transplanting in 1 month in 2019. 3 replicates per application are randomly placed in the fieldIn the ground, 12 experimental cells are formed, every two experimental cells are spaced by 0.4m, and each experimental cell is covered by a 0.4m thin film. About 30m per experimental cell²Split in half, one half of LY9348, the other half of FLY4 (CK). 432 plants are planted in each plot, the hole distance is 15cm multiplied by 18cm, the plots are divided into 24 rows, and 18 plants are planted in each row. Before transplanting, all experimental plots were applied with base fertilizer, superphosphate (90kg/ha P)₂O₅) And potassium sulfate (180kg/ha K)₂O). Urea (N) was applied in three applications, at the time of seeding, at the tillering stage and at the booting stage. After transplantation, each experimental plot was maintained at 5cm water depth. On day 10 before harvest, the water was drained to facilitate harvest. The 30 plants were used to calculate grain number per panicle, grain yield per panicle (g). Grain yield per kg N was estimated based on the 100 plants harvested in the central area of each plot and calibrated by subtracting the standard moisture content of 13.5% at the time of calculation.

The results showed higher grain per ear (FIG. 2A), higher fruit yield per ear (FIG. 2B) and higher yield per ear (FIG. 2C) of LY9348 as compared to the control FLY4H, with the differences being more pronounced in the 0kg/ha N-dosed cells than in the 120, 180 and 240kg/ha N-dosed cells. Further studies found that the application of nitrogen fertilizer did not improve the yield of LY 9348. This demonstrates that LY9348 is able to absorb and utilize nitrogen already in the soil with high efficiency, without the need for large nitrogen fertilizer applications, suggesting that LY9348 is a variety with higher nitrogen use/accumulation efficiency (nut) and produces a yield that decreases with increasing nitrogen application rates per kg nitrogen (fig. 2D).

The above experiments show that LY9348 has high NUE and NUE, and we can conclude that LY9348 has a high Nitrogen Use Efficiency (NUE) (NUE ═ NUpE x nuee ═ rice production/soil nitrogen application). Therefore, LY9348 can be used as a marker post of the high NUE rice variety, and the marker post is used for screening the high NUE rice variety.

Therefore, the high NUE character is defined based on the remote sensing technology, a new screening method is designed according to the definition, and the method is verified experimentally.

2. Plant material and planting

From 3000 genome projects, 50 varieties (indica rice, australian rice and varieties between the two) with higher NUE were selected, and 51 rice varieties (table 1) were added, and planted in the experimental and research base of wuhan university rice (30.3756 ° N, 114.7448 ° E) in north hubei. The Huzhou rice is sown in 2017 in 5-month and 10-day rice and transplanted in 5-month and 31-day rice. The rice in the water of the hilly side germinates in 12-month and 10-month period in 2017, and is transplanted in 1-month and 6-month period in 2018.

Variety information of 151 rice varieties in Table

41 indica rice varieties, one purple rice and 42 rice varieties (Table 2) are selected from Chinese breeding projects, and are planted in Wuhan university hybrid rice experiment and research bases (18 degrees 03 '147.1' N,110 degrees 03 '34.9' E) in Hainan Ling water. The rice is sown in 2017, 12 and 10 days, and transplanted in 2018, 1 and 5 days.

Variety information of Table 242 Rice varieties

Except purple rice and LY9348, the rice varieties have higher NUE but cannot reach excellent rice varieties. The above rice variety has a planting density of 225000 plants per hectare and a total growth time of 6 to 7 months, which varies depending on the variety. 60 plants are planted in each variety, 10 plants are planted in one row, 6 rows are planted in total, the row spacing is 20cm, and the plant spacing is 16 cm. And 1 line is vacant every 6 lines to facilitate variety distinction and UAV information processing.

375Kg of compound fertilizer (the ratio of nitrogen, phosphorus and potassium is 15-15-15) is applied per hectare for conventional paddy field management. At each development stage of each experiment, a UAV drone was arranged to acquire images of all rice fields, and each field was repeatedly measured 5 times.

3. Data collection

3.1 measurement of Nitrogen content by conventional methods

At each developmental stage, leaf samples were taken for accurate nitrogen and chlorophyll content measurements. 3 functional leaves were counted down from the top xyphoid leaf for measuring chlorophyll content and nitrogen content. The average number of SPADs and the average number of nitrogen contents (N%) were recorded in 3 replicates per breed.

The nitrogen content was measured using a nitrogen measuring instrument N-Pen N110, and three plants were selected at each developmental stage to collect leaf samples for measurement. Leaves with the age of 1.5 leaves (the length is 2 times of the length of the sword leaves) are taken at the stage before the sword leaves grow out, and the second leaf below the sword leaves is taken at the stage after the sword leaves grow out. NDGI ═ (R780-R560)/(R780-R560). Chlorophyll content was measured using a Soil Plant Analytical Development (SPAD) chlorophyll content meter (SPAD-502).

In this study, total nitrogen content values (three replicates) were measured for each variety at each developmental stage, for a total of 306 total nitrogen value data and chlorophyll content data for 51 rice varieties in ozhou, and for a total of 252 total nitrogen value data and chlorophyll content data for 42 rice varieties in the water of the memorial province.

Elemental Quantitative Analysis (EQA) was also used in this study to determine nitrogen content as follows: selecting 3 plants, collecting functional leaves, oven drying at 80 deg.C to constant weight, grinding, sieving with 100 mesh sieve, and detecting nitrogen content. The average of the 3 plant data was used as the accurate leaf nitrogen content value for the corresponding rice variety.

3.2 leaf area index measurement

Leaf Area Index (LAI) was also collected in this study as follows: randomly selected 5 plants were used to measure leaf area index. If more than 50% of the leaves are yellow, the leaves are judged to be yellow and removed. Since this study performed destructive measurements of rice material and multiple growing periods required sampling tests, 2 plants with the most green leaves were selected from the 5 plants described above as representative samples for each rice variety and each growing period. The entire plant of both plants, including all tillers, was rooted. All green leaves were peeled off and scanned for Leaf area (Leaf area meter LI-3100C). The average leaf area of all leaves of these two plants was taken as a representative value of the Leaf Area (LA) of the individual plant. Considering a plant density (d) of 1 square meter, LAI ═ LA × d.

3.3 collecting the diffuse reflectance spectra of the crop canopy

Crop diffuse reflectance spectra were measured by an ASD field spec Pro FR spectrometer. Receipts were collected from 1.0m directly above the crop canopy, selected between 10am-2pm on sunny days, once every 5 days. Each test cell was measured 5 times repeatedly, and the average was taken as the spectral reflectance of the cell canopy. The influence of instrument noise is removed through standard white board paper in time correction, and spectrum data of 1301-2500nm wave band with low signal-to-noise ratio is removed.

3.4 Unmanned Aerial Vehicle (UAV) flight and image acquisition

Images of the target study plot were acquired using the Mini-MCA system installed on the drone (S1000, hayakiang), and images were collected every five days from the time of transplantation until the crop was mature. The Mini-MCA comprises an array of 12 individual miniature digital cameras. 10bit SXGA data can be generated for each sensor channel, and the image resolution can reach 1m/130 hectare. Each camera is equipped with a custom bandpass filter centered at a wavelength of 490,520,550,570,670,680,700,720,800,850,900 or 950nm, respectively. After UAV image acquisition, corresponding field measurements are made in situ immediately.

The MCA system is connected to the UAV through a gimbal frame to prevent the influence of the movement of the UAV, and the inaccurate matching effect of the cameras is controlled through 12 cameras matched before flying. Each UAV flight is performed in sunny, cloudless sky conditions for a time between 10am and 2pm, which is the minimum change in the solar altitude. In the Hubei experiment, the UAV flight height is 50m above the target cell, and the spatial resolution is about 2.7 cm. 42 rice variety experiments, the UAV flight height is 200m above the target cell, and the spatial resolution is about 10.8 cm.

The image digital quantization value (DN) is converted to a surface reflectivity (ρ λ) using an empirical linear correction method. Image radiance correction was performed through a standard of 6 ground calibration targets placed in the camera field of view first on each flight. The cell under study and all ground calibration targets are contained in the same picture. Herein, the ground calibration target provides relatively stable reflectivities of 0.03, 0.12, 0.24, 0.36, 0.56, and 0.80 for visible to near-infrared wavelengths, respectively. Since a linear relationship is assumed between DN and ρ λ, the reflectance equation for a rice variety can be:

p_λ＝DN_λ×Gain_λ+Offset_λ

(λ＝490，520，550，570，670，680，700，720，800，850，900 and 900nm) (1)

where ρ is_λAnd DN_λDigitally quantizes the surface reflectivity at wavelength λ and the image for the given pixel. Gain λ and Offset λ are the camera Gain and Offset of the camera at wavelength λ. Gain_λAnd Offset_λThe p value and DN value may be calculated using a least squares method.

3.5 statistical analysis and Vegetative Index calculation

Data analysis and Statistical description were performed by IBM SPSS Statistics (Statistical Product and Service Solutions 22.0, IBM, Armonk, NY, United States). GraphPad software (Version 5.0., Harvey Motulsky) was used&Arthur Christopoulos, San Diego, California, USA). And (3) carrying out statistical evaluation on the data sets of nitrogen content (N%), chlorophyll content (SPAD) and Leaf Area Index (LAI) according to requirements, and displaying normal distribution. The poisson correlation coefficient (r) is used as a result of the correlation analysis. Analysis and comparison of corrected R²And p-value, regression analysis was performed. The best fit curve is converted to an equation as a regression model to represent the correlation between N%, SPAD, LAI × N% and Normalized Difference Red Edge (NDRE) or other Vegetation Index (VI). The calculation formula of each VI is shown in table 3.

TABLE 3 VI equation of calculation

4. Correlation between several VI and Nitrogen content and chlorophyll

The former collected canopy spectral reflectance data using a spectrometer 1m above the rice and analyzed data for 6 key stages in the growth cycle to determine which growth stage was the best stage for the selected VI for assessment of chlorophyll and nitrogen content.

Overall, all VI showed a stronger correlation with chlorophyll (0.5-0.65) than nitrogen content (0.29-0.49). However, for each VI, its correlation pattern with chlorophyll and nitrogen content is the same: NDRE showed the strongest correlation and NDVI showed the weakest correlation. For chlorophyll correlation, NDGI (R)²＝0.6146)＞CIrededge(R²＝0.5953)＞CIgreen(R²0.5171). For nitrogen content, CIrededge (R)²＝0.4634)＞NDGI(R²＝0.4555)＞CIgreen(R²0.4083). Thus, NDRE is the VI that is optimal for assessing chlorophyll and nitrogen content.

5. NDRE real-time mode can be used for reversibly analyzing growth differences among rice varieties

To analyze the entire growth cycle from the transplant period to the harvest period, 51 rice varieties were planted in rectangular cells (1.2m × 1.6m) and image data were collected using UAVs every 5-7 days (as determined by sunlight conditions). The RGB image (fig. 3A) and NDRE (fig. 3B) show images for 6 epochs. A purple rice variety was included as an internal control in the test group, and since it contained higher anthocyanin than chlorophyll, it was advantageous to distinguish it from other varieties in reflectance characteristics at the time of data processing. NDRE values are between 1 for cool blue 0 and warm red, so warmer colors represent higher chlorophyll content, nitrogen accumulation and photosynthetic rate, and colder colors, in contrast. In the birth cycles of all varieties, from TS period, JS period to PIS period, the NDRE value gradually increases, and rapidly decreases after BS period.

The NDRE values for each growth period for 51 rice varieties ranged as follows: TS stage (0.4121-0.5473), JS stage (0.4555-0.6173), PIS stage (0.3762-0.5762), BS stage (0.3506-0.5394), FHS stage (0.1931-0.4134), MRS stage (0.1487-0.3343). Among them, TS, JS, PIS and BS observed the highest NDRE in rice variety #33(Qingtai Ai), FHS and MRS observed the highest NDRE in rice variety #1(LY 9348). The lowest NDRE was observed in #17(ARC11777, TS), #4(Luohong 4B, JS and PIS), #16(MaMaGu, BS), #7(ZuiHou, FHS) and #28(MoMi, MRS).

High NDRE values above 0.5 were observed for all rice varieties in JS, PIS and BS phases, which are related to rice development, since JS phase is a period during vegetative growth when biomass is rapidly accumulated and PIS/BS phase is a period of transformation from vegetative to reproductive growth, which indicates that leaf and stem growth requires more energy than later flower and seed production. However, the exact maximum NDRE value, the time to reach the maximum NDRE value and the time to fall back from the maximum NDRE value vary widely among 51 rice varieties at the same time or at different times for the same variety. This indicates that chlorophyll content, photosynthetic rate, nitrogen uptake, transport, accumulation and ability to maintain nitrogen levels vary among varieties and at different times throughout the reproductive cycle. Thus, NDRE can be used as a parameter to measure and evaluate the real-time changes in chlorophyll and nitrogen accumulation.

6. Optimization of prediction model and establishment of model I and model II

6.1 creation of model I

To determine why the association of NDRE with chlorophyll and nitrogen varied during different growth periods, scatter plots were drawn on a total of 306 data from 6 growth periods for 51 rice varieties for analysis (fig. 4A and C). After transplanting, rice plants develop from plantlets (40cm high, 5-6 leaves) to large plants (120cm, 16-18 leaves). Growth of biomass and canopy modification is based on accumulation of chlorophyll and nitrogen.

Considering the small biomass, narrow leaves, small plants in the TS phase, the chlorophyll and nitrogen contents may be erroneously overestimated, and the reflectance in this phase is actually a mixed characteristic of the plants themselves and the paddy water. Based on the inference, the data in TS period is removed, the regression model is built again, and the NDRE and the chlorophyll have better correlation R²When 0.8127, NDRE also has a better correlation coefficient R with N%²Above 0.60 (fig. 4B and D). Since N% is a pass elementQuantitative elemental analysis (EQA) method, and we therefore consider a regression model (y 5.754 x)²+8.167x +0.5752) was a predictive model based on actual measurements as model i for further analysis below.

In summary, NDRE has a better correlation with chlorophyll and nitrogen content using only data from the fertile phase (JS phase and beyond) after the canopy has adequately covered the surface of the water for data processing. R of the established NDRE and nitrogen content model²The improvement is remarkable.

6.2, LAI had an effect on the dependence of NDRE on nitrogen content and the establishment of model II.

To determine whether canopy architecture is a key factor affecting the correlation of nitrogen content with NDRE, a training dataset (2017, table 2) of 42 rice varieties was used for analysis. Leaf Area Index (LAI) and nitrogen content (EQA) were measured and NDRE calculated from UAV data. N% LAI was used instead of N% as a parameter for correlation analysis. The nonlinear model y is obtained as 1.05571e^4.5666x(modelⅡ)，R²Is 0.86 (FIG. 6). The correlation of the model is better than that of ModelI. Thus, the above experiments demonstrate that NDRE appears to be strongly correlated with nitrogen content when canopy structures such as LAI are taken into account.

7. Comparison of VI and Nitrogen content of LY9348 in various periods with other Rice varieties

7.1 comparison of NDRE values

By carefully analyzing the change curve of the nitrogen content of LY9348 in the whole breeding cycle and the NDRE value, the NDRE value of LY9348 in FHS and MRS stages is the highest compared with the NDRE value of 51 rice varieties. On the other hand, in vegetative growth period (TS period (0.41-0.55), JS period (0.46-0.62), PIS period (0.38-0.58), BS period (0.35-0.54)), the NDRE value of LY9348 is relatively high but not the highest (TS (0.50), JS (0.56), PIS (0.54) and BS (0.51)).

Although significant nitrogen content differences between LY9348 and other varieties appeared during the scion and grain development stages, earlier stage nitrogen content levels were also worth monitoring. Many previous studies have shown that increasing nitrogen fertilizer during the TS and BS phases is effective in increasing tillering, biomass and photosynthetic products. However, excessive nitrogen application during these periods may produce more inefficient tillers, shallow roots, unhealthy plant morphological structures, and delay the conversion of vegetative to reproductive growth, resulting in reduced yield. In addition, correct application of nitrogen fertilizer during the stages PIS and BS may increase the number of panicles, number of flowers per panicle, fructification rate, and grain, but these yield-related characteristics would decrease if nitrogen fertilizer was over-applied. Thus, from a global evaluation perspective, the high NUE phenotype is not limited to only high nitrogen levels during the reproductive stages (FHS and MRS), which are part of the high NUE phenotype at moderately high levels during vegetative growth and during the vegetative-reproductive transition.

The experiments and analysis show that the NDRE value calculated by the reflectivity data obtained by the remote sensing technology, and the estimated nitrogen content change curve of the rice in the whole growth cycle or part of the growth cycle is the stable character of each rice variety in a specific environment and can be used as the NUE phenotype.

7.2 comparison of Nitrogen content

We further analyzed the data for 6 growth periods for 51 rice varieties. Nitrogen content estimation by EQA showed that LY9348 maintained higher nitrogen content in 51 rice varieties for all 6 periods, but was higher than other rice varieties in MRS period (fig. 7A). Both model i and model ii detected LY9348 to maintain a higher but not the highest nitrogen level during vegetative growth, with the highest nitrogen level during FHS and MRS (fig. 7B and C). However, the nitrogen content change curves of 51 rice varieties TS, JS and PIS obtained by Model I in the period from TS to BS are flatter than that of Model II, so that Model II seems to have better detection sensitivity and accuracy.

Interestingly, we measured the nitrogen content using a nitrogen meter N-pen N110 meter and plotted the nitrogen content variation curve, which shows that although the nitrogen content measured by the nitrogen meter has a high correlation with the nitrogen content measured by EQA (R)²0.68-0.89), but it failed to distinguish LY9348 from other varieties at FHS and MRS. This may be because the saturation of the reflectance signature measured and estimated by a palm nitrogen meter cannot detect nitrogen contents below 2%.

Our experiments also demonstrated that obtaining the height of the reflectivity (50-200m) did not affect the estimation of the nitrogen content. Moreover, in either ModelI or ModelII, we do not differentiate between the leaves and the ear for nitrogen content estimation, and only use the total reflectance characteristics of each variety for a blended estimation of nitrogen content.

It should be noted that although we try to fit the data of modelI and ModelII to the EQA measurement, the measurement of EQA is sampling measurement and there is a systematic error, and modelI and II are macroscopic data estimation results, in fact we cannot completely determine whether the error between the modelI and II estimation results and the true nitrogen content is large or the error between the EQA measurement results and the true nitrogen content is large. This systematic error may be the reason why the EQA method fails to distinguish high NUE LY9348 from the population better than modelI and ModelII.

We conclude from the above experiments that the NDRE value of LY9348 at 6 growth stages or the unique profile of nitrogen content calculated from the NDRE values is a stable trait under the same or similar environmental conditions and therefore can be used for high-throughput screening of high NUE rice varieties, and that the screening method thus obtained is feasible and reliable for high-throughput screening of high NUE varieties from a large number of rice varieties

We screened several possible high NUE rice varieties using the nitrogen content variation curve of LY9348 of this method as a standard, and further experimental verification is underway.

Furthermore, although the examples of the present invention have been described, in part, in the context of rice and NUE, the methods of the present invention are also applicable to other plants (e.g., wheat, corn, etc.) and other nutrient elements (e.g., phosphorus, potassium, etc.).

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

The application of LY9348 in high-throughput screening of high NUE rice varieties is characterized by comprising the step of using nitrogen-related traits of LY9348 as screening standards, and the method comprises the following steps;

s1: acquiring nitrogen-related traits of LY9348 in multiple growth periods under a specific environment;

s2: acquiring nitrogen-related characters of the rice variety to be screened in a corresponding growth period under a corresponding environment;

s3: comparing the nitrogen-related traits of the rice variety to be screened with those of LY9348, and when the nitrogen-related traits of the rice variety to be screened are the same as or higher than those of LY9348, making the rice variety to be screened be a high-NUE rice variety;

the nitrogen-related traits are nitrogen contents of a plurality of growth periods, wherein the plurality of growth periods comprise a tillering period, an elongation period, an ear differentiation period, a booting period, a heading period and a milk stage;

the nitrogen content is a nitrogen content calculated using the NDRE value.