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

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 this project was funded by the National Rice Industry System (CARS-01-07).

technical field

The present invention relates to the field of smart agriculture, more particularly, to the application of LY9348 in high-throughput screening of high-NUE rice varieties.

Background technique

Nitrogen (N) is an indispensable nutrient element for plant growth and plays an important role in photosynthesis, energy conversion, structural composition or biosynthesis. Plants absorb N from the soil and transport it through the vascular system to specific organs to accumulate as nitrogen compounds, and then decompose and migrate to the target organs to participate in various life activities of the plant and maintain the nutrient balance within the plant.

In grain production, the application of high nitrogen fertilizer is one of the key factors affecting yield. However, in recent years, with the increase of nitrogen fertilizer application, the yield of food crops has not increased correspondingly, but has reached a plateau. From 1980 to 2010, my country's nitrogen fertilizer application increased by 512%, while grain production increased by only 65%. Excessive nitrogen application not only increases costs, but also leads to reduced nitrogen use efficiency and nitrogen loss. The overflow of nitrogen fertilizer in the field, into the soil and water, can also cause some serious environmental problems. According to estimates, if nitrogen use efficiency is improved by 1%, the annual cost of fertilizers in the world can be reduced by 2.3 billion US dollars.

Rice is one of the most important food crops in the world, providing food for nearly half of the world's population. China's rice yield is the highest in the world, but the average nitrogen fertilizer application rate in China's rice fields is 180-209 kg/hm ² , much higher than the world average of 105 kg/hm ² , and the actual use efficiency is only about 30-35%. Improving the nitrogen use efficiency (NUE) of rice by optimizing field management can reduce the nitrogen application rate to 150-165 kg/hm ² . However, to reach maximum yield potential (10-15 Mt/hm ² ), most super rice varieties require nitrogen application rates as high as 300 kg/hm ² . Therefore, reducing the nitrogen application rate from the perspective of field management alone cannot fundamentally solve the problem of nitrogen use efficiency. Breeders hope to be able to screen for rice varieties with high NUE to completely solve this problem.

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

Since the uptake and utilization of nitrogen by rice is a complex and comprehensive behavior involving multiple life activities, breeders are currently unable to find a certain gene or group a gene group as a molecular marker of high NUE. Relying on traditional methods to collect rice samples at specific growth stages (such as heading stage or milk maturity stage) to measure nitrogen content to characterize NUE, on the one hand, cannot accurately characterize nitrogen use efficiency, on the other hand, it is time-consuming and labor-intensive, 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 characterizing high NUE traits are needed to be suitable for high-throughput screening.

With the development of remote sensing technology and UAV, as well as the improvement of camera resolution, its combination with remote sensing technology is gradually used in agricultural production and research. Lukas Prey et al. placed a spectrometer about 1 m above the wheat canopy to measure the reflectance of the canopy and to evaluate the corresponding physiological data. Zheng Hengbiao from Nanjing Agricultural University and others used drones with cameras to capture crop images, and used these images as the basis to analyze and evaluate the physiological parameters of crops. correlation. However, these studies have focused on exploring the measurement methodologies for estimating relevant physiological parameters using VI, and no studies have applied the results of these measurement methodologies to explore the description of NUE phenotypes and how to screen high NUE rice varieties.

Therefore, there is a need for a rice cultivar with standard high NUE traits, and selection of high NUE rice cultivars based on the related traits of the cultivar.

SUMMARY OF THE INVENTION

In order to solve the above problems, the team found in the research process that the rice variety LY9348 has the characteristics of high NUE, which is embodied in that it has higher nitrogen uptake efficiency and nitrogen conversion efficiency compared with the male parent, female parent and other rice varieties. , that is, more nitrogen can be accumulated in the body under the condition of low nitrogen fertilizer application, and yield is not affected by low nitrogen fertilizer application.

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

The present invention also provides a method for screening high-NUE rice varieties, including the step of using the nitrogen-related traits of LY9348 as screening criteria.

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

Preferably, the vegetation index is an NDRE value.

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

By using the NDRE value or the nitrogen content calculated from the NDRE value to screen high-NUE rice varieties, we can use drones combined with remote sensing technology to obtain images of the rice planting area, and then calculate the NDRE value based on the image, and further calculate the nitrogen content. The NDRE value or nitrogen content is 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: Obtain the nitrogen-related traits of LY9348 in multiple growth periods in a specific environment;

S2: obtaining nitrogen-related traits in the corresponding growth period of the rice variety to be screened in the corresponding environment;

S3: Comparing the nitrogen-related traits of the rice variety to be screened with the nitrogen-related traits of LY9348, when the nitrogen-related traits of the rice variety to be screened are all the same as or higher than the nitrogen-related traits of LY9348 , the rice variety to be screened is a high NUE rice variety.

The invention discloses that LY9348 has the characteristics of high NUE, and discloses the nitrogen-related traits of LY9348, and a method for high-throughput screening of high-NUE rice varieties by using LY9348 and its nitrogen-related traits. Based on the content of the present invention, those skilled in the art can use unmanned aerial vehicle combined with remote sensing technology to monitor the vegetation index or nitrogen content calculated from the vegetation index in large-scale and high-throughput rice growth process, and then easily screen high NUE Therefore, it can overcome the dependence of high nitrogen fertilizer in the current rice planting field, reduce the cost, and reduce the environmental damage caused by the application of high nitrogen fertilizer. The invention is a new attempt from traditional breeding to modern breeding, and a further exploration to smart agriculture and precision agriculture.

Description of drawings

Figure 1 shows the nitrogen content changes in 6 growth periods of LY9348 and its parents (LH4B, CH9348) and two other rice varieties R8108 and LY8 grown in the same experimental field using different methods.

Figure 2 shows the comparison of agronomic traits between FLY4H and LY9348 in the nitrogen application field test, where A is the number of grains per plant; B is the seed setting rate (%); C is the rice yield per plant (g); D is the NUE (per rice yield in kilograms of nitrogen). n=30, *, **, *** represent P<0.05, 0.01 and 0.001, respectively.

Figure 3 shows the RGB images and NDRE images of 51 rice varieties in 6 growth stages collected by drone, where: A is the RGB images of the 6 growth stages, a is the TS stage, b is the JS stage, and c is the PIS stage, d is BS stage, e is FHS stage, f is MRS stage; B is NDRE image of 6 reproductive stages, a is TS stage, b is JS stage, c is PIS stage, d is BS stage, e is FHS stage period, f is the MRS period.

Figure 4 shows the data relationship of 51 rice varieties in 6 growth stages and the constructed regression model, in which: A is the relationship between SPAD and NDRE in 6 growth stages, n=306; B is 5 growth stages except TS stage Regression model between SPAD and NDRE constructed from period data, R ² >0.81, n=255; C is the relationship between N% and NDRE in 6 growth periods, n=306; B is 5 periods except TS period Regression model between SPAD and NDRE constructed from growth period data, R ² >0.61, n=255.

Figure 5 shows the relationship between SPAD, N% and NDRE in the FHS stage, where: A is the relationship between SPAD and NDRE in all rice varieties (n=51); B is the relationship between SPAD and NDRE in early mature (EM) rice varieties (n=34); C is the relationship between SPAD and NDRE in late-maturing (LM) rice varieties (n=17); D is the relationship between N% and NDRE in all rice varieties (n=51); E is early-maturing ( EM) Relationship between N% and NDRE in rice varieties (n=34); F is the relationship between N% and NDRE in late maturing (LM) rice varieties (n=17). ***p<0.001.

Figure 6 shows the nonlinear regression model between N%*LAI and NDRE constructed from the data of 42 rice varieties grown in Hainan at 6 growth stages.

Figure 7 shows the nitrogen content change curve measured by the EQA method (A) and the NDRE value estimated by the images collected by the UAV and the calculated NDRE value using Model I (B) and Model II (C) for the whole growth cycle of 51 rice varieties, respectively. Comparison of nitrogen content change curves.

Detailed ways

1. Nitrogen utilization characteristics of Luoyou 9348 (LY9348)

LY9348 is a hybrid rice variety bred by the team using the sterile line "Luohong 4B" and the restorer line "Chenghui 9348". It was approved by the Hubei Provincial Crop Variety Approval Committee in 2016, and the variety approval number is Hubei Shenda 2016014.

1.1. Nitrogen content change curve of LY9348

LY9348 and its parents (LH4B, CH9348) and two other rice varieties, R8108 and LY8H, were planted in an experimental field, and plant samples were collected during 6 growth periods of rice plants, respectively. Elemental quantitative analysis (EQA) was used to determine these 5. Changes in nitrogen content of rice cultivars. The six growth stages included: tillering stage (TS), jointing stage (JS), ear differentiation stage (PIS), booting stage (BS), heading stage (FHS) and milk ripening stage (MRS).

The steps of the EQA method are as follows: 3 plants are selected, functional leaves are collected, dried at 80°C to constant weight, ground, passed through a 100-mesh sieve, and the nitrogen content is detected. The average value of the 3 plant data was used as the precise leaf nitrogen content value of the corresponding rice variety.

The results are shown in Fig. 1, LY9348 maintained a higher nitrogen content from the JS phase, and this state continued until the end of the MRS phase. Because both male and female parents showed much lower nitrogen content. This indicated that the higher nitrogen accumulation ability of LY9348 was not inherited from the parents, but resulted from the combination of heterosis. Since the five cultivars were grown in the same fields and received the same water and fertilizer management, the higher nitrogen accumulation in LY9348 was evidenced by its higher nitrogen uptake efficiency (NUpE).

1.2. Field nitrogen application rate tracking test

The field nitrogen application rate tracking experiment was carried out, LY9348 and FLY4(CK) were tested for 4 nitrogen application rates (0kg/ha, 120kg/ha, 180kg/ha and 240kg/ha). It was sown in Lingshui on December 10, 2018, and transplanted in January 2019. Three replicates of each application rate were randomly placed in the field to form 12 experimental plots with a 0.4 m interval between each two plots, and each plot was covered with a 0.4 m film. Each experimental plot is about 30m ² , and it is divided into two halves, one half is planted with LY9348, and the other half is planted with FLY4(CK). 432 plants were planted in each plot, with a hole spacing of 15cm × 18cm, divided into 24 rows with 18 plants in each row. Before transplanting, all experimental plots were applied with basal fertilizers, superphosphate (90 kg/ha P ₂ O ₅ ) and potassium sulfate (180 kg/ha K ₂ O). Urea (N) was applied three times at sowing, tillering and booting stages. After transplanting, each experimental plot was maintained at a water depth of 5 cm. On the 10th day before harvest, drain the water to facilitate harvesting. 30 plants were used to calculate the number of grains per plant, the rate of seed setting per plant, and the grain yield (g) per plant. Grain yield per kg N was estimated based on 100 plants harvested in the central area of each plot and calibrated by subtracting 13.5% of the standard moisture content from the calculation.

The results showed that compared with the control group FLY4H, LY9348 had higher grain number per row (Fig. 2A), seed setting rate per row (Fig. 2B) and yield (Fig. 2C), and this difference was higher in the 0kg/ha N application plot than 120 , 180 and 240kg/ha N application plots were more obvious. Further study found that the application of nitrogen fertilizer did not improve the yield of LY9348. This shows that LY9348 can efficiently absorb and utilize the existing nitrogen in the soil without the need to apply a large amount of nitrogen fertilizer, suggesting that LY9348 is a variety with higher nitrogen use/accumulation efficiency (NUtE), and the yield per kg of nitrogen increases with the amount of nitrogen application. increased and decreased (Fig. 2D).

The above experiments show that LY9348 has high NUpE and NUtE, we can conclude that LY9348 has a high nitrogen use efficiency (NUE) variety (NUE=NUpE x NUtE=rice yield/soil nitrogen application rate). Therefore, we can use LY9348 as a benchmark for high NUE rice varieties, and use this benchmark to screen for high NUE rice varieties.

To this end, we defined the high NUE trait based on remote sensing technology, designed a new screening method according to the definition, and verified the method experimentally.

2. Plant material and planting

50 varieties with higher NUE (indica rice, Australian rice and varieties in between) were selected from the 3000 genome project, plus a purple rice, a total of 51 rice varieties (Table 1), planted in Wuhan, Ezhou, Hubei University Rice Experiment and Research Base (30.3756°N, 114.7448°E). These rice from Ezhou were sown on May 10, 2017 and transplanted on May 31. The rice in Lingshui germinated on December 10, 2017, and was transplanted on January 6, 2018.

Table 1 Variety information of 51 rice varieties

41 indica rice cultivars were selected from Chinese breeding programs, plus one purple rice, for a total of 42 rice cultivars (Table 2), which were planted in the Wuhan University Hybrid Rice Experiment and Research Base in Lingshui, Hainan (18°03′147.1″N, 110°03'34.9"E). Rice was sown on December 10, 2017 and transplanted on January 5, 2018.

Table 2 Variety information of 42 rice varieties

Except for purple rice and LY9348, the above rice cultivars are all rice cultivars with high NUE but not excellent. The planting density of the above rice varieties is 225,000 plants per hectare, and the total growth time is 6 to 7 months, depending on the variety. Each variety is planted with 60 plants, 10 plants in a row, a total of 6 rows, the row spacing is 20cm, and the plant spacing is 16cm. Every 6 lines is empty for 1 line to facilitate the differentiation of varieties and processing of UAV information.

375Kg of compound fertilizer (NPK ratio of 15-15-15) was applied per hectare for conventional paddy field management. In each developmental period of each experiment, a UAV drone was arranged to acquire images of all paddy fields, and each paddy field was repeatedly measured 5 times.

3. Data collection

3.1. Traditional methods to measure nitrogen content

During each developmental stage, leaf samples were collected for precise nitrogen and chlorophyll content determination. Three functional leaves were counted down from the top sword leaf to measure chlorophyll content and nitrogen content. Each variety was replicated in 3 replicates, and the mean SPAD and nitrogen content (N%) were recorded.

Nitrogen content was measured using a nitrogen measuring instrument N-Pen N 110, and leaf samples were collected from three plants at each developmental stage for measurement. At the stage before the sword leaf grows, the leaves at the age of 1.5 (the length is twice that of the sword leaf) are taken, and at the stage after the sword leaf is grown, the second leaf under the sword leaf is obtained. NDGI=(R780-R560)/(R780-R560). Chlorophyll content was measured using a Soil Plant Analysis and Development (SPAD) Chlorophyll Content Meter (SPAD-502).

In this study, the total nitrogen content value of each variety was measured at each developmental stage (three replicates). There were 306 total nitrogen value data and chlorophyll content data for 51 rice varieties in Ezhou, and 252 data for 42 rice varieties in Lingshui. Total nitrogen value data and chlorophyll content data.

In this study, elemental quantitative analysis (EQA) was also used to determine nitrogen content. The method was as follows: 3 plants were selected, functional leaves were collected, dried at 80°C to constant weight, ground, and passed through a 100-mesh sieve to detect nitrogen content. The average value of the 3 plant data was used as the precise leaf nitrogen content value of the corresponding rice variety.

3.2. Leaf area index measurement

Leaf area index (LAI) was also collected in this study by the following method: 5 plants were randomly selected for LAI measurement. If more than 50% of the parts are yellow, the leaves are judged to be yellow leaves and removed. Since the rice material was destructively measured in this study, and multiple growth periods required sampling and testing, the 2 plants with the most green leaves were selected from the above 5 plants as a representative sample for each rice variety and each growth period. The entire plant, including all tillers, of both plants was excavated with roots. All green leaves were stripped and scanned for leaf area calculation (Leaf AreaMeter LI-3100C). The average leaf area of all leaves of the two plants was taken as a representative value of leaf area (LA) per plant. Considering a plant density (d) of 1 square meter, LAI=LA×d.

3.3. Collection of Diffuse Reflectance Spectra of Crop Canopy

Crop diffuse reflectance spectra were measured by ASD FieldSpec Pro FR spectrometer. Collect receipts from 1.0m directly above the crop canopy, choose to collect between 10am-2pm on a sunny day, and collect every 5 days. Five repeated measurements were performed in each experimental plot, and the average was taken as the spectral reflectance of the plot canopy. The influence of instrument noise is removed by standard whiteboard paper in time correction, and the spectral data in the 1301-2500 nm band with low signal-to-noise ratio is removed.

3.4. Unmanned aerial vehicle (UAV) flight and image acquisition

Images of the target research plot were acquired using the Mini-MCA system mounted on a UAV (S1000, DJI), and images were collected every five days from transplanting until the crops matured. The Mini-MCA consists of an array of 12 individual miniature digital cameras. Each sensor channel can generate 10bit SXGA data, and the image resolution can reach 1m/130 hectares. Each camera is equipped with a custom bandpass filter centered at wavelengths 490, 520, 550, 570, 670, 680, 700, 720, 800, 850, 900 or 950nm. Corresponding field measurements were performed in situ immediately after UAV image acquisition.

The MCA system is connected to the UAV through a gimbal to prevent it from being affected by the movement of the UAV, and the misregistration effect of the cameras is controlled by a total of 12 cameras before the flight. Each UAV flight took place under clear, less cloudy sky conditions between 10am and 2pm, which is the minimum change in the sun's altitude angle. In the Ezhou experiment, the UAV flying height was 50m above the target cell, and the spatial resolution was about 2.7cm. For the experiment of 42 rice varieties, the flying height of UAV was 200m above the target plot, and the spatial resolution was about 10.8cm.

The image digital quantization value (DN) was converted into surface reflectance (ρλ) using empirical linear correction method. Image radiometric correction is performed by a standard consisting of 6 ground calibration targets, which are first placed in the camera field of view on each flight. The studied cell and all ground calibration targets are included in the same photo. In this paper, the ground calibration targets provided relatively stable reflectances 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 between DN and ρλ is assumed, the reflectance equation for rice varieties 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 _λ and DN _λ are the surface reflectance at wavelength λ and the image digital quantization value of the specified pixel. Gainλ and Offsetλ are the camera gain and offset of the camera at wavelength λ. Gain _λ and Offset _λ can be calculated according to the ρ value and the DN value by the least square method.

3.5. Statistical analysis and calculation of Vegetative Index

Data analysis and statistical description were performed by IBM SPSS Statistics (Statistical Product and Service Solutions 22.0, IBM, Armonk, NY, United States). Graphs were made using GraphPad software (Version 5.0., Harvey Motulsky & Arthur Christopoulos, San Diego, California, USA). Statistical evaluation of nitrogen content (N%), chlorophyll content (SPAD) and leaf area index (LAI) datasets according to demand showed normal distribution. The Poisson correlation coefficient (r) was used as the result of the correlation analysis. Regression analysis was performed to analyze and compare adjusted R2 and ^p -values. The best fit curves were converted into equations as regression models to represent the correlation between N%, SPAD, LAI*N% and normalized difference red edge (NDRE) or other vegetation indices (VI). The calculation formula of each VI is shown in Table 3.

Table 3 VI calculation formula

4. Correlations between several VIs and nitrogen content and chlorophyll

Canopy spectral reflectance data were collected using a spectrometer 1 m above rice, and data from 6 key stages in the growth cycle were analyzed to determine which growth stage was the most selected VI for assessing chlorophyll and nitrogen content. good stage.

Overall, all VIs showed stronger correlations with chlorophyll (0.5-0.65) than nitrogen content (0.29-0.49). However, for each VI, the correlation patterns with chlorophyll and nitrogen content were the same: NDRE showed the strongest correlation and NDVI the weakest. For chlorophyll correlation, NDGI (R ² =0.6146)>CIrededge (R ² =0.5953)>CIgreen (R ² =0.5171). For nitrogen content, CIrededge (R ² =0.4634)>NDGI (R ² =0.4555)>CIgreen (R ² =0.4083). Therefore, NDRE is the best VI for evaluating chlorophyll and nitrogen content.

5. NDRE real-time mode can reversely analyze the growth differences between rice varieties

In order to analyze the entire growth cycle from the transplanting period to the harvesting period, 51 rice varieties were planted in rectangular plots (1.2m × 1.6m), and image data were collected by trial UAV every 5-7 days (depending on sunlight conditions). RGB images (Fig. 3A) and NDRE (Fig. 3B) show images for 6 epochs. A purple rice variety was included in the test group as an internal control. Since this purple rice variety contains more anthocyanins than chlorophyll, it is beneficial to distinguish it from other varieties in reflection characteristics during data processing. NDRE values are between 0 for cool blues and 1 for warm reds, so, relatively speaking, warmer colors indicate higher chlorophyll content, nitrogen accumulation, and photosynthetic rates, and cooler colors conversely. In the growth cycle of all varieties, the NDRE value gradually increased from TS stage, JS stage to PIS stage, and decreased rapidly after BS stage.

The range of NDRE values of 51 rice varieties at each growth stage is 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 period (0.1487-0.3343). Among them, TS, JS, PIS and BS observed the highest NDRE in rice cultivar #33 (Qingtai Ai), and FHS and MRS observed the highest NDRE in rice cultivar #1 (LY9348). The lowest NDREs were 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 at JS, PIS and BS stages of all rice cultivars, which were related to rice development, because JS stage is the period of rapid biomass accumulation during vegetative growth, and PIS/BS stage is the vegetative growth direction. The transformation stage of reproductive growth, which states that leaf and stem growth requires more energy than subsequent flower and seed production. However, the exact maximum NDRE value, the time of reaching the maximum NDRE value, and the time of falling back from the maximum NDRE value varied widely in the same period among the 51 rice varieties, or in different periods of the same variety. This suggests that chlorophyll content, photosynthetic rate, nitrogen uptake, transport, accumulation, and ability to maintain nitrogen levels vary among these cultivars and at different times throughout the growth cycle. Therefore, NDRE can be used as a parameter to measure and evaluate real-time changes in chlorophyll and nitrogen accumulation.

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

6.1. Establishment of model I

To determine why the correlations of NDRE with chlorophyll and nitrogen varied at different growth stages, scatter plots were drawn for a total of 306 data from 51 rice varieties at 6 growth stages for analysis (Figure 4A and C). After transplantation, rice plants developed from small plants (40 cm high, 5-6 leaves) to large plants (120 cm, 16-18 leaves). Biomass growth and canopy modification are based on chlorophyll and nitrogen accumulation.

Considering the small biomass, narrow leaves, and small plants at the TS stage, the chlorophyll and nitrogen contents may have been falsely overestimated, and the reflectance at this stage is actually a mixed characteristic of the plant itself and the paddy water body. Based on this inference, we removed the TS period data and re-established the regression model, NDRE and chlorophyll had a better correlation R ² =0.8127, NDRE and N% also had a better correlation coefficient R ² above 0.60 (Figure 4B and D ). Since N% is measured by the Elemental Quantitative Analysis (EQA) method, we consider that the regression model (y=5.754x ² +8.167x+0.5752) is a prediction model based on the actual measured value, which is used as model I for the following further analysis.

In conclusion, only the data of the growth period (JS period and later) after the canopy fully covered the water surface was used for data processing, and NDRE had better correlation with chlorophyll and nitrogen content. The R2 of the established ^NDRE versus nitrogen content model was significantly improved.

6.2. LAI has influence on the correlation between NDRE and nitrogen content and the establishment of model II.

To determine whether canopy structure is a key factor affecting the correlation of nitrogen content with NDRE, the analysis was performed using a training dataset of 42 rice cultivars (2017, Table 2). Leaf area index (LAI) and nitrogen content (EQA) were measured and NDRE was calculated from UAV data. Use N%LAI instead of N% as the parameter for correlation analysis. The nonlinear model y = 1.05571e ^4.5666x (model II) was obtained, with an R ² of 0.86 (Fig. 6). This model correlates better than ModelI. Therefore, the above experiments demonstrate that NDRE exhibits a strong correlation with nitrogen content after taking into account the canopy structure such as LAI.

7. Comparison of VI and nitrogen content of LY9348 with other rice varieties in different periods

7.1. Comparison of NDRE values

We carefully analyzed the change curve of nitrogen content and NDRE value of LY9348 in the whole growth cycle and found that compared with the NDRE value of 51 rice varieties, LY9348 had the highest NDRE value in FHS and MRS stages. On the other hand, in the 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 was relatively high but not highest (TS(0.50), JS(0.56), PIS(0.54) and BS(0.51)).

Although the obvious difference in nitrogen content between LY9348 and other cultivars appeared during the period of rice and grain development, nitrogen levels at earlier periods were also worth monitoring. Many previous studies have shown that increasing nitrogen fertilizer during TS and BS periods can effectively increase tiller number, biomass, and photosynthesis. However, excessive nitrogen application during these periods may produce more ineffective tillers, shallow roots, unhealthy plant morphology, and delayed conversion of vegetative to reproductive growth, leading to reduced yields. In addition, correct nitrogen application during the PIS and BS periods can increase the number of stalks, spikelets per stalk, seed setting and grain, but these yield-related traits will decrease if nitrogen is over-applied. Therefore, from a global evaluation point of view, the high NUE phenotype is not limited to high nitrogen levels during reproductive stages (FHS and MRS), but moderately high nitrogen levels during vegetative growth and vegetative-reproductive transitions levels are part of the high NUE phenotype.

The above experiments and analysis show that the NDRE value calculated from the reflectance data obtained by remote sensing technology, the estimated nitrogen content change curve in the entire growth cycle or part of the growth cycle of rice is the stability of each rice variety in a specific environment, which can be as a NUE phenotype.

7.2. Comparison of nitrogen content

We conducted further analysis of the data from 51 rice cultivars at 6 growth stages. Nitrogen content estimation by EQA method showed that, among 51 rice cultivars, LY9348 maintained higher nitrogen content in all 6 periods, but higher than other rice cultivars during the MRS stage (Fig. 7A). Both Model I and Model II detected that LY9348 maintained higher, but not the highest, nitrogen levels during vegetative growth and had the highest nitrogen levels during FHS and MRS (Figure 7B and C). However, the nitrogen content variation curves of 51 rice cultivars from TS, JS, PIS to BS stages obtained by ModelI were flatter than those obtained by Model II, so Model II seemed to have better detection sensitivity and precision.

Interestingly, we used the N-pen N110 meter to detect nitrogen content and plotted the nitrogen content change curve, and the results showed that although the nitrogen content measured by the nitrogen meter had a high correlation with the nitrogen content measured by EQA (R2 was 0.68 ⁾ . -0.89), but it failed to differentiate LY9348 from other varieties during FHS and MRS periods. This may be because the saturation of reflectance features measured and estimated by the handheld nitrogen meter cannot detect nitrogen levels below 2%.

Our experiments also demonstrate that the height (50-200m) at which the reflectance is obtained does not affect the estimation of nitrogen content. Furthermore, neither in Model I nor Model II, we did not distinguish between leaves and stalks for nitrogen content estimation, only the total reflectance feature of each variety was used for mixed estimation of nitrogen content.

It should be noted that although we try to fit the data of model I and Model II with the measurement results of EQA, the measurement method of EQA is sampling measurement, and there are also systematic errors, while Model I and II are the results of macro data calculation. In fact, we It is not entirely certain whether the error between the Model I and II estimates and the true nitrogen content is large, or whether the error between the EQA measurements and the true nitrogen content is large. This systematic error may be the reason why the EQA method failed to distinguish the high NUE LY9348 from the population better than model I and model II.

Based on the above experiments, we can conclude that the NDRE value of LY9348 or the unique change curve composed of nitrogen content calculated from the NDRE value in the six growth periods is a stable trait under the same or similar environmental conditions, therefore, it can be used for high-pass quantitative screening of high-NUE rice varieties, and the resulting screening method is feasible and reliable for high-throughput screening of high-NUE varieties from a large number of rice varieties

Using this method, the nitrogen content change curve of LY9348 was used as a standard to screen several possible high-NUE rice varieties, and further experimental verification is underway.

In addition, although the example section of the present invention has been described around rice and NUE, the method of the present invention can also be adapted to other plants (eg, wheat, corn, etc.) and other nutrients (eg, phosphorus, potassium, etc.) middle.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (1)

1. 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.

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