Corn yield analysis method under water stress

Abstract

The invention discloses a method for analyzing the corn yield under water stress, and belongs to the technical field of agricultural planting. Regulation and control effects of soil moisture stress on key physiological parameters (net photosynthetic rate An and stomatal conductance gs) of the corn, growth indexes (plant height, stem diameter and leaf area index LAI) and yield constituent elements (kernel number per ear and weight per hundred kernels) of the corn on the yield are analyzed. Photosynthetic parameters, growth indexes and yield composition in the elongation stage and the heading stage are measured, a structural equation model and variance decomposition are adopted to analyze and quantify a photosynthesis-growth-yield multi-factor interaction path under water stress, and then support is provided for screening of drought-resistant corn varieties and formulation of a precise irrigation management strategy.

Description

Technical Field

[0001] The invention belongs to the technical field of agricultural planting, and particularly relates to a method for analyzing corn yield under water stress. Background Art

[0002] Maize is one of the most important food and feed crops worldwide, widely cultivated in arid and semi-arid regions. In my country, in particular, maize acreage has surpassed rice and wheat to become the leading food crop. Its high and stable yields are crucial for food security and economic development. However, with global climate change and the frequent occurrence of extreme weather events, drought has become a major limiting factor affecting maize growth, development, and yield formation. Studies have shown that drought directly leads to yield reduction by inhibiting crop photosynthesis, reducing dry matter accumulation, and affecting grain filling rate during the grain filling period. In terms of cultivar improvement, selecting drought-tolerant varieties with high water efficiency is considered an important approach to mitigate yield decline. However, the physiological mechanisms driving yield formation in different maize varieties under deficit irrigation conditions remain unclear. Therefore, clarifying the physiological mechanisms underlying yield variation in maize under different drought environments and across varieties is crucial for improving maize drought resistance and water use efficiency. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for analyzing corn yield under water stress, so as to analyze the regulatory effect of soil water stress on corn yield components and key physiological indicators, and then provide support for the screening of drought-resistant corn varieties and the formulation of precision irrigation management strategies.

[0004] The technical solution adopted in the present invention is as follows:

[0005] A method for analyzing corn yield under water stress comprises the following steps:

[0006] (1) Four different varieties of corn were selected for planting. Three irrigation levels were set for each corn: full irrigation W1 (100% Q), deficit irrigation W2 (75% Q), and deficit irrigation W3 (50% Q). Twelve treatments were constructed and a field plot experiment was conducted. Three experimental plots were set up for each treatment and randomly arranged.

[0007] (2) After corn sowing, each experimental plot was irrigated with 40 mm of emergence water to promote seed germination and seedling growth. After emergence, irrigation was continued according to the set irrigation schedule.

[0008] (3) At the jointing and heading stages of corn, two corn plants with average growth were selected from each experimental plot to measure plant height, stem diameter, leaf area index (LAI), and aboveground biomass. For aboveground biomass determination, two corn plants with average growth were selected from each experimental plot and divided into stem, leaf, and fruit parts. After being dried at 105°C, they were placed in a 75°C oven to constant weight to obtain the dry matter accumulation (DMA).

[0009] (4) Two corn plants were randomly selected from each experimental plot during the jointing and heading stages. The top fully expanded leaves were selected during the jointing stage, and the leaves at the ear position were selected during the heading stage for measurement. The gas exchange parameters were measured using the LI-6800 portable photosynthetic meter. The gas exchange parameters included the net photosynthetic rate A of the leaves. n and stomatal conductance g s ;

[0010] (5) Using the sampling method, two plots were selected in each experimental plot, each plot was not less than 1 m in length and width, all ears were harvested, and 5 ears were selected from each plot to measure ear length, ear diameter, number of kernels per ear (KNP), and 100-kernel weight (HGW). The yield of the plot was the dry weight of kernels in 24 corn ears, converted into a yield per unit area with a moisture content of 14%;

[0011] (6) All experimental data were preprocessed and analyzed, and the structural equation model and variance decomposition analysis were used to collaboratively analyze the multi-level regulatory mechanism of corn yield formation under water stress.

[0012] Furthermore, in the step (1), four corn varieties were selected for testing, namely: "Wugu 738", "Jindan 73", "Xianyu 335" and "Denghai 605". The area of ​​the test plot was 4m×8m. The corn plants in the test plot were planted in a north-south direction, and drip irrigation tapes were laid between two rows. The spacing between the drip irrigation tapes was 80cm, the spacing between the drippers was 30cm, the crop row spacing was 40cm, and the plant spacing was 27.5cm. Drip irrigation under surface film was adopted, and the planting density was 6000 plants / mu.

[0013] Furthermore, the irrigation schedule is based on the crop water requirement ET c and effective rainfall P e The difference ET c -P e OK, when ET c -P e When the water reaches 40mm, start irrigation. The amount of water for full irrigation treatment is Q=ET c -P e The irrigation amounts of the two deficit irrigation treatments were 0.75Q and 0.5Q respectively; the rainfall amount of a single rainfall > 5mm is called effective rainfall, and the rainfall amount at this time is P e ET cThe calculation process is as follows: Calculate the parameter crop evapotranspiration ET0 in each period according to the formula, ET0 multiplied by the crop coefficient K of each growth stage c Get ET c , K in different growth stages c The data were calculated based on the experimental station's multi-year corn film-mulching drip irrigation test data and the growth period interpolation.

[0014] The ET0 calculation formula is as follows:

[0015]

[0016] Where ET0 is the reference crop water requirement; Δ is the slope of the saturated water vapor pressure vs. temperature curve; R n is the net radiation above the canopy; G is the soil heat flux; γ is the hygrometer constant; T is the daily average temperature at 2 m; u2 is the wind speed at 2 m (; e s is the saturated water vapor pressure; e a is the actual water vapor pressure.

[0017] Furthermore, the nitrogen, phosphorus and potassium dosages for corn planting are: N: 250kg / hm 2 , P2O5: 165kg / hm 2 , K2O: 60kg / hm 2 Among them, 40% nitrogen fertilizer and 100% phosphorus fertilizer and potassium fertilizer are applied before sowing, and 60% nitrogen fertilizer is applied as topdressing during each growth period, according to the ratio of 2:1:1 during the jointing stage, heading stage and filling stage. Topdressing and irrigation treatment are carried out at the same time.

[0018] Furthermore, a standard automatic weather station was used to continuously observe the rainfall, solar radiation, temperature, relative humidity, and wind speed data during the growth period, and the data were automatically recorded every 15 minutes.

[0019] Furthermore, plant height and leaf area were measured using a tape measure with an accuracy of 1 mm. The height from the ground at the root of the plant to the highest growth point was recorded as the plant height. The length and width of all leaves of the plant were measured separately. The leaf area was leaf length × leaf width × 0.75. The stem diameter was measured using a vernier caliper with an accuracy of 0.01 mm. The measurement position was close to the bottom of the plant. Two measurements were made at the same position in perpendicular directions. The average value was used to obtain the stem diameter.

[0020] Furthermore, the structural equation model and variance decomposition analysis were analyzed in detail as follows:

[0021] Structural equation model analysis was performed using the lavaan package in R Studio. First, the net photosynthetic rate of leaves A n , stomatal conductance g s, leaf area index LAI, dry matter accumulation DMA, plant height, stem diameter, number of grains per ear KNP, and 100-grain weight HGW were used as direct variables affecting yield to test the significance and fitness of the model and path; the standardized path coefficient was calculated using the maximum likelihood estimation method, the path significance was verified using the Bootstrap method, and the model fitness was comprehensively evaluated using the chi-square freedom ratio, comparative fit index, approximate root mean square error, and standardized residual root mean square. When the test standard was not met, the theoretical model was adjusted according to the path coefficient of each parameter, and the parameters with insignificant path coefficients were gradually adjusted to variables that had an indirect effect on yield through intermediate variables based on experience, and the variables with insignificant direct and indirect path coefficients were eliminated until the model passed the significance and fitness test. The measurement results of the jointing stage and the heading stage were used to perform structural equation model analysis to compare the effects of physiological and growth parameters of the two growth periods on yield;

[0022] Variance decomposition analysis was used to quantify the independent and synergistic contributions of each variable in the selected structural equation model to yield variation. The factors were transformed and standardized by Hellinger to eliminate dimensionality and non-normality interference, and the permutation test was used to verify the significance of the effect. To avoid the bias of multicollinearity on the decomposition results, the variance inflation factor was used to test the independence of variables. When the synergistic contribution between a variable and other variables could not be quantified, the variable was eliminated until it was verified by Monte Carlo simulation to ensure the robustness of the decomposition results and avoid random error interference, and a Venn diagram was drawn.

[0023] Furthermore, the model fit test criteria were: chi-square degrees of freedom ratio <3, comparative fit index >0.90, root mean square error of approximation <0.08, and root mean square of standardized residual <0.05.

[0024] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0025] 1. In this invention, by analyzing the relationship between photosynthetic parameters, growth indicators and yield, the mechanism by which drought stress limits corn yield by inhibiting photosynthetic capacity and growth indicators is revealed, and the differences in drought resistance of different corn varieties are further clarified, providing support for the screening of drought-resistant corn varieties and the formulation of precision irrigation management strategies.

[0026] 2. The present invention reveals the physiological mechanism of drought resistance differentiation in corn varieties and its cascade effect on yield formation, clarifies the changes in growth indicators and physiological parameters of different corn varieties under different irrigation treatments, and reveals the relationship between parameters and their comprehensive impact on corn growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings, wherein:

[0028] Figure 1 Figure for the difference of plant height of different corn varieties under different irrigation treatments in jointing stage (a) and heading stage (b) of the present application;

[0029] Figure 2 Figure for the difference of stem diameter of different corn varieties under different irrigation treatments in jointing stage (a) and heading stage (b) of the present application;

[0030] Figure 3 Figure for the difference of leaf area index LAI of different corn varieties under different irrigation treatments in jointing stage (a) and heading stage (b) of the present application;

[0031] Figure 4 Figure for the difference of above-ground dry matter of different corn varieties under different irrigation treatments in jointing stage (a) and heading stage (b) of the present application;

[0032] Figure 5 Figure for the difference of gas exchange parameters of different corn varieties under different irrigation treatments in jointing stage (a, b) and heading stage (c, d) of the present application;

[0033] Figure 6 Figure for the analysis of corn yield regulation path based on structural equation model of the present application;

[0034] Figure 7 Figure for the contribution quantification analysis of corn yield influencing factors based on variance decomposition analysis of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0036] The present application is realized by the following technical solutions:

[0037] The experiment was carried out from April to September 2024 at the Shiyang River Experimental Station of China Agricultural University in Wuwei City, Gansu Province (37°51'N, 102°52'E, 1581m above sea level). The region has a typical inland arid desert climate with rich light and heat resources. The annual sunshine hours are more than 3000h, the frost-free period is an average of 154d, the average annual total accumulated temperature is 3550℃ (the reference temperature is 0℃), the average annual temperature is 8℃, and the average annual wind speed is 1.3m / s. The average annual precipitation is 212.2mm, the evaporation of the water surface is about 2000mm, and the groundwater level is below 25m. The field water holding capacity and soil bulk density are 34.11% and 1.58g / cm respectively. 3 .

[0038] Maize (Zea mays L.) was studied, with four widely cultivated maize varieties in Northwest China selected: Wugu 738 (P1), Jindan 73 (P2), Xianyu 335 (P3), and Denghai 605 (P4). Three irrigation levels were applied: full irrigation (W1) (100% Q), deficit irrigation (W2) (75% Q), and deficit irrigation (W3) (50% Q), for a total of 12 treatments. The experiment was conducted in a field plot system, with three randomly arranged plots per treatment, for a total of 36 plots measuring 4 m × 8 m. Maize plants were planted in a north-south orientation, with drip tapes laid between rows, 80 cm apart, 30 cm between emitters, 40 cm between rows, and 27.5 cm between plants. Drip irrigation was applied under a surface film, with a planting density of 6,000 plants per mu.

[0039] Irrigation system: After corn sowing, in order to promote seed germination and seedling growth, each experimental plot was irrigated with 40mm of water for emergence. After emergence, irrigation was carried out according to the designed irrigation system. The irrigation system was based on the crop water requirement ET c and effective rainfall P e The difference (ET c -P e ) OK, when ET c -P e When the water reaches 40mm, start irrigation. The amount of water for full irrigation treatment is Q=ET c -P e The irrigation amounts of the two deficit irrigation treatments were 0.75Q and 0.5Q respectively. A single rainfall amount > 5mm is called effective rainfall, and the rainfall amount at this time is P e ET c The calculation process is as follows: Calculate the parameter crop evapotranspiration ET0 in each period according to the formula, ET0 multiplied by the crop coefficient K of each growth stage c Get ET c , K in different growth stages cThe ET0 calculation formula is as follows:

[0040]

[0041] Where: ET0 is the reference crop water requirement (mm d -1 ); Δ is the slope of the curve of saturated water vapor pressure and temperature (kPa℃ -1 );R n is the net radiation above the canopy (MJ m -2 d -1 ); G is soil heat flux (MJ m -2 d -1 ); γ is the hygrometer constant (kPa℃ -1 ); T is the daily average temperature at 2m height (℃); u2 is the wind speed at 2m height (ms -1 );e s is the saturated water vapor pressure (kPa); e a is the actual water vapor pressure (kPa).

[0042] The dosage of nitrogen, phosphorus and potassium is: N: 250kg / hm 2 , P2O5: 165kg / hm 2 , K2O: 60kg / hm 2 , of which 40% nitrogen fertilizer and 100% phosphorus fertilizer and potassium fertilizer were applied before sowing, 60% nitrogen fertilizer (150kg / hm 2 ) Topdressing should be applied at each growth stage in the ratio of 2:1:1 during jointing, heading and filling stages. Irrigation system is based on ET c OK, when ET c Start irrigation when the thickness reaches 40mm, and carry out topdressing and irrigation at the same time. The amount of irrigation and fertilizer application in each growth period is shown in the table. A total of 8 irrigations and 3 topdressings are carried out.

[0043]

[0044] Table 1 Statistics of irrigation and fertilization time, irrigation amount and nitrogen application amount during the growth period;

[0045] A standard automatic weather station was used to continuously observe rainfall, solar radiation, temperature, relative humidity, wind speed and other data during the growth period, and the data were automatically recorded every 15 minutes.

[0046] During the jointing and heading stages, two corn plants with average growth were selected from each experimental plot, and their plant height, stem diameter, and leaf area were measured. Plant height and leaf area were measured using a tape measure with an accuracy of 1 mm. The height from the ground at the base of the plant to the highest point of growth was recorded as the plant height. The length and width of all leaves of the plant were measured separately. Leaf area = leaf length × leaf width × 0.75. Stem diameter was measured using a vernier caliper with an accuracy of 0.01 mm. The measurement position was close to the base of the plant. Two measurements were taken at the same position in mutually perpendicular directions. The average value was the stem diameter of the plant. For the aboveground biomass determination, two corn plants with average growth were selected from each experimental plot. They were divided into three parts: stem, leaf, and fruit. After being sterilized at 105°C, they were placed in a 75°C oven and dried to constant weight to obtain the cumulative dry matter.

[0047] The net photosynthetic rate (A) of leaves was measured at the jointing stage (June 18) and the heading stage (July 15) of corn using a LI-6800 portable photosynthetic meter (Li-CORI Inc., Lincoln, NE, USA). n and stomatal conductance g s The indoor environmental conditions of the red and blue light sources were set as follows: light intensity 1800 μmol m -2 s -1 , CO2 concentration 400 μmol mol -1 , temperature 25℃, humidity 50-60%. Two plants were randomly selected from each plot, and the top fully expanded leaves were selected at the jointing stage and the leaves at the ear position were selected at the heading stage for measurement.

[0048] The sampling method was adopted, with each plot measuring 1.6m×1.65m. Two plots were selected from each community to harvest all the ears. Five representative ears were selected from each plot to measure the ear length, ear diameter, number of grains per ear, and 100-grain weight. The plot yield was the dry weight of all the grains in the ears, converted into a yield per unit area with a moisture content of 14%.

[0049] The experimental data were preprocessed and analyzed using Microsoft Office Excel 2021. Duncan's multiple comparison method was used for significance testing.

[0050] Modeling and analysis were completed using the RStudio platform combined with the lavaan and vegan packages. A collaborative analysis of the structural equation model (SEM) and variance analysis (VPA) was used to analyze the multi-level regulatory mechanisms of corn yield formation.

[0051] SEM analysis was performed using the lavaan package in RStudio. First, photosynthetic parameters (A, g), growth indicators (LAI, DMA, plant height, stem diameter), and yield components (number of grains per spike KNP, 100-grain weight HGW) were used as direct variables affecting yield to test the significance and fitness of the model. Standardized path coefficients were calculated using the maximum likelihood estimation (ML) method, and path significance (α = 0.05) was verified using the Bootstrap method (1000 repeated samplings). Model fitness was assessed using the chi-squared freedom ratio (χ 2 / df), comparative fit index (CFI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) comprehensive evaluation, the goodness of fit standard χ 2 The requirements for df were / <3, CFI>0.90, RMSEA<0.08, and SRMR<0.05. When these criteria were not met, the theoretical model was adjusted based on the path coefficients of each parameter. Parameters with insignificant path coefficients were gradually adjusted empirically to variables that indirectly affected yield through intermediate variables (with significant path coefficients). Variables with insignificant direct and indirect path coefficients were eliminated until the model passed the significance and goodness of fit tests. SEM analysis was performed using measurement results from the jointing and heading stages to compare the effects of physiological and growth parameters on yield during the two growth stages.

[0052] VPA was used to further analyze the independent and synergistic contributions of each variable in the selected SEM model to yield variation. Factors were first transformed and standardized using Hellinger transformation to eliminate dimensionality and nonnormality, and permutation tests were used to verify effect significance. To avoid bias in the decomposition results due to multicollinearity, the variance inflation factor (VIF) was used to test variable independence (threshold VIF < 5). If the synergistic contribution of a variable with other variables could not be quantified, the variable was removed until Monte Carlo simulation was verified to ensure the robustness of the decomposition results and avoid random error interference. A Venn diagram was then drawn.

[0053] Results and Analysis:

[0054] 1. Corn yield and its components

[0055] Table 2 is the yield change results of different corn varieties under irrigation treatment, two-factor variance analysis shows that the main effect of corn yield is not significant between varieties, but the main effect of water and the interaction effect of variety and water are significant. Under W1 treatment, the yield of P1, P2, P3 and P4 is 17.80 t / ha, 17.59 t / ha, 18.55 t / ha and 18.69 t / ha respectively; W2 treatment leads to a significant decrease of 12.6%, 8.0%, 13.6% and 17.2% in the yield of the four varieties respectively; W3 treatment leads to a significant decrease of 27.8%, 24.9%, 27.0% and 27.1% in the yield of the four varieties, which is significantly higher than that of W2 treatment.

[0056] The number of corn ear grains is directly related to the length and diameter of the ear. As shown in Table 2, the length and diameter of the ear of different varieties of corn have no significant variation between varieties, but have significant differences under different water treatments, and different varieties have different responses to water treatment. Under W2 treatment, the ear length of P1, P2, P3 and P4 is significantly reduced by 15.2%, 10.8%, 15.9% and 18.2% respectively, and the ear diameter has no significant reduction; under W3 treatment, the ear length of the four varieties is significantly reduced by 36.7%, 26.6%, 26.8% and 23.7% respectively, and only the ear diameter of P1 and P3 is significantly reduced by 8.1% and 10.0%.

[0057] Table 2 is the result of yield component hundred-grain weight and ear grain number, the hundred-grain weight and ear grain number of different varieties of corn have no significant variation between varieties, but have significant differences under different water treatments. W2 treatment only significantly reduces the hundred-grain weight of P4; W3 treatment leads to a significant reduction of 16.8%, 14.8%, 19.7% and 14.6% in the hundred-grain weight of each variety respectively. Under W2 treatment, the ear grain number of P1, P3 and P4 is significantly reduced by 15.6%, 18.9% and 20.0% respectively, except for P2; under W3 treatment, it is significantly reduced by 36.0%, 33.5%, 32.7% and 34.6% respectively.

[0058]

[0059] Table 2 Yield Element and Yield Component Statistics

[0060] Among them, the data in the table are expressed as mean ± standard deviation. Different capital letters in the same column indicate significant differences (P<0.05) between different varieties under the same water treatment, and different small letters indicate significant differences (P<0.05) between different water treatments within the same variety.

[0061] 2. Corn plant growth and dry matter accumulation

[0062] The height of corn plant at the jointing stage (V6) is 1.05 m, 1.06 m, 1.07 m and 1.08 m respectively, and the height of P1, P2, P3 and P4 is significantly reduced by 8.7%, 7.5%, 7.4% and 7.1% respectively under W3 treatment. Figure 1a) Water treatment and variety were significantly different, and the heading stage Figure 1 b) Only water treatment was significantly different, and there was no interaction effect between water treatment and variety at two growth stages. At the jointing stage, only P4 was significantly reduced by 12.3% under W2 treatment, and P1, P2 and P4 were reduced by 4.4%, 3.3% and 4.2% respectively under W3 treatment. At the heading stage, the effect of deficit irrigation on plant height was further intensified: compared with W1, the plant height of the rest varieties was reduced by 3.3%-4.4% under W2 treatment, and the plant height of each variety was significantly reduced by 4.2%-6.0% under W3 treatment.

[0063] At the jointing stage ( Figure 2 (a)), stem diameter showed significant differences between varieties and water treatments. Under W2 treatment, only P2 and P3 stem diameters were significantly reduced by 8.0% and 8.4%, respectively; under W3 treatment, only P2 and P3 stem diameters were significantly reduced by 9.0% and 8.9%, respectively. Figure 2 (b)), the stem diameter of each treatment was significantly increased compared with the jointing stage. However, the difference between water treatments decreased at the heading stage: under W3 condition, only P4 stem diameter was significantly reduced by 14.8%.

[0064] The changes of leaf area index (LAI) at the jointing stage and the heading stage are shown in Figure 3 At the jointing stage ( Figure 3 (a)), only P2 and P4 were significantly reduced by 23.9% and 8.9%, respectively, and under W3 treatment, only P1 and P2 were significantly reduced by 8.3% and 27.7%, respectively. P2 had the largest LAI under W1 treatment; at the heading stage ( Figure 3 (b)), under W3 treatment, the LAI of P1 and P2 was reduced by 15.1% and 17.7%, respectively. The LAI of P2 was the largest under W1 treatment at both the jointing stage and the heading stage.

[0065] At the jointing stage ( Figure 4 a), P4 had the highest aboveground dry matter weight under W1 treatment, but it was significantly reduced by 18.2% under W3 treatment. At the heading stage ( Figure 4 b), the difference in aboveground dry matter weight between varieties was not significant, but the difference between water treatments was significant. Only P3 had significantly reduced aboveground dry matter weight by 20.6% under W2 treatment; only P1 and P3 had significantly reduced aboveground dry matter weight by 16.5% and 20.3%, respectively, under W3 treatment.

[0066] 3. Corn leaf gas exchange parameters

[0067] At the jointing stage ( Figure 5 a, b), water and variety had significant effects on A n and g sThe effects of water treatment were significant, and the interaction effect between the two was significant. The water treatment effect was as follows: under the W2 treatment, except for P4, the A n and g s Both were significantly reduced, A n The decrease was 29.0%, 22.3% and 20.9%, respectively. s Under W3 treatment, the A n and g s Compared with W1, the range of decrease was 27.7%-36.5% and 31.6%-47.1% respectively. Variety differences were as follows: Under W1, the A n and g s Significantly higher than P2 and P4; under W2, P3's A n and g s Significantly higher than P2; under W3, P1's A n and g s Significantly higher than P4.

[0068] At the heading stage ( Figure 5 c, d), only water has an effect on A n and g s The effect was significant, and the interaction effect between water and variety was not significant. Specifically, under the W2 treatment, only the A n and g s The A of the four varieties decreased significantly by 29.3% and 45.2% under the W3 treatment. n Compared with W1, the rates of G s They decreased by 47.8%, 44.4%, 51.1% and 35.7% respectively.

[0069] 4. Structural Equation Model (SEM) and Variance Profile Analysis (VPA)-Based Path Analysis of Corn Yield

[0070] The SEMs constructed when photosynthetic physiology and crop growth parameters at the jointing stage were used as factors affecting yield did not pass the test. Figure 6 Shows the heading period A n The direct and indirect effects of the indicators such as DMA, LAI, number of grains per ear and 100-grain weight on yield were quantified to quantify the multi-level regulatory mechanism of corn yield. n The grain number per ear was indirectly affected by driving DMA (β=0.733→0.519→0.647, total effect 24.6%). The grain number per ear was the most sensitive factor for yield (β=0.647, p<0.001), with a contribution rate of 64.7%, forming the "A nThe core path of the model is "→ dry matter mass → number of grains per spike → yield". LAI has a positive effect on number of grains per spike (β = 0.276, p = 0.004), but has a weak inhibitory effect on 100-grain weight (β = -0.162, p = 0.035), reflecting resource competition for grain development. The model fitness indexes all reached the excellent standard (χ 2 / df=2.07, CFI=0.929, RMSEA=0.053, SRMR=0.041), indicating that the theoretical framework is valid and the data fit is good.

[0071] Combine Figure 7 VPA analysis showed that yield variation was decomposed into four traits: kernel number per spike, dry matter, LAI, and 100-grain weight, including independent contributions, synergistic effects (multi-trait interactions), and residual variance. Collinearity tests confirmed variable independence (maximum VIF = 4.2), indicating reliable results. The synergistic effect of the four traits contributed 38.2% (p < 0.001), significantly higher than the independent contributions. The residual variance was 0.311, reflecting unquantified genetic, environmental, and management factors contributing to yield variation.

[0072] Through gradient water stress experiments and multi-model analysis, this paper reveals the physiological mechanism of drought resistance differentiation of corn varieties in the arid areas of Northwest China and its cascade effect on yield formation, clarifies the changes in growth indicators and physiological parameters of different corn varieties under different irrigation treatments, and reveals the relationship between parameters and their comprehensive impact on corn growth. The results show that there are significant differences in the physiological responses of different corn varieties under drought stress, which provides support for the screening of drought-resistant corn varieties and the formulation of precise irrigation management strategies.

[0073] The above are the embodiments of the present invention. The foregoing are the preferred embodiments of the present invention. If the preferred implementation methods in each preferred embodiment are not obviously self-contradictory or based on a certain preferred implementation method, each preferred implementation method can be arbitrarily superimposed and used in combination. The embodiments and the specific parameters in the embodiments are only for the purpose of clearly describing the verification process of the invention, and are not intended to limit the scope of patent protection of the present invention. The scope of patent protection of the present invention is still subject to its claims. Any equivalent structural changes made by using the contents of the description and drawings of the present invention should also be included in the scope of protection of the present invention.

Claims

1. A method for analyzing corn yield under water stress, characterized in that: The following steps are involved: (1) Several different varieties of corn were selected for planting. Three irrigation levels were set for each corn: full irrigation W1 (100% Q), deficit irrigation W2 (75% Q), and deficit irrigation W3 (50% Q). Twelve treatments were constructed and a field plot experiment was conducted. Three experimental plots were set up for each treatment and randomly arranged. (2) After corn sowing, each experimental plot was irrigated with 40 mm of emergence water to promote seed germination and seedling growth. After emergence, irrigation was continued according to the set irrigation schedule. (3) At the jointing and heading stages of corn, two corn plants with average growth were selected from each experimental plot to measure plant height, stem diameter, leaf area index (LAI), and aboveground biomass. For aboveground biomass determination, two corn plants with average growth were selected from each experimental plot and divided into stem, leaf, and fruit parts. After being dried at 105°C, they were placed in a 75°C oven to constant weight to obtain the dry matter accumulation (DMA). (4) Two corn plants were randomly selected from each experimental plot during the jointing and heading stages. The top fully expanded leaves were selected during the jointing stage, and the leaves at the ear position were selected during the heading stage for measurement. The gas exchange parameters were measured using the LI-6800 portable photosynthetic meter. The gas exchange parameters included the net photosynthetic rate A of the leaves. n and stomatal conductance g s ; (5) Using the sampling method, two quadrats were selected in each experimental plot, with each quadrat being no less than 1 m in length and width. All ears were harvested, and 5 ears were selected from them to measure ear length, ear diameter, number of kernels per ear (KNP), and 100-kernel weight (HGW). The yield of the quadrats was the dry weight of kernels in the corn ears in the quadrats, converted into yield per unit area at a moisture content of 14%; (6) All experimental data were preprocessed and analyzed, and the structural equation model and variance decomposition analysis were used to collaboratively analyze the multi-level regulatory mechanism of corn yield formation under water stress.

2. The method for analyzing corn yield under water stress according to claim 1, wherein: In the step (1), four corn varieties are selected for testing, namely: "Wugu 738", "Jindan 73", "Xianyu 335", and "Denghai 605". The area of ​​the test plot is 4m×8m. The corn plants in the test plot are planted in a north-south direction. The drip irrigation tape is laid between two rows. The spacing between the drip irrigation tapes is 80cm, the spacing between the drippers is 30cm, the crop row spacing is 40cm, and the plant spacing is 27.5cm. The drip irrigation method under the surface film is adopted, and the planting density is 6000 plants / mu.

3. The method for analyzing corn yield under water stress according to claim 1, wherein: In step (2), the irrigation system is based on the crop water requirement ET c and effective rainfall P e The difference ET c -P e OK, when ET c -P e When the water reaches 40mm, start irrigation. The amount of water for full irrigation treatment is Q=ET c -P e The irrigation amounts of the two deficit irrigation treatments were 0.75Q and 0.5Q respectively; the rainfall amount of a single rainfall > 5mm is called effective rainfall, and the rainfall amount at this time is P e ET c The calculation process is as follows: Calculate the parameter crop evapotranspiration ET0 in each period according to the formula, ET0 multiplied by the crop coefficient K of each growth stage c Get ET c , K in different growth stages c The data were calculated based on the experimental station's multi-year corn film-mulching drip irrigation test data and the growth period interpolation. The ET0 calculation formula is as follows: Where ET0 is the reference crop water requirement; Δ is the slope of the saturated water vapor pressure vs. temperature curve; R n is the net radiation above the canopy; G is the soil heat flux; γ is the hygrometer constant; T is the daily average temperature at 2m height; u2 is the wind speed at 2m; e s is the saturated water vapor pressure; e a is the actual water vapor pressure.

4. The method for analyzing corn yield under water stress according to claim 1, wherein: The nitrogen, phosphorus and potassium dosages for corn planting are: N: 250kg / hm 2 , P2O5: 165kg / hm 2 , K2O: 60kg / hm 2 Among them, 40% nitrogen fertilizer and 100% phosphorus fertilizer and potassium fertilizer are applied before sowing, and 60% nitrogen fertilizer is applied as topdressing during each growth period, according to the ratio of 2:1:1 during the jointing stage, heading stage and filling stage. Topdressing and irrigation treatment are carried out at the same time.

5. The method for analyzing corn yield under water stress according to claim 3, wherein: A standard automatic weather station was used to continuously observe rainfall, solar radiation, temperature, relative humidity, and wind speed during the growth period, and the data were automatically recorded every 15 minutes.

6. The method for analyzing corn yield under water stress according to claim 1, wherein: In the step (3), the plant height and leaf area are measured using a tape measure with an accuracy of 1 mm. The height from the ground at the root of the plant to the highest growth point is recorded as the plant height. The length and width of all leaves of the plant are measured respectively. The leaf area is leaf length × leaf width × 0.

75. The stem diameter is measured using a vernier caliper with an accuracy of 0.01 mm. The measurement position is close to the bottom of the plant. The measurement is performed twice at the same position in mutually perpendicular directions. The average value is used to obtain the stem diameter.

7. The method for analyzing corn yield under water stress according to claim 1, wherein: In step (6), the collaborative analysis of the structural equation model and variance decomposition analysis is specifically as follows: Structural equation model analysis was performed using the lavaan package in the RStudio platform. First, the net photosynthetic rate of leaves A n , stomatal conductance g s , leaf area index LAI, dry matter accumulation DMA, plant height, stem diameter, number of grains per ear KNP, and 100-grain weight HGW were used as direct variables affecting yield to test the significance and fitness of the model and path; the standardized path coefficient was calculated using the maximum likelihood estimation method, the path significance was verified using the Bootstrap method, and the model fitness was comprehensively evaluated using the chi-square degree of freedom ratio, comparative fit index, approximate root mean square error, and standardized residual root mean square; when the test standard was not met, the theoretical model was adjusted according to the path coefficient of each parameter, and the parameters with insignificant path coefficients were gradually adjusted to variables that had an indirect effect on yield through intermediate variables based on experience, and the variables with insignificant direct and indirect path coefficients were eliminated until the model passed the significance and fitness tests; The results of the measurements at the jointing stage and heading stage were used to conduct structural equation model analysis to compare the effects of physiological and growth parameters at the two growth stages on yield. Variance decomposition analysis was used to quantify the independent and synergistic contributions of each variable in the selected structural equation model to yield variation. The factors were transformed and standardized by Hellinger to eliminate dimensionality and non-normality interference, and the permutation test was used to verify the significance of the effect. To avoid the bias of multicollinearity on the decomposition results, the variance inflation factor was used to test the independence of variables. When the synergistic contribution between a variable and other variables could not be quantified, the variable was eliminated until it was verified by Monte Carlo simulation to ensure the robustness of the decomposition results and avoid random error interference, and a Venn diagram was drawn.

8. The method for analyzing corn yield under water stress according to claim 7, wherein: The criteria for testing the goodness of fit of the model were: chi-square degrees of freedom ratio <3, comparative fit index >0.90, root mean square error of approximation <0.08, and root mean square of standardized residual <0.05.

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