A method for analyzing maize yield under water stress

Abstract

This invention discloses a method for analyzing maize yield under water stress, belonging to the field of agricultural planting technology. It analyzes the effects of soil water stress on key physiological parameters of maize (net photosynthetic rate A) by setting different irrigation treatments and combining field experiments with model analysis. n Pore ​​conductance g s The study investigated the regulatory effects of photosynthetic parameters, growth indices (plant height, stem diameter, leaf area index, LAI), and yield components (number of grains per ear, 100-grain weight) on yield. Photosynthetic parameters, growth indices, and yield components at the jointing and tasseling stages were measured. Structural equation modeling and variance decomposition analysis were used to quantify the multi-factor interaction pathways of "photosynthesis-growth-yield" under water stress, thereby providing support for the screening of drought-resistant maize varieties and the formulation of precision irrigation management strategies.

Description

Technical Field

[0001] This invention belongs to the field of agricultural planting technology, specifically relating to a method for analyzing maize yield under water stress. Background Technology

[0002] Maize is one of the most important food and feed crops globally, widely cultivated in arid and semi-arid regions. Especially in my country, the planting area of ​​maize has surpassed that of rice and wheat, becoming the largest food crop. Its high and stable yields are crucial for food security and economic development. However, with global climate change and the increasing frequency 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 decline by inhibiting crop photosynthesis, reducing dry matter accumulation, and affecting grain filling rates during the grain-filling stage. In terms of variety improvement, screening for drought-resistant varieties with high water efficiency is considered an important way 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 of yield variation in maize under different drought environments and among different varieties is of great significance for improving maize's drought resistance and water resource utilization efficiency. Summary of the Invention

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

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

[0005] A method for analyzing maize yield under water stress includes the following steps:

[0006] (1) Four different varieties of maize were selected for planting. For each variety of maize, three irrigation levels were set: full irrigation W1 (100% Q), deficit irrigation W2 (75% Q), and deficit irrigation W3 (50% Q). Twelve treatments were constructed and field plot experiments were 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 water to promote seed germination and seedling growth. After emergence, irrigation was carried out according to the set irrigation system.

[0008] (3) At the jointing and tasseling stages of maize, two maize plants with average growth were selected from each experimental plot to measure plant height, stem diameter, leaf area index (LAI), and aboveground biomass. When measuring aboveground biomass, two maize plants with average growth were selected from each experimental plot and divided into three parts: stem, leaf, and fruit. After blanching at 105℃, they were dried in a 75℃ oven until constant weight to obtain the cumulative dry matter (DMA).

[0009] (4) Two maize plants were randomly selected from each experimental plot at the jointing and tasseling stages. At the jointing stage, the uppermost fully expanded leaf was selected; at the tasseling stage, the leaf at the ear position was selected for measurement. Gas exchange parameters were measured using a LI-6800 portable photosynthesis meter, including the leaf net photosynthetic rate (A). n and pore conductance g s ;

[0010] (5) Using the quadrat method, two quadrats were selected in each experimental plot. The length and width of each quadrat were not less than 1m. All ears of corn were harvested, and 5 ears of corn were selected from each quadrat. The ear length, ear diameter, number of kernels per ear (KNP), and weight of 100 kernels (HGW) were measured respectively. The yield of the quadrat was the dry weight of kernels of 24 corn ears, which was converted into the yield per unit area with a moisture content of 14%.

[0011] (6) All experimental data were preprocessed and analyzed. Structural equation modeling and variance decomposition analysis were used to analyze the multi-level regulation mechanism of maize yield formation under water stress.

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

[0013] Furthermore, the irrigation system is based on the crop's water requirement ET. c and effective rainfall P e The difference ET c -P e It is certain that when ET c -P e Irrigation begins when the water level reaches 40mm. The irrigation volume for full irrigation is Q = ET. c -P e The irrigation amounts for the two deficit irrigation treatments were 0.75Q and 0.5Q, respectively; rainfall exceeding 5 mm in a single event is defined as effective rainfall, and the rainfall amount at this point is P. e ;ET cThe calculation process is as follows: Calculate the crop evapotranspiration ET0 for each time period using the formula, and then multiply ET0 by the crop coefficient K for each growth stage. c Get ET c K at different reproductive stages c The results were obtained by calculating and interpolating the growth period based on years of corn mulching and drip irrigation experimental data from the experimental station.

[0014] The formula for calculating ET0 is as follows:

[0015]

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

[0017] Furthermore, the nitrogen, phosphorus, and potassium application rates during corn planting are as follows: N: 250 kg / hm² 2 P2O5: 165 kg / hm 2 K2O: 60 kg / hm 2 Among them, 40% of nitrogen fertilizer and 100% of phosphorus and potassium fertilizer are applied before sowing, and 60% of nitrogen fertilizer is applied as topdressing at each growth stage, according to the ratio of 2:1:1 at the jointing stage, heading stage and grain filling stage. Topdressing and irrigation are carried out at the same time.

[0018] Furthermore, standard automatic weather stations were used to continuously observe rainfall, solar radiation, temperature, relative humidity, and wind speed data during the growing season, with data being automatically recorded every 15 minutes.

[0019] Furthermore, plant height and leaf area were measured using a measuring tape 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 plant height. The length and width of all leaves of the plant were measured separately, and the leaf area was calculated as 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 bottom of the plant, and two measurements were taken at the same position in mutually perpendicular directions. The average value was then used to calculate the stem diameter.

[0020] Furthermore, the synergistic analysis of structural equation modeling and variance decomposition analysis specifically includes:

[0021] Structural equation modeling analysis was performed using the lavaan package on the R Studio platform. First, the leaf net photosynthetic rate A was... n Pore ​​conductance g sLeaf area index (LAI), dry matter accumulation (DMA), plant height, stem diameter, number of grains per spike (KNP), and 100-grain weight (HGW) were used as direct variables affecting yield to test the significance and goodness of the model and pathway. Standardized pathway coefficients were calculated using maximum likelihood estimation, and pathway significance was verified using the bootstrap method. Model goodness was comprehensively evaluated using chi-square degrees of freedom ratio, comparison fit index, root mean square error of approximation, and root mean square of standardized residuals. If the test criteria were not met, the theoretical model was adjusted based on the pathway coefficients of each parameter. Parameters with insignificant pathway coefficients were gradually adjusted, using experience, to become variables that indirectly affect yield through intermediate variables. Variables with insignificant direct and indirect pathway coefficients were eliminated until the model passed the significance and goodness tests. Structural equation model analysis was performed using measurements taken at the jointing and heading stages to compare the effects of physiological and growth parameters on yield at the two growth stages.

[0022] Variance decomposition analysis was used to quantify the independent and synergistic contributions of each variable in the structural equation model to yield variation. Factors were transformed and standardized by Hellinger to eliminate interference from dimensions and nonnormality. Permutation tests were used to verify the significance of effects. To avoid the bias of multicollinearity on the decomposition results, variance inflation factor was used to test the independence of variables. When the synergistic contribution of a variable to other variables could not be quantified, the variable was removed until the results were verified by Monte Carlo simulation to ensure the robustness of the decomposition results and avoid the interference of random errors. Venn plots were then plotted.

[0023] Furthermore, the model fit test criteria are: chi-square degrees of freedom ratio <3, comparison fit index >0.90, approximate root mean square error <0.08, and standardized residual root mean square <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 maize yield by inhibiting photosynthetic capacity and growth indicators was revealed, and the differences in drought resistance among different maize varieties were further clarified, providing support for the screening of drought-resistant maize varieties and the formulation of precision irrigation management strategies.

[0026] 2. This invention reveals the physiological mechanism of drought resistance differentiation in maize varieties and its cascade effect on yield formation, clarifies the changes in growth indicators and physiological parameters of different maize varieties under different irrigation treatments, and reveals the interrelationships between parameters and their comprehensive impact on maize growth. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein:

[0028] Figure 1 This is a graph showing the difference in plant height of different maize varieties under different irrigation treatments at the jointing stage (a) and tasseling stage (b) of this invention.

[0029] Figure 2 This is a graph showing the difference in stem diameter of different maize varieties under different irrigation treatments at the jointing stage (a) and tasseling stage (b) of this invention.

[0030] Figure 3 This is a graph showing the differences in leaf area index (LAI) among different maize varieties under different irrigation treatments at the jointing stage (a) and tasseling stage (b) of this invention.

[0031] Figure 4 This is a graph showing the differences in aboveground dry matter weight of different maize varieties under different irrigation treatments at the jointing stage (a) and tasseling stage (b) of this invention.

[0032] Figure 5 This is a graph showing the differences in gas exchange parameters of different maize varieties under different irrigation treatments at the jointing stage (a, b) and tasseling stage (c, d) of this invention.

[0033] Figure 6 This is a diagram illustrating the maize yield regulation path analysis based on structural equation modeling, as presented in this invention.

[0034] Figure 7 This is a quantitative analysis diagram of the contribution of factors affecting maize yield based on variance decomposition analysis in this invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0036] This invention is achieved through the following technical solution:

[0037] The experiment was conducted from April to September 2024 at the Shiyanghe Experimental Station of China Agricultural University (37°51'N, 102°52'E, altitude 1581m), located in Wuwei City, Gansu Province. This region has a typical inland arid desert climate with abundant light and heat resources, including over 3000 hours of sunshine annually, an average frost-free period of 154 days, an average annual accumulated temperature of 3550℃ (baseline temperature 0℃), an average annual air temperature of 8℃, and an average annual wind speed of 1.3m / s. The average annual precipitation is 212.2mm, the surface evaporation is approximately 2000mm, and the groundwater level is below 25m. The field water holding capacity and soil bulk density are 34.11% and 1.58g / cm³, respectively. 3 .

[0038] Using maize (Zea mays L.) as the research object, four maize varieties widely planted in Northwest China were selected: Wugu 738 (P1), Jindan 73 (P2), Xianyu 335 (P3), and Denghai 605 (P4). Three irrigation levels were set up: 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 field plots, with three plots for each treatment randomly arranged, for a total of 36 plots, each plot measuring 4m × 8m. Maize plants were planted in a north-south direction within the experimental plots. Drip irrigation tape was laid between two rows, with a tape spacing of 80cm, a dripper spacing of 30cm, a row spacing of 40cm, and a plant spacing of 27.5cm. Drip irrigation under surface film was used, with a planting density of 6000 plants / mu.

[0039] Irrigation regime: After corn sowing, to promote seed germination and seedling growth, each experimental plot was irrigated with 40 mm of water for emergence. After emergence, irrigation was carried out according to the designed irrigation regime. The irrigation regime was based on the crop's water requirement ET. c and effective rainfall P e The difference (ET) c -P e ) Determine, when ET c -P e Irrigation begins when the water level reaches 40mm. The irrigation volume for full irrigation is Q = ET. c -P e The irrigation amounts for the two deficit irrigation treatments were 0.75Q and 0.5Q, respectively. Rainfall exceeding 5 mm in a single event is considered effective rainfall, and the effective rainfall amount is P. e ET c The calculation process is as follows: Calculate the crop evapotranspiration ET0 for each time period using the formula, and then multiply ET0 by the crop coefficient K for each growth stage. c Get ET c K at different reproductive stages cThe ET0 calculation formula is obtained by calculating and interpolating the growth period based on years of corn mulching and drip irrigation experimental data from the experimental station.

[0040]

[0041] In the formula: ET0 is the reference crop water requirement (mm d) -1 ); Δ is the slope (kPa℃) of the curve relating saturated vapor pressure and temperature. -1 ); R n Net radiation above the canopy (MJ m -2 d -1 G represents soil heat flux (MJ / m³). -2 d -1 ); γ is the hygrometer constant (kPa℃) -1 T is the daily average temperature at a height of 2m (°C); u2 is the wind speed at a height of 2m (m / s). -1 );e s The saturated vapor pressure (kPa); e a This represents the actual water vapor pressure (kPa).

[0042] The application rates of nitrogen, phosphorus, and potassium are as follows: N: 250 kg / hm² 2 P2O5: 165 kg / hm 2 K2O: 60 kg / hm 2 Of these, 40% of the nitrogen fertilizer and 100% of the phosphorus and potassium fertilizers are applied before sowing, and 60% of the nitrogen fertilizer (150 kg / hm²) is applied at the same time. 2 Topdressing should be applied at each growth stage, with a ratio of 2:1:1 for the jointing stage, heading stage, and grain-filling stage. Irrigation system should be based on ET... c It is certain that when ET c Irrigation begins when the soil reaches 40mm in diameter. Topdressing and irrigation are carried out simultaneously. The amount of irrigation and fertilizer applied during each growth stage is shown in the table. A total of 8 irrigations and 3 topdressings are performed.

[0043]

[0044] Table 1. Statistics on irrigation and fertilization time, irrigation volume, and nitrogen application during the growth period;

[0045] The standard automatic weather station was used to continuously observe data such as rainfall, solar radiation, temperature, relative humidity, and wind speed during the growing season, and the data was automatically recorded every 15 minutes.

[0046] During the jointing and tasseling stages, two maize plants of 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 1mm precision measuring tape. The height from the ground at the base of the plant to its highest point was recorded as plant height. The length and width of all leaves were measured, and the leaf area was calculated as leaf length × leaf width × 0.75. Stem diameter was measured using a 0.01mm precision vernier caliper, with the measurement point close to the bottom of the plant. Two measurements were taken at the same location in mutually perpendicular directions, and the average value was taken as the stem diameter. For aboveground biomass determination, two maize plants of uniform growth were selected from each experimental plot and divided into three parts: stem, leaves, and fruit. After blanching at 105℃, the plants were dried in a 75℃ oven until constant weight, and the cumulative dry matter was obtained.

[0047] The net photosynthetic rate (A) of leaves was measured using a LI-6800 portable photosynthesis system (Li-CORInc., Lincoln, NE, USA) at the jointing stage (June 18) and tasseling stage (July 15). n and pore conductance g s Gas exchange parameters. Indoor environmental conditions for red and blue light source: light intensity 1800 μmol / m². -2 s -1 CO2 concentration 400 μmol -1 The temperature was 25℃ and the humidity was around 50-60%. Two plants were randomly selected from each plot. The uppermost fully expanded leaf was selected during the jointing stage, and the leaf at the ear position was selected during the heading stage for measurement.

[0048] Using the quadrat method, each quadrat was 1.6m × 1.65m. Two quadrats were selected from each plot to harvest all ears of grain. Five representative ears of grain were selected from each quadrat, and the ear length, ear diameter, number of grains per ear, and weight of 100 grains were measured. The yield of the quadrat was the dry weight of all ears of grain, which was converted into the yield per unit area with a moisture content of 14%.

[0049] Experimental data were preprocessed and subjected to basic statistical analysis using Microsoft Office Excel 2021. Duncan's multiple comparison method was used for significance testing.

[0050] Modeling and analysis were performed using the RStudio platform in conjunction with the lavaan and vegan packages. A combined structural equation modeling (SEM) and variance decomposition analysis (VPA) approach was employed to analyze the multi-level regulatory mechanisms of maize yield formation.

[0051] SEM analysis was performed using the lavaan package on the RStudio platform. First, photosynthetic parameters (A, g), growth indices (LAI, DMA, plant height, stem diameter), and yield components (grain number per ear KNP, 100-grain weight HGW) were used as direct variables affecting yield to test the significance and goodness of the model. Standardized path coefficients were calculated using maximum likelihood estimation (ML), and path significance was verified using the bootstrap method (1000 repeated samplings) (α = 0.05). Model goodness was assessed using the chi-square degrees of freedom ratio (χ²). 2 The fitness metric is comprehensively evaluated using the following methods: χ² (data point), Comparison Index of Fit (CFI), Root Mean Square Error (RMSEA), and Root Mean Square Error of Standardized Residue (SRMR). 2 / df < 3, CFI > 0.90, RMSEA < 0.08, SRMR < 0.05. If the test criteria are not met, the theoretical model is adjusted based on the path coefficients of each parameter. Parameters with insignificant path coefficients are gradually adjusted, using experience, to become variables that indirectly affect yield through intermediate variables (with significant path coefficients). Variables with insignificant direct and indirect path coefficients are eliminated until the model passes the significance and goodness-of-fit tests. SEM analysis is performed using measurements taken at the jointing and heading stages to compare the effects of physiological and growth parameters on yield at the two growth stages.

[0052] VPA was used to further analyze the independent and synergistic contributions of each variable included in the SEM model to yield variation. First, the factors were subjected to Hellinger transformation and standardization to eliminate interference from dimensions and nonnormality, and the significance of the effects was verified using a permutation test. To avoid bias in the decomposition results due to multicollinearity, the variance inflation factor (VIF) was used to test the independence of variables (threshold VIF < 5). If the synergistic contribution of a variable to other variables could not be quantified, that variable was removed until validated by Monte Carlo simulation, ensuring the robustness of the decomposition results, avoiding interference from random errors, and Venn plots were drawn.

[0053] Results and Analysis:

[0054] 1. Maize yield and its components

[0055] Table 2 shows the yield changes of different maize varieties under irrigation treatments. Two-way ANOVA showed that the main effect of maize yield among varieties was not significant, but the main effect of water and the interaction effect between varieties and water were significant. Under treatment W1, the yields of P1, P2, P3, and P4 were 17.80 t / ha, 17.59 t / ha, 18.55 t / ha, and 18.69 t / ha, respectively. Treatment W2 significantly reduced the maize yield of the four varieties by 12.6%, 8.0%, 13.6%, and 17.2%, respectively. Treatment W3 significantly reduced the maize yield by 27.8%, 24.9%, 27.0%, and 27.1%, respectively, all of which were significantly greater than those in treatment W2.

[0056] The number of kernels per ear of maize is directly related to the length and diameter of the ear. As shown in Table 2, the length and diameter of maize ears did not vary significantly among different varieties, but differed significantly under different water treatments, and the responses of different varieties to water treatments varied. Under treatment W2, the ear lengths of P1, P2, P3, and P4 decreased significantly by 15.2%, 10.8%, 15.9%, and 18.2%, respectively, while the ear diameters did not decrease significantly. Under treatment W3, the ear lengths of the four varieties decreased significantly by 36.7%, 26.6%, 26.8%, and 23.7%, respectively, while only the ear diameters of P1 and P3 decreased significantly by 8.1% and 10.0%, respectively.

[0057] Table 2 shows the results of yield components 100-kernel weight and number of kernels per ear. The 100-kernel weight and number of kernels per ear showed no significant variation among different maize varieties, but significant differences were observed under different moisture treatments. Treatment W2 significantly reduced the 100-kernel weight of P4; treatment W3 significantly reduced the 100-kernel weight of each variety by 16.8%, 14.8%, 19.7%, and 14.6%, respectively. Under treatment W2, except for P2, the number of kernels per ear of P1, P3, and P4 significantly decreased by 15.6%, 18.9%, and 20.0%, respectively; under treatment W3, these numbers significantly decreased by 36.0%, 33.5%, 32.7%, and 34.6%, respectively.

[0058]

[0059] Table 2. Statistics on Output Factors and Output Composition

[0060] Wherein: Data in the table are expressed as mean ± standard deviation. Different uppercase letters in the same column indicate significant differences between different varieties under the same moisture treatment (P<0.05), and different lowercase letters indicate significant differences between different moisture treatments within the same variety (P<0.05).

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

[0062] Corn plant height at the jointing stage ( Figure 1a) Significant differences were observed in water treatment and among varieties, with the heading date ( Figure 1 b) Significant differences were found only among water treatments; no interaction effects were observed between water treatments and varieties at either growth stage. At the jointing stage, under the W2 treatment, only P4 showed a significant decrease of 12.3%. Under the W3 treatment, except for P3, P1, P2, and P4 decreased by 4.4%, 3.3%, and 4.2%, respectively. At the heading stage, the effect of deficit irrigation on plant height was further aggravated: compared to W1, under the W2 treatment, except for P1 which showed no significant decrease, the plant height of other varieties decreased by 3.3%–4.4%. However, under the W3 treatment, the plant height of all varieties decreased significantly, with decreases ranging from 4.2% to 6.0%.

[0063] During the jointing stage ( Figure 2 (a) Stem diameter showed significant differences between varieties and water treatments. Under the W2 treatment, only P2 and P3 showed a significant decrease in stem diameter of 8.0% and 8.4%, respectively; under the W3 treatment, only P2 and P3 showed a significant decrease in stem diameter of 9.0% and 8.9%, respectively. At the heading stage ( Figure 2 (b) Stem diameter increased significantly in all treatments compared to the jointing stage. However, the differences among water treatments decreased at the heading stage: under W3 conditions, only P4 showed a significant decrease in stem diameter of 14.8%.

[0064] Changes in leaf area index (LAI) during the jointing and heading stages are as follows: Figure 3 As shown, during the jointing stage ( Figure 3 (a) Only P2 and P4 showed significant reductions of 23.9% and 8.9%, respectively. Under the W3 treatment, only P1 and P2 showed significant reductions of 8.3% and 27.7%, respectively. P2 had the highest LAI under the W1 treatment; at the heading stage ( Figure 3 (b) Under the W3 treatment, the LAI of P1 and P2 decreased by 15.1% and 17.7%, respectively. The LAI of P2 was the highest under the W1 treatment at both the jointing and heading stages.

[0065] During the jointing stage ( Figure 4 a) P4 showed the highest aboveground dry matter weight under the W1 treatment, but this was significantly reduced by 18.2% under the W3 treatment. At the heading stage ( Figure 4 (b) There were no significant differences in aboveground dry matter among varieties, but significant differences were observed among moisture treatments. Under treatment W2, only P3 showed a significant decrease in aboveground dry matter of 20.6%; under treatment W3, only P1 and P3 showed significant decreases in aboveground dry matter of 16.5% and 20.3%, respectively.

[0066] 3. Gas exchange parameters of maize leaves

[0067] Jointing stage ( Figure 5 a) and b) The effects of moisture and variety on A n and g sAll effects were significant, and the interaction effect between the two was significant. The water treatment effect was as follows: Under W2 treatment, except for P4, the A values ​​of P1, P2, and P3 were significantly different. n and g s All decreased significantly, A n They decreased by 29.0%, 22.3%, and 20.9% respectively, g s The levels decreased by 38.4%, 24.2%, and 34.6%, respectively. Under the W3 treatment, the A levels of the four varieties... n and g s The significant decreases compared to W1 were 27.7%-36.5% and 31.6%-47.1%, respectively. Variety differences were observed: under W1, A in P1 and P3... n and g s Significantly higher than P2 and P4; at W2, A of P3 n and g s Significantly higher than P2; under W3, A of P1 n and g s Significantly higher than P4.

[0068] During the heading stage ( Figure 5 c, d) Only water content affects A n and g s The effect was significant, and the interaction effect between moisture and variety was not significant. Specifically, under the W2 treatment, only P4 showed an effect on A. n and g s Significantly reduced by 29.3% and 45.2%; under W3 treatment, A in all four varieties... n Compared with W1, they decreased significantly by 39.1%, 38.4%, 34.6%, and 32.4%, respectively; G s They decreased by 47.8%, 44.4%, 51.1%, and 35.7%, respectively.

[0069] 4. Structural Equation Modeling (SEM) and Variance Decomposition Analysis (VPA)-based Analysis of Maize Yield Formation Paths

[0070] The SEMs constructed when photosynthetic physiology and crop growth parameters at the jointing stage were used as yield influencing factors all failed the test. Figure 6 Showing heading period A n The direct and indirect effects of indicators such as DMA, LAI, ear grain number, and 100-grain weight on yield were investigated, quantifying the multi-level regulatory mechanism of maize yield formation. The results showed that A n By driving DMA to indirectly affect the number of grains per ear (β=0.733→0.519→0.647, total effect 24.6%), the number of grains per ear was the most sensitive factor for yield (β=0.647, p<0.001), contributing 64.7%, forming "A nThe core pathway is "dry matter weight → number of grains per ear → yield". LAI has a positive effect on the number of grains per ear (β = 0.276, p = 0.004), but a slight inhibitory effect on 100-grain weight (β = -0.162, p = 0.035), reflecting resource competition in grain development. All model fit indices met the excellent standard (χ²). 2 The results (df = 2.07, CFI = 0.929, RMSEA = 0.053, SRMR = 0.041) indicate that the theoretical framework is effective and the data fits well.

[0071] Combination Figure 7 VPA analysis showed that yield variation was decomposed into four traits: number of grains per ear, dry matter weight, LAI (latitude index), and 100-grain weight, including independent contributions, synergistic effects (multi-trait interactions), and residuals. Collinearity tests confirmed the independence of variables (maximum VIF = 4.2), indicating reliable results. The synergistic effect of the four traits contributed 38.2% (p < 0.001), significantly higher than independent contributions. The residuals were 0.311, including yield variation caused by unquantified genetic factors, environmental factors, and management practices.

[0072] This invention, through gradient water stress experiments and multi-model analysis, reveals the physiological mechanism of drought resistance differentiation in maize varieties in arid Northwest China and its cascade effect on yield formation. It elucidates the changes in growth indicators and physiological parameters of different maize varieties under different irrigation treatments, and reveals the interrelationships between parameters and their comprehensive impact on maize growth. The results show that there are significant differences in the physiological responses of different maize varieties under drought stress, thus providing support for the screening of drought-resistant maize varieties and the formulation of precision irrigation management strategies.

[0073] The above description constitutes an embodiment of the present invention. The foregoing descriptions are preferred embodiments of the present invention. Unless there is a clear contradiction or a prerequisite for a particular preferred embodiment, the preferred embodiments can be arbitrarily combined and used. The embodiments and specific parameters described are merely for clearly illustrating 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 determined by its claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention should also be included within the scope of protection of the present invention.

Claims

1. A method for analyzing maize yield under water stress, characterized in that, Includes the following steps: (1) Select multiple different varieties of maize for planting. For each variety of maize, three irrigation levels were set: full irrigation W1 with 100%Q, deficit irrigation W2 with 75%Q, and deficit irrigation W3 with 50%Q. Q is the irrigation amount for full irrigation treatment. Twelve treatment methods were constructed and field plot experiments were conducted. Three experimental plots were set up for each treatment method and randomly arranged. (2) After corn sowing, each experimental plot was irrigated with 40 mm of water to promote seed germination and seedling growth. After emergence, irrigation was carried out according to the set irrigation system. (3) At the jointing and tasseling stages of maize, two maize plants with average growth were selected from each experimental plot to measure plant height, stem diameter, leaf area index (LAI), and aboveground biomass. When measuring aboveground biomass, two maize plants with average growth were selected from each experimental plot and divided into three parts: stem, leaf, and fruit. After blanching at 105 ℃, they were dried in a 75 ℃ oven until constant weight to obtain the cumulative dry matter (DMA). (4) Two maize plants were randomly selected from each experimental plot at the jointing and tasseling stages. At the jointing stage, the uppermost fully expanded leaf was selected, and at the tasseling stage, the leaf at the ear position was selected for measurement. Gas exchange parameters were measured using a LI-6800 portable photosynthesis meter, including the net photosynthetic rate of the leaf. A n and pore conductance g s ; (5) Using the quadrat method, select two quadrats in each experimental plot. The length and width of each quadrat should not be less than 1 m. Harvest all ears of corn and select 5 ears of corn to measure ear length, ear diameter, number of kernels per ear (KNP), and weight of 100 kernels (HGW). The yield of the quadrat is the dry weight of corn kernels in the quadrat, which is converted into the yield per unit area with a moisture content of 14%. (6) All experimental data were preprocessed and analyzed. Structural equation modeling and variance decomposition analysis were used to analyze the multi-level regulation mechanism of maize yield formation under water stress. The synergistic analysis of structural equation modeling and variance decomposition analysis is as follows: Structural equation modeling analysis was performed using the lavaan package on the R Studio platform. First, the net photosynthetic rate of the leaves was calculated. A n Pore ​​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 goodness of the model and pathway. Standardized pathway coefficients were calculated using maximum likelihood estimation, and pathway significance was verified using the bootstrap method. Model goodness was comprehensively evaluated using chi-square degrees of freedom ratio, comparison fit index, root mean square error of approximation, and root mean square of standardized residuals. When the test criteria were not met, the theoretical model was adjusted based on the pathway coefficients of each parameter. Parameters with insignificant pathway coefficients were gradually adjusted, combined with experience, to become variables that indirectly affect yield through intermediate variables. Variables with insignificant direct and indirect pathway coefficients were eliminated until the model passed the significance and goodness tests. Structural equation modeling analysis was conducted using the results measured at the jointing and heading stages to compare the effects of physiological and growth parameters on yield at the two growth stages. Variance decomposition analysis was used to quantify the independent and synergistic contributions of each variable in the structural equation model to yield variation. Factors were transformed and standardized by Hellinger to eliminate interference from dimensions and nonnormality. Permutation tests were used to verify the significance of effects. To avoid the bias of multicollinearity on the decomposition results, variance inflation factor was used to test the independence of variables. When the synergistic contribution of a variable to other variables could not be quantified, the variable was removed until the results were verified by Monte Carlo simulation to ensure the robustness of the decomposition results and avoid the interference of random errors. Venn plots were then plotted.

2. The method for analyzing maize yield under water stress according to claim 1, characterized in that, In step (1), four corn varieties were selected for the experiment. The four corn varieties were "Wugu 738", "Jindan 73", "Xianyu 335" and "Denghai 605". The experimental plot area was 4 m × 8 m. The corn plants in the experimental plot were planted in a north-south direction. The drip irrigation tape was laid in the middle of the two rows with a drip irrigation tape spacing of 80 cm, a dripper spacing of 30 cm, a crop row spacing of 40 cm, and a plant spacing of 27.5 cm. The drip irrigation method under the surface film was adopted, and the planting density was 6000 plants / mu.

3. A method for analyzing maize yield under water stress according to claim 1, characterized in that, 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 It is certain that when ET c - P e Irrigation begins when the water level reaches 40 mm. The irrigation volume for full irrigation is Q = ET. c - P e The irrigation amounts for the two deficit irrigation treatments were 0.75Q and 0.5Q, respectively; rainfall exceeding 5 mm in a single event is defined as effective rainfall, and the rainfall amount at this point is... P e ;ET c The calculation process is as follows: Calculate the crop evapotranspiration ET0 for each time period using the formula, and then multiply ET0 by the crop coefficient for each growth stage. K c Get ET c Different reproductive stages K c The results were obtained by calculating and interpolating the growth period based on years of corn mulching and drip irrigation experimental data from the experimental station. The formula for calculating ET0 is as follows: ； Where ET0 is the reference crop water requirement; Δ is the slope of the curve relating saturated water vapor pressure and temperature; R n Net radiation above the canopy; G γ is the soil heat flux; γ is the hygrometer constant. T The average daily temperature at an altitude of 2 m; 2 represents the wind speed at a distance of 2 m; It is the saturated vapor pressure; This is the actual water vapor pressure.

4. A method for analyzing maize yield under water stress according to claim 1, characterized in that, The recommended nitrogen, phosphorus, and potassium application rates for corn cultivation are: N: 250 kg / hm² 2 P2O5: 165 kg / hm 2 K2O: 60 kg / hm 2 Among them, 40% of nitrogen fertilizer and 100% of phosphorus and potassium fertilizer are applied before sowing, and 60% of nitrogen fertilizer is applied as topdressing at each growth stage, according to the ratio of 2:1:1 at the jointing stage, heading stage and grain filling stage. Topdressing and irrigation are carried out at the same time.

5. A method for analyzing maize yield under water stress according to claim 3, characterized in that, The standard automatic weather station was used to continuously observe rainfall, solar radiation, temperature, relative humidity and wind speed during the growing season, and the data was automatically recorded every 15 minutes.

6. A method for analyzing maize yield under water stress according to claim 1, characterized in that, In step (3), plant height and leaf area are measured using a measuring tape with an accuracy of 1 mm. The height from the ground at the base of the plant to the highest point of growth is recorded as plant height. The length and width of all leaves of the plant are measured separately. The leaf area is calculated as leaf length × leaf width × 0.

75. 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 same position is measured twice in mutually perpendicular directions, and the average value is used to obtain the stem diameter.

7. A method for analyzing maize yield under water stress according to claim 1, characterized in that, The model fit test criteria are: chi-square degrees of freedom ratio <3, comparison fit index >0.90, approximate root mean square error <0.08, and standardized residual root mean square <0.05.

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