Cotton irrigation optimization method based on water utilization rate and yield

Abstract

The invention belongs to the field of cotton planting, and provides a cotton irrigation optimization method based on water utilization rate and yield. Comprising the steps of collecting biomass and seed cotton yield, calculating a harvest index, setting a 5TE sensor, collecting apparent dielectric constants, calculating soil water content, calculating grid total water content, counting accumulated soil water consumption, integrating comprehensive cotton data, obtaining a fitting model, obtaining a characteristic curve and analyzing the characteristic curve. By collecting and analyzing the water utilization rate and yield data of the cotton under different densities, screening of the optimal planting density is achieved.

Description

Technical Field

[0001] The invention relates to the field of cotton planting, and in particular to a cotton irrigation optimization method based on water utilization rate and yield. Background Art

[0002] Climate change, especially warming and changes in rainfall patterns, has led to increasingly frequent regional water shortages, posing a huge challenge to global agricultural production. Water resources, as a vital component of agricultural productivity, are greatly affected. Drought and water shortage may make crops more sensitive to climate change, thereby suppressing future yields. Compared with humid areas, the total annual irrigation water use in arid areas has increased more rapidly. With the increase in irrigation volume and the expansion of irrigated areas, the serious water shortage problem in the region and even the world has been seriously aggravated. How to scientifically and efficiently manage agricultural water resources to enable agricultural production to adapt to climate change has become an ongoing challenge. Cotton, as one of the most important economic crops in the world, is extremely sensitive to climate change. Most cotton-producing areas in the world face climate risks such as drought, while irrigation water resources in most planting areas are becoming increasingly scarce, and competition for water resources is escalating, resulting in a decline in cotton productivity. Soil moisture conditions affect cotton root activity, underground biomass, and cotton water consumption. There are differences in water use at different depths in cotton populations at different densities, which is closely related to yield. However, whether water consumption differences directly affect yield differences has not been reported. In-depth understanding of the relevant laws shows that, in theory, the stability of cotton yield can be achieved by precisely controlling water consumption. In addition, the effect of soil water consumption on cotton yield may be mainly reflected in the flowering and bolling stage. Previous studies have shown that in the flowering and bolling stage of cotton under different planting patterns, the vertical movement of soil moisture in the cotton row can increase water consumption and thus increase yield, while the horizontal movement between rows has a negative impact on yield. This theory helps to optimize agronomic measures, but the relationship between water consumption at the horizontal position of the cotton row and yield under different density treatments needs to be further understood. In-depth understanding of the water relationship of plants is an essential basis for implementing sustainable agricultural water management, and is also of great significance for improving crop models. Accurate understanding of the allocation of water resources in the field requires in-depth research on the water resource competition mechanism among plants. The productivity and harvest index of crops are closely related to the intensity of internal competition in crops, among which internal competition in crops is essentially a density effect. Moderately increasing density can increase yield, but as plant density increases, plants will face more intense competition for light and growth space, or underground roots will intensify competition for soil water.

[0003] At present, the understanding of the mechanism of crop water competition is still insufficient. One of the key problems is that the accuracy of soil moisture sampling is not high enough. In the past, rainfall was often used to estimate crop water consumption, but this method has limitations. More in-depth and detailed research will help to accurately reveal the mechanism of water resource competition among crops, thereby more accurately guiding the practice of agricultural water resource management. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a cotton irrigation optimization method based on water utilization rate and yield, by collecting and analyzing cotton water utilization rate and yield data under different densities, to achieve the screening of the optimal planting density.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A cotton irrigation optimization method based on water utilization efficiency and yield, comprising:

[0007] Collect biomass, seed cotton yield, and meteorological data for cotton fields at at least one planting density;

[0008] Calculating a harvest index using the biomass and the seed cotton yield;

[0009] Based on the spatial grid method, at least one 5TE sensor is set in the cotton planting field;

[0010] Using the 5TE sensor to collect the apparent dielectric constant of the cotton planting site;

[0011] Calculating the soil moisture content of the cotton planting site using the apparent dielectric constant;

[0012] Based on Simpson's 3 / 8 rule, calculate the total water content of each preset grid;

[0013] Calculating cumulative soil water consumption using the total water content of the grid;

[0014] The meteorological data, the biomass, the seed cotton yield, the harvest index, the total water content of the grid and the cumulative soil water consumption are sorted to obtain comprehensive cotton data;

[0015] Performing picture combination and fitting of linear model, logistic model and double Gaussian model on the comprehensive cotton data to obtain a fitting model;

[0016] Performing visual drawing using the fitting model to obtain a characteristic curve;

[0017] The characteristic curve was subjected to difference significance analysis, spearman correlation analysis and LSD significance analysis to obtain the target control ratio.

[0018] Preferably, the planting density includes: 90,000 plants / ha, 120,000 plants / ha, 150,000 plants / ha, 180,000 plants / ha, 210,000 plants / ha and 240,000 plants / ha.

[0019] Preferably, collecting biomass, seed cotton yield and meteorological data in a cotton planting field of at least one planting density includes:

[0020] Drying the sample plants in the cotton planting field at 80° C. to a constant weight to obtain the biomass;

[0021] Cotton bolls of 100 sample plants in the cotton planting field are collected, and all the cotton bolls are dried and weighed to obtain the seed cotton yield.

[0022] Preferably, based on the spatial grid method, at least one 5TE sensor is set in the cotton planting field, including:

[0023] The 5TE sensor is arranged every 20 cm in the horizontal and vertical directions of the cotton planting field. Preferably, the calculation formula of the soil moisture content is: θ is the soil moisture content; ε b is the dielectric constant of the soil volume.

[0024] Preferably, the calculation formula for the total water content of the grid includes: as well as j=1,2,3,4,..,ncol-1,ncol; ncol is the total number of columns; G i,j is the depth of the grid point in the i-th row and j-th column; ΔX and Δy are the column spacing and row spacing of the preset grid data file respectively; A i is the area of ​​the i-th cross section; V is the volume of soil water content; Grid i，j is the definite integral average of the soil moisture content in the (i, j) grid; area is the soil profile area.

[0025] Preferably, the calculation formula for the cumulative soil water consumption is: SWC is the cumulative soil water consumption; n is the number of sampling hours; Grid i It is the grid file data of the i-th hour.

[0026] Preferably, the calculation formula of the Logistic model is: Y is the biomass; A is the theoretical maximum biomass; t is the number of days after emergence; b and k are both constants.

[0027] The present invention discloses the following technical effects:

[0028] The present invention provides a cotton irrigation optimization method based on water utilization rate and yield. By collecting cotton water utilization rate and yield data, the problem of single data source is solved, and accurate screening of cotton planting density is achieved. By performing Logistic model fitting, the problem of poor fitting curve effect is solved, and nonlinear fitting of comprehensive cotton data is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1 A flow chart of a cotton planting density optimization method provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of an implementation environment provided for an embodiment of the present invention, Figure 2 (a) is the sensor grid distribution and field display real scene. Figure 2 (b) is a thumbnail of cotton planting pattern and sensor distribution grid;

[0032] Figure 3 A dynamic change diagram of above-ground and underground biomass provided by an embodiment of the present invention;

[0033] Figure 4 A quadrant diagram of the yield, harvest index and water consumption of cotton of different densities in two years provided by an embodiment of the present invention;

[0034] Figure 5 A three-dimensional relationship diagram of water consumption provided by an embodiment of the present invention, Figure 5 (a) is the three-dimensional relationship between biomass and different densities and different water consumption. Figure 5 (b) is the three-dimensional relationship between yield and different densities and different water consumption;

[0035] Figure 6 The soil water consumption distribution diagram of cotton at different densities during the bud stage and the boll stage provided by the embodiment of the present invention;

[0036] Figure 7 A schematic diagram of the distribution of soil water consumption and yield of cotton at the bud stage and the boll stage provided by an embodiment of the present invention;

[0037] Figure 8 A schematic diagram of the correlation between water consumption and output provided by an embodiment of the present invention, Figure 8 (a) is a schematic diagram of the correlation between water consumption and output in vertical position. Figure 8 (b) is a schematic diagram of the correlation between water consumption and output at horizontal position;

[0038] Fig. 9 A schematic diagram of the distribution of the difference in cotton yield and soil water consumption at different densities in two years provided by an embodiment of the present invention;

[0039] Fig.10 A schematic diagram of the relationship between the difference in biomass and yield of cotton of different densities in two years and the difference in soil water consumption provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] The purpose of the present invention is to provide a cotton irrigation optimization method based on water utilization rate and yield, and to screen the best planting density by collecting and analyzing cotton water utilization rate and yield data under different densities.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 The flowchart of the cotton planting density optimization method provided by the embodiment of the present invention is as follows: Figure 1 As shown, the present invention provides a cotton irrigation optimization method based on water utilization rate and yield, comprising:

[0044] Collect biomass, seed cotton yield, and meteorological data for cotton fields at at least one planting density;

[0045] Harvest index was calculated using biomass and seed cotton yield;

[0046] Based on the spatial grid method, at least one 5TE sensor is set in the cotton planting field;

[0047] The apparent dielectric constant of cotton fields was collected using 5TE sensors;

[0048] The apparent dielectric constant was used to calculate the soil moisture content in cotton-growing areas;

[0049] Based on Simpson's 3 / 8 rule, calculate the total water content of each preset grid;

[0050] The total water content of the grid was used to calculate the cumulative soil water consumption;

[0051] The meteorological data, biomass, seed cotton yield, harvest index, total grid water content, and cumulative soil water consumption were collated to obtain comprehensive cotton data;

[0052] The comprehensive cotton data were combined with pictures and fitted with linear model, logistic model and double Gaussian model to obtain the fitting model;

[0053] Use the fitting model for visual drawing to obtain the characteristic curve;

[0054] The characteristic curves were subjected to difference significance analysis, spearman correlation analysis and LSD significance analysis to obtain the target control ratio.

[0055] Specifically, the planting densities include: 90,000 plants / ha, 120,000 plants / ha, 150,000 plants / ha, 180,000 plants / ha, 210,000 plants / ha and 240,000 plants / ha.

[0056] Optionally, biomass, seed cotton yield and meteorological data are collected in a cotton field of at least one planting density, including:

[0057] The sample plants in the cotton planting field were dried at 80°C to a constant weight to obtain biomass;

[0058] Cotton bolls were collected from 100 sample plants in the cotton planting field, and all the bolls were dried and weighed to obtain the seed cotton yield.

[0059] refer to Figure 2 (a) and Figure 2 (b) Based on the spatial grid method, at least one 5TE sensor is set in the cotton planting field, including:

[0060] A 5TE sensor was set every 20 cm in the horizontal and vertical directions of the cotton planting field.

[0061] Specifically, the calculation formula for soil moisture content is: θ is the soil moisture content; ε b is the dielectric constant of the soil volume.

[0062] Preferably, the calculation formula for the total water content of the grid includes: as well as j=1,2,3,4,..,ncol-1,ncol; ncol is the total number of columns; G i,j is the depth of the grid point in the i-th row and j-th column; ΔX and Δy are the column spacing and row spacing of the preset grid data file respectively; A i is the area of ​​the i-th cross section; V is the volume of soil water content; Grid i，j is the definite integral average of soil moisture content in the (i, j) grid; area is the soil profile area.

[0063] Specifically, the calculation formula for cumulative soil water consumption is: SWC is the cumulative soil water consumption; n is the number of sampling hours; Grid i It is the grid file data of the i-th hour.

[0064] Specifically, the calculation formula of the Logistic model is: Y is biomass; A is the theoretical maximum biomass; t is the number of days after emergence; b and k are both constants.

[0065] Furthermore, this embodiment selects a certain test base as the experimental site. The test site is sandy loam with medium fertility, which belongs to the typical continental extreme arid desert climate, with an average annual precipitation of 40.1-82.5 mm. Meteorological data such as near-ground temperature and rainfall were obtained from the meteorological station near the experimental field. In the meteorological data, the rainfall in the sowing and seedling stages in 2019 was large, and the rainfall in the bud stage and the flower and boll stage was small; the rainfall in the bud stage and the flower and boll stage in 2020 was large.

[0066] Specifically, the field experiment included six planting densities: D1 (90,000 plants / ha), D2 (120,000 plants / ha), D3 (150,000 plants / ha), D4 (180,000 plants / ha), D5 (210,000 plants / ha) and D6 (240,000 plants / ha). A randomized block design was used, and each treatment was repeated 3 times in each block to reduce the impact of environmental factors and improve the reliability and accuracy of the experimental results. The early-maturing variety Zhongmiansuo No. 88 was used as the experimental cotton variety, and a wide and narrow double-row planting pattern of one film, three tubes and six rows was adopted. The area of ​​the plot is 47.88m 2 (6.84m = 7m), apply 230kg hm before sowing -2 N60% urea, 315kg·hm -2 P 2 O 5 and 43kg·hm -2 K 2The sowing dates in 2019 and 2020 are April 18 and April 21, respectively, and the transplanting dates are May 5 and May 11, respectively. Manual sowing and transplanting are uniformly adopted, and the harvesting dates are October 16 and October 3, respectively. This embodiment uses a single-wing labyrinth drip irrigation belt with a specification of φ16, a dripper spacing of 300mm, and a dripper flow rate of 3L·h -1 , irrigated 12 times during the cotton growing season, with the irrigation quota set at 25-35 mm. Winter and spring irrigation was carried out before sowing. Local agronomic practices were used for agronomic management in 2019 and 2020.

[0067] Furthermore, agronomic traits (plant figure and biomass) were measured every 15 days throughout the growing season, starting from 20 days after sowing until harvest. To avoid any boundary effects, 10 random cotton plants in the middle row of each plot were used to record the plant figure, and 2 random plants were used for biomass determination. According to the developmental stage, the plant samples were divided into underground organs (roots) and aboveground organs (stems, leaves, flowers and bolls), and these organs were dried at 80°C to constant weight to determine the biomass. After boll opening, a piece (6 rows) with uniform growth was selected for each replication of each treatment, and 100 cotton bolls were manually collected, dried and weighed to calculate the seed cotton yield. The harvest index is defined as the ratio of seed cotton yield to biological yield, and the formula is as follows:

[0068]

[0069] Harvest Index (HI) is an important indicator to measure the relationship between crop economic yield and total biological yield, and is usually used to evaluate crop production efficiency. Among them, Harvest Index (HI) represents harvest index; seed cotton yield represents seed cotton yield; and biological yield represents biological yield.

[0070] The growth of cotton was investigated every three days after the cotton seedlings emerged, and moisture monitoring was carried out during the bud stage (50% of the cotton plants presented buds with a diameter of about 3 mm, from June 20 to July 20 in 2019 and from June 17 to July 18 in 2020) and the boll stage (50% of the cotton plants produced the first boll, from July 20 to September 4 in 2019 and from July 18 to September 2 in 2020) of the cotton population.

[0071] Specifically, a sensor network based on a cloud control system was set up in 6 different cotton densities to monitor the soil volumetric moisture content in each plot. According to the spatial grid method, a 5TE sensor was set every 20 cm in the horizontal and vertical directions between the cotton rows of each different density, with a total of 30 sampling points. The 5TE sensor collected soil temperature data and apparent dielectric constant ε every 1 hour.b , and upload the data to the cloud. Use Topp to estimate soil moisture content θ:

[0072]

[0073] In order to evaluate the difference in soil moisture content between cotton plants of different densities, the required grid data were extracted using Software Stata 16.0, and a Grid(i,j) file was created for the sensor data, where i and j represent the horizontal and vertical coordinates. The total moisture content of each grid was calculated using the Simpson 3 / 8 rule:

[0074]

[0075]

[0076] G i,1 , G i,2 , G i,3 ...is the coefficient vector; G i,j is the depth of the grid point in the i-th row and j-th column, ΔX and Δy are the column and row spacing of the grid data file respectively. i is the area of ​​the ith cross section, and V is the total soil moisture volume measured on the specific cross section. i，j is the definite integral average of the soil water content in the (i, j) grid data file, and area is the area of ​​the soil profile. The cumulative soil water consumption SWC of the cotton population is calculated using the following formula:

[0077]

[0078] n is the number of sampling hours, which is also the order of the grid file. i is the grid file data of the ith hour. -1 and water consumption rate greater than 0.1 mm·h -1 This method reduces the impact of extreme rainfall on the calculation accuracy and takes into account that the water in the soil at a depth of 10-110cm is not only consumed by crops, but also infiltrates from shallow soil to deep soil.

[0079] Preferably, the two-factor correlation between water consumption and yield and between water consumption difference and yield difference is subjected to spearman correlation analysis, and the one-way ANOVA is performed by least significant difference (LSD) to test the significance of the difference, and the calculation formula is as follows:

[0080]

[0081] Where rs is the correlation coefficient, k represents the total number of data, and d i represents the rank difference of the i-th pair of data, and Indicates all d i The sum of the sums of squares. To constrain i, ensure that d i Only valid data pairs are involved. Usually, the value of i should range from 1 to n, where n represents the total number of data. The Spearman correlation coefficient is mainly used to solve problems related to name data and sequence data. Since it does not require data conditions like the Pearson correlation coefficient, the Spearman correlation coefficient has a much wider range of application.

[0082] Furthermore, statistical correlation refers to a linear relationship and cannot reflect the specific process of the influence between variables. Therefore, this embodiment adopts a nonlinear model, the logistic growth model, to simulate the relationship between each variable for cotton plants, aboveground and underground biomass and post-emergence date, and the calculation formula is as follows:

[0083]

[0084] Where "Y" is the biomass of cotton plants, "A" is the theoretical maximum biomass, "t" is the days after emergence (DAE), and "b" and "k" are constants.

[0085] Specifically, Kriging is a method for unbiased optimal estimation of regionalized variables in a limited area. It is often used for two-dimensional interpolation. All spatiotemporal distribution maps of moisture are generated by Kriging interpolation. Spatial data interpolation: Kriging can interpolate between a set of discrete sampling point data to generate a continuous spatial data distribution. This allows estimates to be obtained at unsampled points, which helps to comprehensively analyze data in the region. Data smoothing and denoising: Kriging takes into account the spatial correlation between sampling points. Through interpolation, it not only fills the gaps in the data, but also smoothes the data, reducing the impact of random noise on the analysis results, thereby improving the quality of the data. Improve estimation accuracy: Kriging provides an unbiased optimal estimate, which can minimize the estimation variance and provide a more accurate spatial data distribution. This provides more reliable basic data for subsequent analysis, such as calculating cumulative change (SWC). Kriging not only provides unbiased estimates, but also makes the estimated variance as small as possible. After the attribute values ​​of the interpolation points are determined, a continuous spatial data distribution of the entire region can be generated. This is very important for applications such as generating maps of spatial variables, conducting environmental simulations, or predicting future changes. Determining the attribute value is a key step in the Kriging method, which ensures the unbiased, accurate and continuous interpolation results, making the Kriging method an indispensable tool in spatial data analysis and prediction. 0 The estimated value of is calculated as follows:

[0086]

[0087]

[0088] Where Z*(x 0 ) is x 0 Kriging interpolation result of the point, λ i is the weight coefficient to be determined, C(x i ,x j ) is Z(x i ) and Z(x j ) is the covariance function of .

[0089] Further, 1) Spatial data interpolation, Purpose: Interpolate between a set of discrete sampling point data to generate a continuous spatial data distribution. Function: Kriging estimates attribute values ​​at unsampled points by considering the spatial correlation between sampling points. This makes the data distribution in the entire area more complete and facilitates comprehensive analysis. For example, when studying soil moisture, the Kriging method can generate a soil moisture distribution map for the entire study area, even if some areas do not have direct sampling data. 2) Data smoothing and denoising, Purpose: Interpolation not only fills in the gaps in the data, but also smoothes the data, reducing the impact of random noise on the analysis results. Function: Kriging takes into account the spatial correlation between sampling points during the interpolation process. This correlation makes the interpolation result not only a simple filling, but also a certain degree of smoothing of the data. This can reduce the impact of random noise and improve the quality of the data. For example, when analyzing environmental pollution data, through Kriging interpolation, a smoother and more reliable pollution distribution map can be obtained, which more accurately reflects the actual situation. 3) Improve estimation accuracy, use: Kriging provides an unbiased optimal estimate, which can minimize the estimation variance and provide a more accurate spatial data distribution. Function: Through the Kriging method, the interpolation results generated are unbiased and have the smallest variance. This means that the interpolation results have higher accuracy and reliability. For example, in meteorological data analysis, Kriging can provide more accurate spatial distribution data such as temperature and precipitation, providing a reliable data basis for further climate research and prediction. 4) Generate continuous spatial data distribution, use: The determination of attribute values ​​can generate continuous spatial data distribution for the entire region. Function: Once the attribute value of the point to be interpolated is determined, a continuous spatial data distribution for the entire region can be generated. This is very important for applications such as generating maps of spatial variables, conducting environmental simulations, or predicting future changes. For example, in water resources management, the water resources distribution map generated by the Kriging method can help decision makers to reasonably allocate and manage water resources. In short, the Kriging method significantly improves the quality and reliability of data analysis through spatial interpolation, data smoothing, improving estimation accuracy, and generating continuous spatial data distribution. It is a very important tool in spatial data analysis and prediction.

[0090] Specifically, Microsoft Office Excel 2019 (Microsoft, Bothell, WA, USA) and Stata 16.0 (Stata Corp LP, College Station, Texas, USA) were used for data collation. Adobe Illustrator CC 2019 (Adobe Systems., San Jose, CA, USA), Microsoft Office PowerPoint 2019 (Microsoft, Bothell, WA, USA) and GraphPad Prism 8.0 (GraphPad Software, Dotmatics, Boston, MA, USA) were used for picture combination, linear model, logistic model and double Gaussian model fitting. Surfer 20 (Golden Software Inc., Golden, CO, USA) was used for visualization of soil moisture data. SPSS 25.0 (SPSS Inc., Chicago, IL, USA) was used for significance analysis, spearman correlation analysis and LSD significance analysis at the 0.05 level.

[0091] refer to Figure 3 , the dynamic changes of cotton with different planting densities within 120 days after emergence all conform to the logical growth curve, but the performance varies in different years. In 2019, the growth curves of total biomass, aboveground and underground biomass of cotton with different densities were significantly different, while in 2020, only the curves of underground biomass were more obvious. The underground biomass of cotton with different densities in both years tended to stabilize about 70 days after emergence. When the density was D1 to D4, the maximum underground biomass of the two-year model showed the characteristic of increasing with increasing density. When the density continued to increase, the underground biomass decreased in 2019, while it increased slightly in 2020. In 2019, the growth rates of total biomass and aboveground biomass of cotton of different densities showed a downward trend 120 days after emergence, while in 2020 there was still a rapid increase trend 120 days after emergence.

[0092] refer to Figure 4In terms of cotton yield and water consumption, all six planting densities in 2019 were low water consumption and low yield. In 2020, D1 was high water consumption and low yield, D2, D3 and D6 were low water consumption and high yield, and D3 and D4 were high water consumption and high yield. The water consumption of D1 was greatly affected by the inter-annual influence, and the water consumption and yield of D3 and D4 were greatly affected by the inter-annual influence. The ratio of yield to water consumption is the water use efficiency. In 2019, it was relatively dry, and all planting densities had high water use efficiency. In 2020, the water use efficiency of D6 was the highest and that of D1 was the lowest. In the quadrant diagram of harvest index and water consumption, it can be seen that D6 showed low water consumption and high HI in both years, while D2 showed low water consumption and low HI in both years. The water consumption or HI of other planting densities was not stable in two years. For example, the water consumption of D4 and D5 had large inter-annual differences, and the water consumption and HI of D1 and D3 had large inter-annual differences.

[0093] refer to Figure 5 , using the 3D-surface interpolation method to generate a three-dimensional benefit diagram of density-water consumption-biomass / yield. In the three-dimensional surface diagram, the steeper the surface corresponding to the response factor, the more significant the impact of the factor on the response value. Figure 5 In (a), the effect of planting density on biomass accumulation is more obvious; Figure 5 In (b), the effect of water consumption on yield is more obvious. With the increase of density and water consumption, biomass and yield increase. When the density is below D2, biomass and yield increase with the increase of water consumption, but the value is still higher than that of low density. When the density is greater than D2, the biomass shows a trend of first increasing and then decreasing. From D2 to D4, the yield shows a trend of first increasing and then decreasing with the increase of water consumption, and from D5 to D6, it shows a trend of increasing with the increase of water consumption. When the density reaches about D5, the water consumption is controlled at about 250-400mm, and a large biomass and yield can be achieved at the same time. D3 and D4 can also achieve high yields, but the cotton biomass is lower. D6 can also achieve high yields, but the required water consumption is higher.

[0094] refer to Figure 6, the grid method was used to draw contour maps to visualize sensor data, so as to obtain the water consumption distribution characteristics of the soil profile. The overall trend of water consumption in the bud and boll stages of cotton with increasing density is relatively similar. The main difference is that the water consumption of cotton in the bud stage in 2019 was mainly concentrated in the soil layer of 0-30cm, and the difference in water consumption between density treatments was not obvious. There was no change in water consumption in the soil layer below 30cm. The water consumption in 90-110cm may be an individual phenomenon caused by leakage, while the water consumption between cotton rows in the horizontal position was significantly higher. The water consumption in the boll stage in 2019 was consistent with the trend of the bud stage, and the water consumption increased significantly. The main difference was that the water consumption in the shallow layer of 0-30cm increased, and the water consumption in the bottom soil of 90-110cm increased with increasing density. The change in water consumption in the bud and boll stages of cotton in 2020 was significantly larger than that in 2019, and the water consumption trend in the two periods was basically the same with the change in density. The total soil water consumption of D1, D2 and D3 showed a decreasing trend, the total soil water consumption of D4 and D5 showed an increasing trend, and the total soil water consumption of high-density D6 showed a slowly decreasing trend; the water consumption of D4 and D5 density changed greatly at the soil depth of 10-110cm; the other four densities had no obvious difference in shallow soil (10-30cm), but changed in deep soil (40-110cm). The cotton boll stage had higher soil water consumption in the entire soil layer than the bud stage.

[0095] refer to Figure 7 , Pearson correlation analysis was conducted on the distribution of water consumption and yield at different densities during the bud and boll stages of cotton in two years, and the relationship between soil water consumption distribution and yield during the bud and boll stages was quantified by significance and correlation coefficient. During the cotton bud stage, at a depth of 30-50cm, water consumption at most positions was positively correlated with cotton yield; at a depth of 70-90cm, water consumption at horizontal inter-row positions was significantly positively correlated with yield. The position with a significant positive correlation between water consumption and yield during the boll stage was the horizontal inter-row position. Water consumption at the cotton row during the boll stage had no significant correlation with yield.

[0096] refer to Figure 8 , the correlation between the average water consumption and yield of vertical and horizontal sections of cotton with different densities was further analyzed. Most values ​​were clustered and had strong spatial correlation. The cotton yield of different treatments in the bud and boll stages showed a significant positive correlation with soil water consumption at a depth of 30-90 cm, and the correlation coefficient in the boll stage was greater than that in the bud stage. In the bud and boll stages, the soil water consumption of D4 and D5 at different horizontal positions was also significantly correlated with the yield, and the yield of D2 was significantly correlated with the water consumption at different horizontal positions in the boll stage. The horizontal distribution of yield and water consumption of D1, D3, and D6 showed a weak decoupling phenomenon.

[0097] refer to Fig. 9In the bud stage and the boll stage, the cotton yield difference was weakly decoupled from the water consumption difference at different spatial locations. However, the water consumption difference at the depth of 30-90 cm in the center line of the cotton row during the bud stage was significantly positively correlated with the yield difference. There is a big difference between the boll stage and the bud stage, and the main reason for the difference in cotton yield is the difference in water consumption on the cotton row, not in the center line between rows.

[0098] refer to Fig.10 , when water consumption increases, cotton biomass and seed cotton yield will also increase. Nonlinear regression results show that the fitting effect of double Gaussian model is the best, that is, total water consumption increases, and the final biomass and seed cotton yield of cotton will first show a trend of becoming larger, but when water consumption exceeds a certain value (reaching the Mean1 point of the double Gaussian model), biomass and yield will decline and then rise, until more water is consumed (reaching the Mean2 point of the double Gaussian model), the second peak value of biomass and yield will appear. In the present embodiment, the cotton water consumption reaches the first high-yield peak when it is 255.9mm, and reaches the first biomass peak when it is 297.2mm. The difference correlation between the water consumption difference of cotton in the two-year bud stage and the difference in biomass and yield is not significant, and the difference in biomass and water consumption is significantly positively correlated in the flowering and boll stage, and the difference in yield is significantly negatively correlated with the difference in water consumption. The intercept of the regression line and the X-axis represents the point where the difference in biomass or yield is zero. When the difference in water consumption of cotton in different plots in the two years was closer to 116.3 mm and 130.8 mm, respectively, the biomass and yield were more stable.

[0099] Furthermore, this embodiment uses high spatiotemporal resolution sensors and geostatistical methods to explore in detail the effects of different planting densities on cotton growth dynamics, soil moisture distribution and utilization efficiency. The results show that planting density and soil water consumption significantly affect the underground biomass and yield of cotton. At a density of 210,000 plants / ha, when the water consumption is between 250-400 mm, the biomass and yield of cotton reach a stable high level. The fitting results of the double Gaussian model show that when the water consumption is 255.9 to 297.2 mm, the biomass and yield reach the first peak; then, as the water consumption increases, both decrease first and then increase. When the water consumption increases to 500 mm to 630 mm, the second yield and biomass peak is reached. There is a significant correlation between the soil water consumption distribution and yield during the bud stage and the boll stage, among which the water consumption of the 30-50 cm deep soil during the bud stage is positively correlated with cotton yield, while the water consumption during the boll stage is mainly significantly correlated with the yield in the inter-row position. For different cotton planting densities, when the water consumption difference is close to 116.3 mm and 130.8 mm, the biomass and yield are more stable. This example reveals the spatial competition pattern and efficient utilization law of cotton population for soil water under different planting densities. These results are conducive to optimizing cotton planting density and improving water management strategies, and have practical significance for improving water use efficiency and stable and high yield.

[0100] The beneficial effects of the present invention are as follows:

[0101] The present invention realizes the screening of the optimal planting density by collecting and analyzing the cotton water utilization rate and yield data under different densities.

[0102] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0103] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A cotton irrigation optimization method based on water utilization rate and yield, characterized in that: include: Collect biomass, seed cotton yield, and meteorological data for cotton fields at at least one planting density; Calculating a harvest index using the biomass and the seed cotton yield; Based on the spatial grid method, at least one 5TE sensor is set in the cotton planting field; Using the 5TE sensor to collect the apparent dielectric constant of the cotton planting site; Calculating the soil moisture content of the cotton planting site using the apparent dielectric constant; Based on Simpson's 3 / 8 rule, calculate the total water content of each preset grid; Calculating cumulative soil water consumption using the total water content of the grid; The meteorological data, the biomass, the seed cotton yield, the harvest index, the total water content of the grid and the cumulative soil water consumption are sorted to obtain comprehensive cotton data; Performing picture combination and fitting of linear model, logistic model and double Gaussian model on the comprehensive cotton data to obtain a fitting model; Performing visual drawing using the fitting model to obtain a characteristic curve; The characteristic curve was subjected to difference significance analysis, spearman correlation analysis and LSD significance analysis to obtain the target control ratio.

2. A cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: The planting densities include: 90,000 plants / ha, 120,000 plants / ha, 150,000 plants / ha, 180,000 plants / ha, 210,000 plants / ha and 240,000 plants / ha.

3. The cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: Collect biomass, seed cotton yield, and meteorological data for cotton fields at at least one planting density, including: Drying the sample plants in the cotton planting field at 80° C. to a constant weight to obtain the biomass; Cotton bolls of 100 sample plants in the cotton planting field are collected, and all the cotton bolls are dried and weighed to obtain the seed cotton yield.

4. The cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: Based on the spatial grid method, at least one 5TE sensor is set in the cotton planting field, including: The 5TE sensor is arranged every 20 cm in the horizontal and vertical directions of the cotton planting field.

5. The cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: The calculation formula for the soil moisture content is: θ is the soil moisture content; ε b is the dielectric constant of the soil volume.

6. The cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: The calculation formula of the total water content of the grid includes: as well as ncol is the total number of columns; G i,j is the depth of the grid point in the i-th row and j-th column; ΔX and Δy are the column spacing and row spacing of the preset grid data file respectively; A i is the i-th cross-sectional area; V is the soil water content volume; Grid i，j is the definite integral average of the soil moisture content in the (i, j) grid; area is the soil profile area.

7. The cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: The calculation formula for the cumulative soil water consumption is: SWC is the cumulative soil water consumption; n is the number of sampling hours; Grid i It is the grid file data of the i-th hour.

8. The cotton irrigation optimization method based on water utilization rate and yield according to claim 1, characterized in that: The calculation formula of the Logistic model is: Y is the biomass; A is the theoretical maximum biomass; t is the number of days after emergence; b and k are both constants.

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