Coastal Saline-alkali Land Soil Monitoring Data Acquisition System

Abstract

The present invention relates to the technical field of data acquisition. Specifically, it relates to a soil monitoring data acquisition system for coastal saline-alkali land, including a soil data acquisition module, an underground river shape analysis module, and an irrigation fresh water prediction module. The saline-alkali land is equally divided into multiple identical regions through the soil data acquisition module. Then, the underground river shape analysis module analyzes the regions with similar fresh water amounts through the historical data in the divided regions, calculates the conductivity difference of the regions corresponding to the similar irrigation fresh water amounts. If the conductivity difference > the conductivity difference threshold, it is determined that there is an underground river under the two corresponding divided regions, constructs the shape of the underground river, defines divided regions A and B according to the shape of the underground river, and calculates the correlation coefficient between the conductivity and the fresh water irrigation amount in divided region A. Thus, when irrigating with fresh water, the fresh water irrigation amount with different soil conductivities can be calculated according to the correlation coefficient, avoiding blind large-scale irrigation and reducing the waste of water resources caused by over-irrigation.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and more specifically, to a soil monitoring data acquisition system for coastal saline-alkali land. Background Art

[0002] The high salinity and alkaline soil in coastal saline-alkali land have many adverse effects on the growth of crops. The salt ions in the soil can cause difficulties for plant roots to absorb water, resulting in physiological drought. When the salt concentration in the soil solution is too high, the osmotic pressure of root cells is lower than that of the soil solution, and water cannot enter the roots normally, leading to slow plant growth, poor development, or even death. According to statistics, on unimproved coastal saline-alkali land, the survival rate of crops is usually less than 30%, and the yield is extremely low, severely restricting the development of local agriculture. Therefore, before carrying out agricultural planting on saline-alkali land, workers need to regularly detect different areas of the saline-alkali land, and based on the detection results, implement fresh water irrigation on the saline-alkali land and store the detection data as historical data.

[0003] However, in coastal areas, the geological structure is relatively complex. After long-term geological changes and seawater erosion, there have been many changes in the structure of the underground rock layer and soil layer, creating conditions for the formation of underground rivers. Therefore, there will be underground rivers below the saline-alkali land, causing the fresh water that should slowly infiltrate in the soil pores during irrigation to flow away along the underground river. If the amount of fresh water required for irrigation in different areas of the saline-alkali land cannot be analyzed based on the existence of the underground river, it will affect the effect of fresh water irrigation on the saline-alkali land. In view of this, we propose a soil monitoring data acquisition system for coastal saline-alkali land. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that during the process of irrigating fresh water on saline-alkali land, under normal circumstances, the irrigated fresh water should slowly infiltrate in the soil pores, gradually dissolve and carry away the salts in the soil, playing a role in improving the soil. However, in coastal saline-alkali land, due to the sea-level rise and fall during the geological history period, there are often underground rivers below the saline-alkali land. Once encountering an underground river, the fresh water will be affected by its strong attraction and flow away quickly along the underground river, shortening the residence time of the fresh water in the soil and making it impossible to analyze the amount of fresh water for irrigation in the underground river area.

[0005] To achieve the above purpose, the present invention provides a soil monitoring data acquisition system for coastal saline-alkali land that can collect data for analyzing the shape of underground rivers in historical data and predict the amount of fresh water for the next irrigation based on the shape of the underground river, including a soil data acquisition module, an underground river shape analysis module, and an irrigation fresh water prediction module.

[0006] The soil data acquisition module divides the saline-alkali land at equal intervals, and uses the distance calculation method to determine the historical data of the divided areas; the underground river shape analysis module normalizes the historical data, calculates the difference value between the water content and the conductivity in the historical data, judges the divided areas with similar fresh water amounts for irrigation, and uses the graph theory method to establish the underground river shape; the fresh water irrigation prediction module defines divided areas A and B based on the underground river shape, uses the change trend analysis method to analyze the effective irrigation depth of fresh water, calculates the conductivity of the effective irrigation depth and the fresh water irrigation amount, and calculates the correlation coefficient between the two.

[0007] As a further improvement of this technical solution, the soil data acquisition module establishes a plane rectangular coordinate system with a certain corner point of the saline-alkali land as the origin, and senses the saline-alkali land in The length in the axis direction is The length in the axis direction is ;

[0008] The number of rows is: where represents rounding down; the number of columns is: .

[0009] As a further improvement of this technical solution, the working principle of the distance calculation method in the soil data acquisition module is as follows:

[0010] Sense the detection coordinates in each divided area. If the number of detection coordinates >, then calculate the distance between the detection coordinates and the center coordinates of the divided area;

[0011] Sense that the coordinates of the lower left vertex of the divided area are , and the coordinates of the upper right vertex are , then the center coordinates of the divided area are: ; ;

[0012] The distance between each detection coordinate and the center coordinates of the divided area is: where Detection coordinates, is the center coordinate, is the th distance between the detection coordinate and the center coordinate. Call out the smallest , which is the historical data of the divided area.

[0013] As a further improvement of this technical solution, the calculation formula for the normalization process of the underground river shape analysis module is as follows:

[0014] ;

[0015] wherein, is the water content after normalization, is the minimum value of the water content in the historical data, is the maximum value of the water content in all historical data; is the conductivity after normalization, is the minimum value of the conductivity in the historical data, is the maximum value of the conductivity in all historical data, and the above conductivity is the average value of the conductivities at different depths;

[0016] As a further improvement of the technical solution, the underground river shape analysis module senses the conductivity and water content of the normalized irrigation fresh water: , wherein, is the conductivity and water content of the th divided area after irrigation with fresh water, is the th divided area, and is the average value of the conductivities at different depths , is the th divided area, and is the detection quantity of the conductivities at different depths, is the th divided area, and is the water content;

[0017] Calculate the difference values between the water content and the conductivity in sequence: , where , is the difference value, is the quantity of the difference values;

[0018] Sort the difference values in descending order into a difference value set , ;

[0019] Set a judgment threshold , and select the data in the difference value set from large to small: : , wherein, represents rounding down, is the quantity of the difference data selected from the difference value set, and the corresponding divided areas are irrigated with similar amounts of fresh water;

[0020] Calculate the average value of the selected data as the difference threshold: Sense the selected data as: , and at this time the difference threshold is wherein, is the quantity of the selected data.

[0021] As a further improvement of this technical solution, the underground river shape analysis module senses the unselected difference values , is the number of unselected difference values. The unselected difference values are compared with the difference threshold in turn. If , it is determined that the area corresponding to the difference value is irrigated with fresh water of similar water volume. If , it is determined that there is no fresh water irrigation in the area corresponding to the difference value.

[0022] As a further improvement of this technical solution, for the graph theory method in the underground river shape analysis module, the conductivity of the similar irrigation fresh water division area is retrieved, and the conductivity difference threshold is set. If the conductivity difference at the same depth > the conductivity difference threshold, it is determined that there is an underground river below the two corresponding division areas. By comparing in turn, the underground rivers below multiple division areas are connected, which is the shape of the underground river.

[0023] As a further improvement of this technical solution, the trend analysis method in the irrigation fresh water prediction module analyzes the change trend of the conductivity at different depths. When the conductivity changes from decreasing to increasing, the decreasing corresponding detection depth is the effective irrigation depth of fresh water, and its expression is: Sense a total of division areas A. For division area A, at depth , the conductivity is denoted as , and the fresh water irrigation volume is denoted as ; when , , it is determined that the conductivity is in the decreasing stage; when, it is determined that the conductivity is in the increasing stage, is the effective irrigation fresh water depth of division area A.

[0024] As a further improvement of this technical solution, the correlation coefficient in the irrigation fresh water prediction module is used to predict the irrigation water volume at different irrigation depths when dividing the area for irrigation with fresh water: Integrate the conductivity of the effective irrigation depth as: ; The irrigation fresh water volume ; Calculate the mean conductivity at the effective irrigation depth: , and the mean irrigation fresh water volume: ;

[0025] The correlation coefficient .

[0026] As a further improvement of the technical solution, the irrigation fresh water prediction module further defines the divided area above the underground river as divided area B. If the conductivity change trend in divided area B is the same as that in divided area A, then divided area B is mapped to divided area A. If the conductivity change trend continuously decreases, it is determined that the amount of irrigation fresh water in divided area B is excessive.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] In the soil monitoring data acquisition system for coastal saline-alkali land, the saline-alkali land is equally divided into multiple identical areas through the soil data acquisition module. Then, the underground river shape analysis module analyzes the areas with similar fresh water amounts through the historical data in the divided areas, calculates the conductivity difference of the areas corresponding to the similar irrigation fresh water amounts. If the conductivity difference > the conductivity difference threshold, it is determined that there is an underground river below the two corresponding divided areas. Then, the coordinates of the multiple divided areas are connected to construct the shape of the underground river. At this time, the irrigation fresh water prediction module defines divided area A and B according to the shape of the underground river, calculates the correlation coefficient between the conductivity and the fresh water irrigation amount in divided area A, so that when irrigating fresh water, the fresh water irrigation amount with different soil conductivities can be calculated according to the correlation coefficient, avoiding blind large-scale irrigation and reducing water resource waste caused by over-irrigation. Especially in coastal areas where water resources are relatively scarce, the utilization efficiency of water resources is improved; and according to the conductivity change trend, it is analyzed whether the existence of the underground river will interfere with the judgment of the fresh water irrigation effect. If not, then divided area B is mapped to divided area A, and then according to the conductivity change trend in divided area B, it is analyzed whether the historical irrigation fresh water has an excessive irrigation amount, ensuring the scientific nature of the irrigation strategy, enabling the fresh water to act more effectively on the improvement of saline-alkali land, improving the effect of irrigation on reducing soil salinity, and realizing precise irrigation.

[0029] In addition to the purposes, features, and advantages described above, the present invention has other purposes, features, and advantages. The following will refer to the drawings for a further detailed description of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the overall module schematic diagram of the present invention;

[0031] Figure 2 It is the schematic diagram of the conductivity change trend of the present invention.

[0032] The meanings of the reference numerals in the figure are as follows:

[0033] 100. Soil data acquisition module; 200. Underground river shape analysis module; 300. Irrigation fresh water prediction module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Referring to Figure 1 - Figure 2 as shown, the soil monitoring data acquisition system for coastal saline-alkali land includes a soil data acquisition module 100, an underground river shape analysis module 200, and an irrigation fresh water prediction module 300;

[0036] In order to avoid affecting the accuracy and comparability of data due to the unfixed detection coordinates during the detection of saline-alkali land (the topography and geomorphology of coastal saline-alkali land are often complex, with various terrains such as tidal flats, swamps, and wetlands. When detecting in these areas, due to terrain limitations, it may be difficult for the detection personnel to accurately reach the pre-set coordinate positions, and they can only temporarily select relatively accessible locations nearby for detection, resulting in changes in the detection coordinates), the soil data acquisition module 100 equally divides the saline-alkali land and uses the distance calculation method to select historical data close to the central coordinates of the divided areas (the historical data includes data such as the water content of the saline-alkali land, the conductivity of different depths of the saline-alkali land before and after irrigation with fresh water, the amount of irrigation fresh water in different areas, and the irrigation coordinates) as the historical data representing the area. Specifically, it includes the following steps:

[0037] Step 1: Establish a plane rectangular coordinate system with a corner point of the saline-alkali land as the origin, and sense the length of the saline-alkali land in the axis direction as , and the length in the axis direction as , and set the equally divided spacing as ;

[0038] The number of divided rows is: , where represents rounding down; the number of divided columns is: .

[0039] Step 2: Sense the detection coordinates within each divided area. If the number of detection coordinates > 1, calculate the distance between the detection coordinates and the central coordinates of the divided area;

[0040] Sense that the lower left vertex coordinates of the divided area are , and the upper right vertex coordinates are , then the central coordinates of the divided area are: ; ;

[0041] The distance between each detection coordinate and the central coordinate of the divided area is: , where is the detection coordinate, is the central coordinate, is the th distance between the detection coordinate and the central coordinate. The smallest is retrieved, which is the historical data of the divided area.

[0042] After saline-alkali land is irrigated with fresh water, as fresh water is added, the fresh water will mix with the salts in the soil. At this time, the proportion of salt ions in the solution with an increasing total volume decreases, resulting in a decrease in conductivity. Therefore, the relationship between water content and conductivity is an inverse relationship. The underground river shape analysis module 200 determines the divided area of the similar fresh water volume for irrigation by calculating the difference between the water content and conductivity and setting a judgment threshold;

[0043] Since water content and conductivity are two indicators with different physical meanings and dimensions, if the difference is directly calculated through historical data, some larger numerical data will dominate the calculation process, while smaller numerical values will be ignored. Therefore, first, the water content and conductivity are normalized, and the calculation formula is as follows:

[0044] , where is the water content after normalization, is the minimum value of the water content in the historical data, is the maximum value of the water content in all historical data; is the conductivity after normalization, is the minimum value of the conductivity in the historical data, is the maximum value of the conductivity in all historical data; and the conductivity is the average value at different depths;

[0045] Then, sense the conductivity and water content after irrigating with normalized fresh water: , where is the conductivity and water content of the th divided area after irrigating with fresh water, is the th average value of the conductivity at different depths within the th divided area is the th detection quantity of the conductivity at different depths within the th divided area, is the water content of the

[0046] Calculate the difference value between the water content and conductivity in sequence: , where , is the difference value, is the number of difference values;

[0047] Arrange the difference values in descending order and sort them into a set of difference values , ;

[0048] Set a judgment threshold , and select data from the set of difference values from large to small: Data: , where represents rounding down, is the number of difference data selected from the set of difference values, and there is a similar amount of fresh water irrigated in the corresponding divided area;

[0049] Calculate the average value of the selected data as the difference threshold: The perceived selected data is: , and at this time the difference threshold is where is the number of selected data.

[0050] To avoid abnormal data generated by historical data due to factors such as measurement errors and differences in local soil characteristics, resulting in the difference values calculated from the abnormal data being greatly deviated from the normal range, the underground river shape analysis module 200 perceives the unselected difference values , is the number of unselected difference values. Compare the unselected difference values with the difference threshold in turn. If , it is judged that there is a similar amount of fresh water irrigated in the divided area corresponding to the difference value. If , it is judged that there is no fresh water irrigated in the area corresponding to the difference value.

[0051] To analyze the shape of the underground river under the saline-alkali land, the underground river shape analysis module 200 retrieves the conductivity (conductivity at different depths) of the similar irrigation fresh water divided area, sets the conductivity difference threshold. If the conductivity difference at the same depth > the conductivity difference threshold, it is judged that there is an underground river under the two corresponding divided areas. Compare them in turn and use the graph theory method to connect the underground rivers under multiple divided areas, which is the shape of the underground river. Its expression is as follows:

[0052] Perceive any two divided areas and , at depth the conductivity difference is ;

[0053] Using graph theory to establish the shape of the underground river: Each divided area is regarded as a node. If (indicating that the conductivity difference at the same depth > the conductivity difference threshold), then an edge is added between nodes and . The edge is the underground river existing below the two areas;

[0054] Further considering that during the geological history period, the sea level has experienced multiple rises and falls. When the sea level drops, the downcutting effect of rivers in coastal areas is enhanced, and the rivers deepen and widen. As the sea level rises again, part of the ancient river channels are flooded by seawater, but their channel structures underground still remain and become underground rivers. When there is an underground river in the area corresponding to the irrigation fresh water in some areas, the underground river is equivalent to a hidden drainage channel in the soil, causing the fresh water that should have slowly infiltrated in the soil pores to be diverted by the strong attraction of the underground river at this time. If a large amount of irrigation fresh water is diverted by the underground river, it will cause waste of fresh water. Therefore, the irrigation fresh water prediction module 300 senses the divided areas that irrigate the same amount of fresh water and are not above the underground river, defines them as divided area A, and retrieves the conductivity (conductivity at different depths after irrigation) and the amount of irrigation fresh water in divided area A. In order to avoid not knowing the treatment effects achieved by different amounts of irrigation fresh water when treating saline-alkali land, the trend analysis method analyzes the change trend of the conductivity at different depths. When the conductivity changes from decreasing to increasing, the decreasing corresponding detection depth is the effective irrigation depth of fresh water. According to the effective irrigation depth and the amount of fresh water irrigation, the correlation coefficient between the two is calculated to predict the amount of irrigation water required for different irrigation depths when irrigating fresh water in the divided area;

[0055] Sense that there are a total of divided areas A. For divided area A, at depth , the conductivity is denoted as , and the amount of fresh water irrigation is denoted as ; When , , it is determined that the conductivity is in the decreasing stage; When, it is determined that the conductivity is in the increasing stage, Then it is the effective irrigation depth of fresh water for divided area A.

[0056] Correlation coefficient: Integrate the conductivity at the effective irrigation depth as: ; The amount of fresh water irrigation ; Calculate the mean value of the conductivity at the effective irrigation depth: , and the mean value of the amount of fresh water irrigation: ;

[0057] Correlation coefficient ;

[0058] Correlation coefficient Based on statistical principles, the numerator reflects the degree of co-variation between variables, and the denominator is used for standardization to obtain a correlation coefficient between -1 and 1 to measure the linear correlation between the two;

[0059] Therefore, by analyzing the change trend of the conductivity at different depths in the divided area A to determine the effective irrigation depth of fresh water, the influence range and degree of the irrigated fresh water on the saline-alkali land in the soil can be intuitively understood. Combining with the amount of irrigated fresh water, the treatment effects achieved by different irrigation amounts can be clarified, helping researchers and agricultural workers accurately evaluate the progress and effectiveness of saline-alkali land treatment.

[0060] To avoid the situation that although there is an underground river under the saline-alkali land, due to the excessive depth of the underground river from the saline-alkali land, the underground river will not affect the irrigated fresh water when irrigating the saline-alkali land. If it cannot be distinguished whether the underground river affects the irrigation of the saline-alkali land with fresh water, it will lead to the inability to effectively reduce the soil salinity by reasonably adjusting the irrigation strategy. Therefore, the divided area above the underground river where the irrigation fresh water prediction module 300 is located is defined as divided area B. If the conductivity change trend in divided area B is the same as that in divided area A, then divided area B is mapped to divided area A. If the conductivity change trend continuously decreases, it is determined that the amount of irrigated fresh water in divided area B is too much, so as to prevent the interference of the existence of the underground river on the judgment of the irrigation effect. If such a distinction is not made, the influence of the underground river on the soil conductivity will be attributed to the irrigation strategy problem, resulting in the inability to accurately evaluate the true effect of irrigation on saline-alkali land treatment.

[0061] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only the preferred examples of the present invention and do not limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A soil monitoring data acquisition system for coastal saline-alkali land, characterized in that, It includes a soil data collection module (100), a subterranean river shape analysis module (200), and an irrigation fresh water prediction module (300); The soil data collection module (100) equally divides the saline-alkali land, and uses the distance calculation method to determine the historical data of the divided areas; the subterranean river shape analysis module (200) normalizes the historical data, calculates the difference value between the water content and the conductivity in the historical data, judges the divided areas with similar fresh water amounts for irrigation, and uses the graph theory method to establish the subterranean river shape; the irrigation fresh water prediction module (300) defines divided areas A and B based on the subterranean river shape, uses the change trend analysis method to analyze the effective irrigation depth of fresh water, calculates the conductivity of the effective irrigation depth and the fresh water irrigation amount, and calculates the correlation coefficient between the two; The dark river shape analysis module (200) senses the conductivity and water content of the irrigation fresh water after normalization: , where is the conductivity and water content of the th divided area after irrigation with fresh water, is the mean value of the conductivity at different depths within the th divided area, , is the detection quantity of the conductivity at different depths within the th divided area, is the water content of the th divided area; Calculate the difference values between the water content and the conductivity in sequence: , where , is the difference value, is the number of difference values; Arrange the difference values in descending order as a set of difference values , ; Set a judgment threshold , select data from the difference value set in descending order : , where represents rounding down, is the number of difference data in the selected difference value set, and there is a similar amount of fresh water for irrigation in the corresponding divided area; Calculate the average value of the selected data as the difference threshold: The perceived selected data is: , and at this time the difference threshold is where is the number of selected data; The underground river shape analysis module (200) senses the unselected difference values , is the quantity of unselected difference values. The unselected difference values are successively compared with the difference threshold . If , it is determined that there is fresh water with a similar water volume in the corresponding divided area of the difference value. If , it is determined that there is no irrigation with fresh water in the area corresponding to the difference value.

2. The coastal saline-alkali soil monitoring data acquisition system according to claim 1, wherein: The soil data acquisition module (100) establishes a plane rectangular coordinate system with a corner point of the saline-alkali land as the origin, and senses that the length of the saline-alkali land in the axis direction is , and the length in the axis direction is . The set equidistant division interval is ; Number of divided rows is: , where represents rounding down; the number of divided columns is: .

3. The coastal saline-alkali soil monitoring data acquisition system according to claim 2, wherein: The working principle of the distance calculation method in the soil data collection module (100) is as follows: Sense the detection coordinates in each divided area. If the number of detection coordinates > 1, then calculate the distance between the detection coordinates and the center coordinates of the divided area; The coordinates of the lower left vertex of the perceived division area are , and the coordinates of the upper right vertex are , then the coordinates of the center of the division area are: ; ; The distance between each detection coordinate and the center coordinate of the divided area is: , where is the detection coordinate, is the center coordinate, is the th distance from the detection coordinate to the center coordinate. Retrieve the smallest , which is the historical data of the divided area.

4. The coastal saline-alkali soil monitoring data acquisition system according to claim 2, characterized in that: The calculation formula for the normalization process in the subterranean river shape analysis module (200) is as follows: ； Wherein, when it is the water content after normalization processing, is the minimum value of the water content in the historical data, is the maximum value of the water content in all historical data; when it is the conductivity after normalization processing, is the minimum value of the conductivity in the historical data, is the maximum value of the conductivity in all historical data, and the above conductivity is the average value of the conductivities at different depths.

5. The coastal saline-alkali soil monitoring data acquisition system according to claim 4, wherein: In the graph theory method of the subterranean river shape analysis module (200), the conductivity of the divided areas with similar irrigation fresh water is retrieved, and the conductivity difference threshold is set. If the conductivity difference at the same depth > the conductivity difference threshold, then it is judged that there is a subterranean river under the two corresponding divided areas. Compare them in turn, and connect the subterranean rivers under multiple divided areas, which is the subterranean river shape.

6. The coastal saline-alkali soil monitoring data acquisition system according to claim 1, wherein: The trend analysis method in the fresh water irrigation prediction module (300) analyzes the trend of the conductivity at different depths. When the conductivity changes from decreasing to increasing, the decreasing corresponding detection depth is the effective irrigation depth of fresh water, and its expression is: Perceived total partitioned areas A. For partitioned area A, at depth , the conductivity is denoted as , and the fresh water irrigation volume is denoted as ; when , , it is determined that the conductivity is in the decreasing stage; , it is determined that the conductivity is in the increasing stage, is the effective irrigation fresh water depth of partitioned area A.

7. The coastal saline-alkali soil monitoring data acquisition system according to claim 6, characterized in that: In the irrigation fresh water prediction module (300), the correlation coefficient is used to predict the irrigation water volume required for different irrigation depths when dividing the irrigation fresh water in the area: The integrated effective irrigation depth conductivity is: ; Irrigation fresh water volume ; Calculate the mean conductivity at the effective irrigation depth: , Mean irrigation fresh water volume: ; Correlation coefficient .

8. The coastal saline-alkali soil monitoring data acquisition system according to claim 7, wherein: The irrigation fresh water prediction module (300) also defines the divided area above the subterranean river as divided area B. If in divided area B, the conductivity change trend is the same as that in divided area A, then map divided area B to divided area A. If the conductivity change trend continuously decreases, then it is judged that the fresh water irrigation amount in divided area B is excessive.

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