Method, device and equipment for calculating fertilization amount and nutrition loss amount of crop soil

Abstract

The invention provides a crop soil fertilization amount and nutrition loss amount calculation method, and belongs to the field of ecological environment, and the method comprises the steps: carrying out the grid division of a to-be-planted region of a target point crop according to a set grid unit size; according to the sequence of the related parameters, solving the space-time optimized planting distribution quantity of different planted crops, and obtaining the crop planting distribution on the space grid units of the to-be-planted area; spatial interpolation is carried out according to the phenological starting isolines of different crops, and phenological starting time values of different planted crops in different growth phenological stages are obtained; according to the information and the water consumption and fertilization intensity numerical values of each crop in each phenological stage, the water demand and fertilization amount of soil in different grids and different planting and growing processes of each crop are obtained through calculation; and calculating the nutrient loss amount of the crop soil under different time scales according to the solved fertilization amount, the accumulated fertilization amount in the soil and the rainfall amount required by each crop in different planting and growing processes. According to the method, the time difference and the space difference of growth rhythms of crops generated along with the change of geographic positions are effectively considered, and the calculation result is more accurate.

Description

Technical Field

[0001] The present invention belongs to the field of ecological environment, and in particular relates to a method, device and equipment for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil. Background Art

[0002] Soil fertilization and loss are mainly related to various factors such as crop fertilization process, rainfall process, plant absorption rate, slope of fertilized land, soil permeability, field management, etc. At present, the calculation of agricultural non-point source pollution loads in larger areas is mainly based on emission coefficient algorithms or model algorithms with rainfall-driven loss equations as the core, and the calculation time scale is mainly annual or monthly.

[0003] The analysis of agricultural pollution loads primarily involves calculating regional fertilizer inputs and regional losses. Currently, the method for calculating agricultural non-point source fertilizer application is to estimate fertilizer demand based on the different planting areas and fertilizer requirements of the crops grown in the region. This typically involves estimating the demand for pure nitrogen, phosphorus, and potassium on an annual timescale. For example, this algorithm is relatively simple to use based on the net application rate of each fertilizer type and the total sown area of ​​each crop. Three typical methods for calculating nutrient losses after fertilization are available: Direct observation, which is accurate and reliable but labor-intensive, requires more resources and is difficult to scale for large areas and long periods of observation. The ECM (output coefficient model) has advantages: it simplifies the formation of non-point source pollution and reduces reliance on monitoring data; its disadvantage is that the fixed output coefficient can have large errors. The third type of method is the mechanism model calculation method. The advantage of this type of method is that it mainly studies the foundation and basis of the mechanism process and has good rationality. The disadvantage is that it requires more input data, a large workload, and certain requirements for the professional skills of the model user.

[0004] If you want to solve the specific and refined fertilization intensity for different crops, different growth stages and different regions, you must quantitatively solve the phenological distribution, fertilizer demand, soil nutrient accumulation, metabolic level and other aspects of each crop in different geographical regions to meet the requirements.

[0005] The main problems in the current calculation of agricultural non-point source pollution loads are as follows: (1) The temporal differences in the growth rhythm (phenology) of the same crop in a large area due to changes in geographical location are basically not taken into account in the calculation of the amount of fertilizer applied in the region; (2) The temporal and spatial differences in the fertilization requirements of crops at different stages are not taken into account. If the differences in fertilization under these growth-driven conditions are not taken into account, large errors may occur when solving the agricultural non-point source pollution load emissions. Summary of the Invention

[0006] In order to solve the problem that the existing technology basically does not take into account the temporal and spatial differences in the growth rhythm (phenology) of the same crop in a large area as it changes with geographical location when calculating the amount of fertilizer applied in a region, which may lead to large errors in solving the agricultural non-point source pollution load emissions, the present invention provides a method, device and equipment for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil comprises the following steps:

[0009] Divide the target crop planting area into grids according to the set grid unit size;

[0010] According to the crop planting suitability score ranking, the order of crop planting area size, and the planting suitability score order of each grid unit in the administrative region, the spatiotemporal optimal planting allocation of different planting crops for each crop in the unit grid and the preset time scale is solved until the allocated district and county-level planting area is consistent with the district and county-level planting space statistical value, and finally the crop planting distribution on the spatial grid unit of the to-be-planted area is obtained;

[0011] According to the phenological starting contour lines of different crops, spatial interpolation is performed, and the phenological starting time values ​​of different crops in different growth phenological stages are obtained by using the inverse distance linear interpolation algorithm;

[0012] Based on the crop planting distribution on the spatial grid cells of the planting area, the phenological start time values ​​of different crops in different growth phenological stages, and the collected water consumption and fertilization intensity data of each crop in each phenological stage, the soil water requirement, fertilizer amount, crop utilization amount and cumulative fertilizer amount in the soil of each crop during the planting and growth process are calculated;

[0013] The nutrient loss of crop soil at different time scales was calculated based on the soil water demand, fertilizer application, crop utilization, accumulated fertilizer application in the soil, and grid interpolated rainfall during the planting and growth of each crop.

[0014] Preferably, the phenological start time values ​​of different crops at different growth phenological stages are obtained by using the inverse distance linear interpolation algorithm, specifically:

[0015] Vphen(x 1000 ,y 1000 ,i,k)=r1.Vphen(x1,y1,i,k)+r2.Vphen(x2,y2,i,k)

[0016] Among them, Vphen(x 1000 ,y1000 ,i,k) are the coordinates of the center of the 1000m grid unit (x 1000 ,y 1000 ) The time of the kth phenological stage of the i-th crop, i = 1, ..., 22; k = 1, ..., NumPhen(i); NumPhen(i) is the phenological stage classification number of the i-th crop; Vphen(x1, y1, i, k) and Vphen(x2, y2, i, k) are the phenological time values ​​of this type of crop corresponding to the two nearest contour nodes (x1, y1) and (x2, y2) on the two adjacent contour lines. The distances between the two nodes and the interpolation node are S1 and S2 respectively. The weight coefficients are solved as follows:

[0017] Preferably, the specific method for obtaining the crop planting distribution on the spatial grid units of the area to be planted includes:

[0018] Collect statistical data sets of different crop planting amounts in different years at the district and county levels from agricultural statistics departments;

[0019] Collect and process digital elevation datasets, soil datasets, and land use datasets within unit grids at different times, and solve the terrain and slope distribution data of the unit grid based on these data;

[0020] Convert the district and county administrative unit polygons into DTM attribute data at a spatial scale of 1000m;

[0021] The planting areas of various crops in each district and county are obtained, and the crop planting suitability scores of the grids in the to-be-planted area are sorted using the bubble method according to the terrain and slope distribution data of the unit grid, the DTM attribute data, and the planting areas of various crops in each district and county. The most suitable crop types in the grids of the to-be-planted area are allocated according to the crop planting suitability and crop planting area in the grids from large to small. For crops that rotate multiple times within a year, they are allocated by month within the year, and finally the crop planting distribution on the spatial grid of the to-be-planted area is obtained.

[0022] Preferably, it also includes using remote sensing interpretation data of typical crops in local areas or target areas to update statistical data of various crops in various districts and counties.

[0023] Preferably, the different time scales include annual scale, monthly scale, ten-day scale and daily scale, and the calculation formula for soil nutrient loss on the annual scale is:

[0024] L SE =Q S ·C N / P ·η

[0025] Where Q Sis the average annual sediment yield in the basin, C N / P is the total nitrogen and total phosphorus concentrations in the soil corresponding to the average annual sediment yield, and η is the enrichment ratio of the soil to total nitrogen and total phosphorus;

[0026] The calculation formula for the monthly soil nutrient loss is:

[0027]

[0028] Where Q S (m) is the average monthly sediment yield in the basin, C N / P (m) is the total nitrogen and total phosphorus concentrations in the soil corresponding to the average monthly sediment yield, and η is the enrichment ratio of the soil to total nitrogen and total phosphorus;

[0029] Q S =X·SDR

[0030] X=R·K·L·S·C·P

[0031]

[0032] Among them, X is the average soil loss or erosion rate, R is the rainfall erosion factor, K is the soil erosion factor, L is the slope length factor, S is the slope factor, C is the vegetation cover management factor, and P is the soil and water conservation factor; P i is the average monthly rainfall, s a is the sand content; s i is the silt content; C l is the clay content; C o is the organic carbon content, λ is the slope length, m is the slope length index, θ is the slope angle, NDVI is the vegetation planning index, SDR is the ratio of sediment transported into the river under natural erosion conditions, is the measured sediment flux at each basin control station, A S is the corresponding basin area of ​​the control station, X y is the average annual soil loss within the basin.

[0033] The present invention also provides a device for calculating regional fertilizer application and loss in crop soil, comprising:

[0034] A grid division module is used to divide the target crop planting area into grids according to the set grid unit size;

[0035] The spatial partitioning module is used to solve the spatiotemporal optimal planting allocation of different crops for each unit grid and preset time scale according to the order of crop planting suitability scores, crop planting area size, and planting suitability scores of each grid unit in the administrative region, until the allocated district and county-level planting area is consistent with the district and county-level planting space statistical value, and finally obtain the crop planting distribution on the spatial grid units of the to-be-planted area;

[0036] The phenological division module is used to perform spatial interpolation based on the phenological starting contours of different crops, and obtain the phenological starting time values ​​of different crops in different growth phenological stages through the inverse distance linear interpolation algorithm;

[0037] The fertilizer amount acquisition module is used to calculate the soil water requirement, fertilizer amount, crop utilization amount and accumulated fertilizer amount in the soil of each crop during the planting and growth process based on the crop planting distribution on the spatial grid units of the planting area, the phenological start time values ​​of different crops in different growth phenological stages, and the collected water consumption and fertilizer intensity data of each crop in each phenological stage;

[0038] The loss acquisition module is used to calculate the nutrient loss of crop soil at different time scales based on the soil water demand, fertilizer application, crop utilization, cumulative fertilizer application in the soil and grid interpolation rainfall of each crop during the planting and growth process.

[0039] The present invention also provides a computer device comprising a memory and a processor, wherein the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory to implement a method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil as described in any one of the above items.

[0040] The present invention also provides a computer-readable storage medium, which is used to store computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement a method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil as described in any of the above items.

[0041] The method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil provided by the present invention has the following beneficial effects:

[0042] The present invention is based on the grid division of the target point crop planting area, and realizes the spatiotemporal optimization allocation of different planting crops in the unit grid and preset time scale for each crop in the target area according to the crop planting suitability score ranking, the crop planting area size order and the planting suitability score order of each grid unit in the administrative area. At the same time, the planting range and the phenological contour line coverage area, as well as the phenological contour line interpolation space range, are determined according to the crop planting distribution in the planting area. The phenological start time values ​​of different planting crops in different growth phenological stages are obtained by linear interpolation algorithm, which solves the problem that the number and spacing of phenological contour lines are relatively large, the data are relatively sparse, and some crops are not planted nationwide. Direct interpolation using contour line sampling points will result in the interpolation space range crossing the boundary and some interpolation results being unreasonable. Taking into account the temporal and spatial differences in the growth rhythm of crops due to changes in geographical location, the calculated soil water and fertilizer requirements of each crop during the planting and growth process, as well as the nutrient loss of crop soil at different time scales, are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0044] Figure 1 A flow chart of the method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil provided by the present invention;

[0045] Figure 2 A schematic diagram of the dynamic changes in fertilizer placement in the Yangtze River region during different time periods according to an embodiment of the method of the present invention;

[0046] Figure 3 This is a second schematic diagram of the dynamic change results of fertilizer placement in the Yangtze River region at different time periods according to an embodiment of the method of the present invention;

[0047] Figure 4 Three schematic diagrams showing the dynamic changes in fertilizer placement in the Yangtze River region during different time periods according to the method embodiment of the present invention

[0048] Figure 5 Four schematic diagrams of the dynamic changes in fertilizer application in the Yangtze River region at different time periods according to an embodiment of the method of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0050] Method Example

[0051] The present invention provides a method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil, specifically Figure 1 As shown, the following steps are included:

[0052] S1. Divide the target crop planting area into grids according to a set grid unit size.

[0053] S2. According to the order of crop planting suitability scores, the order of crop planting area sizes, and the order of planting suitability scores of each grid unit in the administrative region, solve the spatiotemporal optimized planting allocation of different planting crops for each crop in the unit grid and the preset time scale, until the allocated district and county-level planting area is the same as the district and county-level planting space statistical value, and finally obtain the crop planting distribution on the spatial grid units of the area to be planted.

[0054] S3. Perform spatial interpolation based on the phenological starting contour lines of different crops, and obtain the phenological starting time values ​​of different crops in different growth phenological stages through the inverse distance linear interpolation algorithm.

[0055] S4. Based on the crop planting distribution on the spatial grid units of the area to be planted, the phenological start time values ​​of different crops in different growth phenological stages, and the collected water consumption and fertilization intensity data of each crop in each phenological stage, the soil water requirement, fertilizer amount, crop utilization amount and cumulative fertilizer amount in the soil of each crop during the planting and growth process are obtained by calculation.

[0056] S5. Calculate the nutrient loss from crop soil at different time scales based on the soil water demand, fertilizer application, crop utilization, accumulated fertilizer application in the soil, and grid interpolated rainfall during the planting and growth process of each crop.

[0057] The calculation method of the present invention is described in detail below, and the specific implementation process is as follows:

[0058] (1) Construct a 1000m grid distribution dataset of the target area planting space and solve it.

[0059] The spatial distribution of planting of different crops is very important for agricultural yield, irrigation water volume, fertilizer application, agricultural economy, agricultural non-point source estimation, etc. There are currently two main types of data involving the spatial distribution of agricultural planting. One is agricultural planting and harvest statistics based on administrative units; the other is agricultural planting data on regional agricultural soil blocks or grids based on interpretation of different remote sensing images. Taking into account the integrity of the data, the spatial distribution of planting used in this application mainly adopts the statistical results data of districts and counties across the country. With the advancement of science and technology in remote sensing and interpretation, the spatiotemporal dynamic distribution of crops in a certain area obtained through remote sensing interpretation has been studied and applied to a certain extent in recent years. At the same time, the two types of methods complement each other and can be transformed from top to bottom and bottom to top. For example, by combining data such as county planting statistics, soil and geomorphology, land use, meteorological conditions, rotation system or habits, river network distribution and population density, a corresponding top-down decomposition algorithm can be designed to solve the optimal configuration data of different crop planting spaces under the premise that the total area of ​​planted crops is equal, and then the corresponding field survey data can be used for necessary verification. On the contrary, the planting space distribution data interpreted by remote sensing can also be calculated by regional summation and compared and verified using statistical data of administrative regions. Considering that large-scale remote sensing interpretation data is difficult to obtain, this application uses county-scale statistical data from the agricultural department to design a crop planting space matching algorithm based on planting suitability scoring to solve the DTM (Digital Terrain Model) data set of different planted crops at a 1000m grid and 10d (day) time scale. The 1000m grid is a grid of 1000m×1000m in size. In the next step, remote sensing interpretation data of relevant key areas can be used to update the corresponding planting space.

[0060] The specific implementation steps are:

[0061] 1) Collect statistical data sets of different crop planting data at the district and county levels from agricultural statistical departments in different years, which are the main target data for planting allocation.

[0062] 2) Collect and process 1000m grid land use datasets for different periods; 1000m terrain distribution or slope distribution data.

[0063] 3) The polygonal electronic data of county administrative units were converted into administrative district number DTM (Digital Terrain Model) with a grid resolution of 1000m using ARCGIS software. This data set is mainly used to record the plane coordinates (x, y) of elements and other physical properties. These DTM data are prepared to facilitate the subsequent solution of various planting problems and regional statistics (such as regional planting area summation, planting suitability and planting allocation) in each administrative district.

[0064] Combining statistical data on various crops, soil topography, land use, meteorological conditions, crop rotation systems or practices, river network distribution, and population density from the agricultural department across various counties and districts, a top-down algorithm was designed. This algorithm, based on the statistical values ​​or totals of regional parameters, determined the specific distribution of regional parameters across each grid cell within the region. This algorithm, primarily based on a crop planting spatial matching algorithm ranked by planting suitability scores, determined the optimal spatiotemporal allocation of each crop across a 1000-meter grid and a 10-day timescale, following the order of crop planting area and the planting suitability scores of each grid cell within the administrative region. This allocation ensured that the planting area allocated to a particular crop at the county level matched the planting statistics for the corresponding crop within the county, as provided by the agricultural department. Ultimately, the optimal planting space allocation was determined to meet the total crop area.

[0065] In order to improve the accuracy of spatial configuration, the remote sensing interpretation data of typical crops in local areas or target areas can be used to replace the crop planting spatial allocation solution in step (3), and other statistical crops can be reallocated or adjusted to ensure that the planting time and spatial allocation of major crops or key areas are closer to the actual situation.

[0066] Based on statistical data on crop planting area by district and county administrative area, spatial optimization allocation is performed based on data such as land use, crop rotation system, and grid slope. A bubble method is used to solve the district and county planting area ranking based on the size of the planting area. The results of the sorting based on the planting scale are then used to calculate the grid distribution of planting suitability for each crop. The most suitable crop type is then prioritized based on the grid planting suitability. For crops that rotate multiple times within a year, monthly allocation is performed within the year. Similarly, the spatiotemporal distribution of crop planting on a 1000m spatial grid is solved, as shown in the figure. The main purpose of the district and county planting area ranking is to first select the primary crops in the district and county, and then prioritize the spatial allocation calculation for the (1000m×1000m) grid. This prioritizes the spatial allocation of the primary crops before calculating the spatial allocation of the secondary crops.

[0067] (2) Grid interpolation is performed on the typical crop phenology contour lines in the region to generate 1000m grid data of different crop phenology.

[0068] After consulting relevant books, articles and other data sets, and taking into account the relatively stable characteristics of large-scale regional climate and data integrity, the present invention mainly adopts the phenological contour map in a certain agricultural phenological atlas document. The phenological contour line is also called the isophenoline, which refers to the line on the map connecting the locations where the developmental stages of a certain plant arrive at the same time. After the unified spatial coordinate system (converted from Clarke_1866_Albers to equal-area projection), data format conversion (converted from e00 file to shp file, from line to point and polygon, etc.), and based on the representativeness and importance of regional crops, 22 different representative crops were selected. Based on the collected electronic data of the phenological spatial distribution contour lines of 22 different representative crops in the target area (such as rice, winter wheat, corn, potato, etc.),

[0069] Considering that the number and spacing of phenological contour lines are relatively large, the data are relatively sparse, and some crops are not planted nationwide, directly using these contour line sampling points for interpolation will result in the interpolation space range crossing the boundary and some interpolation results being unreasonable, and will affect the subsequent establishment of the spatial distribution of fertilizer application at different times. Therefore, the present invention first determines the interpolation space range through the planting range and the contour line coverage area, and then generates contour line node data based on the regional phenological date contour line, and converts it into time numerical nodes through programming, and then designs a linear interpolation algorithm and implements phenological interpolation within the restricted area through programming. The specific solution idea is: select the linear interpolation of the two nearest grid nodes of adjacent contour lines or the two nearest grid nodes on the same side of the adjacent contour lines. The calculation formula of the specific linear interpolation algorithm is as follows:

[0070] Vphen(x 1000 ,y 1000 ,i,k)=r1.Vphen(x1,y1,i,k)+r2.Vphen(x2,y2,i,k) (1)

[0071] Where, Vphen(x 1000 ,y 1000 ,i,k) are the coordinates of the center of the 1000m grid unit (x 1000 ,y 1000) The time of the kth phenological stage of the i-th crop can be a time value or the number of days in a year, i = 1, ..., 22, representing the crop type; k = 1, ..., NumPhen(i); NumPhen(i) is the total number of phenological grades of the i-th crop, and its value is generally between 3 and 9, which is mainly related to the growth process of the crop; Vphen(x1, y1, i, k) and Vphen(x2, y2, i, k) are the phenological time values ​​of this type of crop corresponding to the two nearest nodes (x1, y1) and (x2, y2) on the two adjacent contour lines and the distance between the two nodes and the interpolation node is S1 and S2 respectively, and their weight coefficients are: Based on the above results, the sub-phenology of double early rice in the 1000m grid of the target area was solved.

[0072] This algorithm was used to generate a phenological dataset covering 3-9 growth phenological stages for 22 different crops at the center of a 1000m grid in the target area. The dataset, in the ArcGIS raster ASC format, contains 101 ASC files totaling 17.7GB. This dataset provides a comprehensive data foundation for subsequent analysis of the spatial distribution of crops, corresponding phenological data, and irrigation and fertilization rates.

[0073] The phenological sparse contour lines of 22 crops across the country were digitized, and the main boundaries of crop distribution were established. The sub-phenological contour lines were converted into discrete points. The contour line algorithm of formula (1) was used to automatically solve the time starting value of each phenological stage of each crop in the center of each grid unit, providing key basic data for the next stage of solving the water consumption, nitrogen, phosphorus and potassium fertilizer requirements of crops in this area and at different life stages.

[0074] (3) Based on crop phenology, crop water requirements and fertilizer application amount, the grid data of the cumulative amount of fertilizer per unit area at each 10-day node is solved.

[0075] Through a series of references and web searches, the water requirements per unit area (m2) for each crop at different growth phenological stages (e.g., winter wheat has eight phenological stages: sowing - overwintering - greening - tillering - jointing - heading - grain filling - harvesting; double-season early rice has six phenological stages: sowing - transplanting - tillering - heading - grain filling - harvesting) were summarized. 3 .mu -1 .d -1 ), converted to pure nitrogen fertilizer (kg.ha -1 .d -1 ), converted to pure phosphate fertilizer (kg.ha -1 .d -1 ), pure potassium fertilizer (kg.ha -1 .d -1) Attribute data on crop phenology varies with geographic location and altitude. In the actual solution process, phenology is dynamically solved directly using interpolation results. This attribute data provides the necessary support for the next step of solving the refined water and fertilizer requirements of crops planted in the region at different phenological stages.

[0076] After the crop planting and phenological grid map of each grid (1000m×1000m) is solved, the planting distribution of each grid in the country and its sub-phenological start time value, sub-phenological duration value, and water consumption, nitrogen, phosphorus and potassium fertilizer requirements of each phenological stage of the crop can be calculated based on the grid map. Figure 5 As shown in the figure, the phenological time in the formal solution process will change with the changes in space and elevation. The dynamic distribution of phosphorus fertilizer during the planting and growth process is finally solved as follows: Figure 2-Figure 5 As shown (taking the Yangtze River Basin as an example). Figure 2-Figure 5 It can be seen that in the Yangtze River Basin, due to the effective consideration of the types of crops and their specific temporal and spatial distribution differences in phenology (grid resolution is 1000m, and the current temporal resolution accuracy is 10d (days), the calculated temporal distribution of phosphorus fertilizer application is more dynamic and can reflect the differences in fertilization of the same crop in different regions and at different altitudes, which is a better innovation than previous related studies.

[0077] (4) Algorithm for multi-time scale problem of nutrient loss based on soil loss equation: year-month-ten-day.

[0078] 1) Annual scale solution model.

[0079] At present, the mainstream soil and water loss equation mainly uses the annual scale international soil loss equation or modified model for solution. Its main calculation formula is as follows. Because the sub-phenology of crops and their different growth stages have significantly different requirements for water and fertilizer application. At the same time, due to the significant seasonal distribution of rainfall in the Yangtze River, using the annual average value to calculate soil and water loss or nutrient loss cannot fully reflect the actual annual dynamic process of physical and chemical processes. Among them, the soil erosion process adopts the international RUSLE formula, whose main form is:

[0080] X=R·K·L·S·C·P(2)

[0081] Where X is the average soil loss or erosion rate (t.ha -1 .a -1 ), R is the rainfall erosion factor (MJ.mm.ha -1 .h - 1 .a -1 ), K is the soil erosion factor (t.ha.h.ha-1 .MJ -1 .mm -1 ), L slope length factor (dimensionless number), S slope factor (dimensionless number), C vegetation cover management factor (dimensionless number), P soil and water conservation factor. The expression of R is as follows:

[0082]

[0083] P i is the average monthly rainfall (mm), i is the month, and the soil erodibility factor K is calculated using the method proposed by Sharply:

[0084]

[0085] in, s a is the sand particle (0.05-2.0mm) content (%); s i is silt (0.002-0.05mm) mm content (%); C l is the clay content (<0.002mm) (%); C o is the organic carbon content (%).

[0086] The expression of L is as follows:

[0087]

[0088] Where λ is the slope length (m), which can be solved by the ARCGIS hydrological analysis module using the regional DEM analysis and calculation, m is the slope length index (dimensionless), and θ is the slope angle (°). When θ>5°, m=0.5; when 3.5≤θ≤5°, m=0.4; when 1.0°≤θ≤3.5°, m=0.3; when θ≤1.0°, m=0.2. The expression for S is:

[0089]

[0090] The expression of C is as follows: NDVI is a vegetation planning index. The values ​​of P are shown in Table 1:

[0091] Table 1 P factor values

[0092] Land cover Crop land woodland grassland water bodies urban land Unused land P 0.5 0.9 0.9 0.0 1.0 1.0

[0093] Total nitrogen, total phosphorus and other nutrients

[0094] L SE =Q S ·C N / P ·η(7)

[0095] Q Sis the average annual sediment yield in the basin, and its expression is: Q S =X·SDR, where X represents the annual soil erosion in the statistical area and SDR is the proportion of sediment transported into rivers under natural erosion conditions. Its expression is:

[0096]

[0097] Where, is the cumulative sediment flux (t) measured at each basin outlet control station during the year, A S is the corresponding basin area of ​​the control station, X y C is the annual soil loss intensity within the basin. N / P is the concentration of total nitrogen and total phosphorus in the soil (%, g / 100g), η is the enrichment ratio of total nitrogen and total phosphorus in the soil, and its value is 1.77.

[0098] 2) Monthly scale solution mode.

[0099] In the monthly scale solution process, the main consideration is the influence of the changes in rainfall or runoff processes within the monthly process, and the influence of the accumulation and loss of soil nutrients can be appropriately ignored. Therefore, it is only necessary to consider the average nitrogen and phosphorus content of the soil solved monthly by the present invention into the annual process solution mode process (calculation formula (2), formula (7)), and then use the monthly rainfall or control to account for the proportion of the monthly average flow to the annual average to solve the monthly sediment and nitrogen and phosphorus loss process.

[0100]

[0101] Among them, Q S (m) is the measured sediment flux at each basin outlet control station in the mth month, C N / P (m) is the concentration of total nitrogen, total phosphorus, etc. in the soil in the mth month (%, g / 100g).

[0102] 3) Ten-day and daily scale solution mode.

[0103] When solving models on the ten-day and daily time scales, the specific impacts of the dynamic accumulation and release of pollutants need to be considered.

[0104] The present invention mainly makes full use of or collects data on existing typical crop phenology libraries, different planting space distributions, crop fertilization libraries, etc. in the national region, and sorts them according to the crop planting suitability scores, the order of crop planting area sizes, and the planting suitability scores of each grid unit in the administrative region, to achieve the spatiotemporal optimization of different planting crops in the unit grid and preset time scale for each crop in the country or other large regions.

[0105] The present invention determines the planting range and the coverage area of ​​phenological contour lines, as well as the interpolation space range of phenological contour lines according to the crop planting distribution in the area to be planted, and obtains the phenological start time values ​​of different crops in different growth phenological stages through a linear interpolation algorithm. This solves the problems that the number and spacing of phenological contour lines are relatively large, the data are relatively sparse, and some crops are not planted nationwide. Directly using contour line sampling points for interpolation will cause the interpolation space range to exceed the boundary and some interpolation results to be unreasonable.

[0106] The present invention uses the above-mentioned spatiotemporal optimization allocation of crops and linear interpolation algorithm to achieve refined solutions to crop water consumption, fertilizer application, fertilizer loss process, and monthly time scale processes in the whole country or other large regions based on set unit grids, laying the foundation for solving agricultural fertilizer demand, loss pollution load and corresponding non-point source model at a large regional scale.

[0107] Based on the same inventive concept, the present invention provides a device for calculating the regional fertilization amount and loss amount of crop soil, including a grid division module, a space division module, a phenological division module, a fertilization amount acquisition module and a loss amount acquisition module.

[0108] Specifically, the grid division module is used to divide the target crop's planting area into grids according to a set grid cell size. The spatial division module is used to solve the spatiotemporal optimized planting allocation of different crops for each crop within the grid cell and a preset time scale, sorting by crop planting suitability score, crop planting area size, and the planting suitability score order of each grid cell within the administrative region, until the allocated district-level planting area is consistent with the district-level planting spatial statistical value, ultimately obtaining the crop planting distribution within the spatial grid cells of the planting area. The phenological division module is used to perform spatial interpolation based on the phenological start contours of different crops, and obtain the phenological start time values ​​of different crops at different growth phenological stages using the inverse distance linear interpolation algorithm. The fertilizer amount acquisition module is used to calculate the soil water and fertilizer requirements of each crop during the planting and growth process based on the crop planting distribution within the spatial grid cells of the planting area, the phenological start time values ​​of different crops at different growth phenological stages, and the collected water consumption and fertilizer intensity values ​​of each crop at each phenological stage. The loss acquisition module is used to calculate the nutrient loss of crop soil at different time scales based on the amount of fertilizer applied to the soil, the accumulated fertilizer application in the soil, and the rainfall during the planting and growth of each crop.

[0109] Each module in the aforementioned device for calculating regional fertilizer application and loss in crop soil may be implemented in whole or in part via software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0110] The present invention provides a computer device comprising a memory and a processor, wherein the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory to implement a method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil as described above, wherein the method has been described in detail in the method embodiment and will not be repeated here.

[0111] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions, the instructions can be executed by a processor of a computer device to complete the above method. For example, the non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, the steps in the embodiment of the method for calculating the regional fertilization amount and loss amount of crop soil can be implemented. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may provide methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0116] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification and examples have described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are included in the scope of protection of the patent for the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil, characterized in that: The steps include: Divide the target point crop planting area into grids according to the set grid unit size; According to the crop planting suitability score ranking, the crop planting area size order and the planting suitability score order of each grid unit in the administrative region, the spatiotemporal optimal planting allocation of each crop in the unit grid and the preset time scale is solved until the allocated county-level planting area is the same as the county-level planting space statistical value, and finally the crop planting distribution on the spatial grid unit of the area to be planted is obtained; According to the starting contours of different crop phenology, spatial interpolation is performed, and the starting time values ​​of different crops at different growth phenological stages are obtained by using the inverse distance linear interpolation algorithm; Based on the crop planting distribution on the spatial grid units of the planting area, the phenological start time values ​​of different crops in different growth phenological stages, and the collected water consumption and fertilization intensity data of each crop in each phenological stage, the soil water requirement, fertilizer amount, crop utilization and cumulative fertilizer amount in the soil of each crop during the planting and growth process are calculated; The nutrient loss of crop soil at different time scales was calculated based on the soil water demand, fertilizer application, crop utilization, cumulative fertilizer application in the soil and grid interpolation rainfall of each crop during the planting and growth process.

2. The method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil according to claim 1, characterized in that: The phenological start time values ​​of different crops at different growth phenological stages are obtained by the inverse distance linear interpolation algorithm, specifically: Vphen(x 1000 ,y 1000 ,i,k)=r1.Vphen(x1,y1,i,k)+r2.Vphen(x2,y2,i,k) Among them, Vphen(x 1000 ,y 1000 ,i,k) are the coordinates of the center of the 1000m grid unit (x 1000 ,y 1000 ) The time of the kth phenological stage of the i-th crop, i = 1, ..., 22; k = 1, ..., NumPhen(i); NumPhen(i) is the phenological stage classification number of the i-th crop; Vphen(x1, y1, i, k) and Vphen(x2, y2, i, k) are the phenological time values ​​of this type of crop corresponding to the two nearest contour nodes (x1, y1) and (x2, y2) of the interpolation node and on two adjacent contour lines, respectively. The distances of the two nodes from the interpolation node are S1 and S2, respectively, and their weight coefficients are solved as follows:

3. The method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil according to claim 1, characterized in that: The specific method for obtaining the crop planting distribution on the spatial grid unit of the to-be-planted area includes: Collect statistical data sets of different crop planting quantities in different years at the district and county level from agricultural statistical departments; Collect and process digital elevation data sets, soil data sets and land use data sets in unit grids at different periods, and solve the terrain and slope distribution data of unit grids based on each data; Convert the district and county administrative unit polygons into DTM attribute data at a spatial scale of 1000m; The planting areas of various crops in each district and county are obtained, and the crop planting suitability scores in the grids to be planted are sorted by the bubble method according to the terrain and slope distribution data of the unit grid, the DTM attribute data and the planting areas of various crops in each district and county. The most suitable crop types in the grids of the to-be-planted area are allocated according to the crop planting suitability and crop planting area in the grids from large to small. For crops that rotate multiple times within a year, they are allocated by month within the year, and finally the crop planting distribution on the spatial grid of the to-be-planted area is obtained.

4. The method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil according to claim 3, characterized in that: It also includes the use of remote sensing interpretation data of typical crops in local areas or target areas to update statistical data of various crops in each district and county.

5. The method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil according to claim 3, characterized in that: The different time scales include annual scale, monthly scale, ten-day scale and daily scale. The calculation formula of soil nutrient loss at the annual scale is: 50 SE =Q S ·C N / P ·η In the formula, Q S is the average annual sediment yield in the basin, C N / P is the total nitrogen and total phosphorus concentrations in the soil corresponding to the average annual sediment yield, and η is the enrichment ratio of soil to total nitrogen and total phosphorus; The calculation formula for the soil nutrient loss on a monthly scale is: In the formula, Q S (m) is the average monthly sediment yield in the basin, C N / P (m) is the total nitrogen and total phosphorus concentrations in the soil corresponding to the average monthly sediment yield, and η is the enrichment ratio of the soil to total nitrogen and total phosphorus; Among them, X is the average soil loss or erosion rate, R is the rainfall erosion factor, K is the soil erosion factor, L is the slope length factor, S is the slope factor, C is the vegetation cover management factor, and P is the soil and water conservation factor; P i is the average monthly rainfall, s a is the sand content; s i is the silt content; C l C is the clay content; o is the organic carbon content, λ is the slope length, m is the slope length index, θ is the slope angle, NDVI is the vegetation planning index, SDR is the proportion of sediment transported into the river under natural erosion conditions, is the measured sediment flux at each basin control station, A S is the corresponding basin area of ​​the control station, X y is the average annual soil loss within the basin.

6. A device for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil, characterized in that: include: A grid division module is used to divide the target point crop planting area into grids according to a set grid unit size; The spatial division module is used to solve the spatiotemporal optimal planting allocation of different crops in the unit grid and the preset time scale according to the crop planting suitability score sorting, the crop planting area size order and the planting suitability score order of each grid unit in the administrative area, until the allocated district and county level planting area is the same as the district and county level planting space statistical value, and finally obtain the crop planting distribution on the spatial grid unit of the area to be planted; The phenological division module is used to perform spatial interpolation based on the phenological starting contours of different crops, and obtain the phenological starting time values ​​of different crops in different growth phenological stages through the inverse distance linear interpolation algorithm; The fertilizer amount acquisition module is used to calculate the soil water requirement, fertilizer amount, crop utilization and accumulated fertilizer amount in the soil of each crop during the planting and growth process according to the crop planting distribution on the spatial grid units of the planting area, the phenological start time values ​​of different crops in different growth phenological stages and the collected water consumption and fertilizer intensity data of each crop in each phenological stage; The loss acquisition module is used to calculate the nutrient loss of crop soil at different time scales based on the soil water demand, fertilizer amount, crop utilization, cumulative fertilizer amount in the soil and grid interpolation rainfall of each crop during the planting and growth process.

7. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory to implement a method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement a method for calculating the amount of fertilizer applied and the amount of nutrient loss in crop soil as described in any one of claims 1-5.

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