Assessment Method for the Stability of Agricultural Ecosystems Based on the AFW-BRS Model

Abstract

The present invention provides a method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model, belonging to the technical field of ecological evaluation, including: using Model 1 to simulate the changes in the biomass of each species before and after the use of chemical substances in the agricultural cycle, where Model 1 is an agricultural food web model; using Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of chemical substances, where Model 2 is a BRS evaluation model; using Model 1 to simulate and analyze the changes in the ecosystem after the introduction of species, and using Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of species; obtaining a set of factors affecting the stability of the agricultural ecosystem, and combining the simulation results of Model 1 and Model 2 to determine an organic agriculture strategy; optimizing the organic agriculture strategy when Model 1 does not meet the set standards. Analyze the chain reactions generated by the introduction of other species into the ecosystem, and evaluate the necessity and impact of various chemical substances in the ecosystem.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological assessment, and particularly relates to a method for assessing the stability of an agricultural ecosystem based on the AFW-BRS model. Background Art

[0002] With the growth of the global population and the increasing demand for resources such as food, forest land is often reclaimed into farmland, which has led to great changes in the ecosystem. Initially, agricultural yields may be high, but over time, soil fertility declines, pests and diseases increase, and the dependence on chemical pesticides and fertilizers also grows. Agricultural activities simplify the ecosystem and reduce species diversity. Over time, marginal habitats may gradually recover some ecological functions. In this process, the adoption of sustainable agricultural practices directly affects ecological restoration. Analyzing ecological changes through mathematical modeling and exploring agricultural management practices helps to achieve a balance between agricultural output and ecological sustainability.

[0003] Therefore, the present invention proposes a method for assessing the stability of an agricultural ecosystem based on the AFW-BRS model. Summary of the Invention

[0004] The present invention provides a method for assessing the stability of an agricultural ecosystem based on the AFW-BRS model, which is used to construct an agricultural food web model (AFW) and a BRS assessment model to understand the changes in the ecosystem and promote the sustainable development of agriculture. It can simulate the relative changes in the biomass of producer species before and after the use of chemicals; use the BRS assessment model to calculate the Shannon-Wiener index and the stability index S of the agricultural ecosystem after the application of chemicals. The AFW model can reveal the intricate ecological relationships between different species and accurately present the impact of human intervention on the ecosystem. At the same time, the model can also visualize the mutual relationships between species in the agricultural ecosystem; the model is flexible and can not only analyze the chain reactions generated by introducing other species into the ecosystem, but also evaluate the necessity and impact of various chemicals in the ecosystem. It can comprehensively evaluate the possible impacts of these interference factors on the ecosystem.

[0005] The present invention provides a method for assessing the stability of an agricultural ecosystem based on the AFW-BRS model, including:

[0006] Step 1: Use Model 1 to simulate the changes in the biomass of each species before and after the use of chemicals in the agricultural cycle, where Model 1 is an agricultural food web model, and the agricultural food web model is: a competition model and a Lotka-Volterra model;

[0007] Step 2: Use Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of chemicals, where Model 2 is a BRS assessment model;

[0008] Step 3: Use Model 1 to simulate and analyze the changes in the ecosystem after the introduction of species. Meanwhile, use Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of species.

[0009] Step 4: Obtain the factor set affecting the stability of the agricultural ecosystem, and determine the organic agriculture strategy by combining the simulation results of Model 1 and Model 2.

[0010] Step 5: Conduct a sensitivity analysis on Model 1. When Model 1 meets the set criteria, issue the organic agriculture strategy to the agricultural side. Otherwise, optimize the organic agriculture strategy, where the AFW - BRS model is Model 1 and Model 2.

[0011] Preferably, the competition model consists of a differential equation for the ecological quantity of producers, a differential equation for the weed biomass, and an equation for the impact of chemicals on the species biomass.

[0012] The Lotka - Volterra model consists of the Lotka - Kolmogorov equation, where the Lotka - Kolmogorov equation is used to model the change pattern between the biomass of pests and birds.

[0013] Preferably, the construction of the differential equation for the ecological quantity of producers includes:

[0014] Construct a logistic growth equation for the growth part of producers according to the relative utilization space between producers and the farmland area in the ecosystem, and in combination with the competition relationship between producers and weeds.

[0015] Construct an equation for the impact of seasonal factors on the growth rate of producers using a sine function.

[0016] Construct an equation for the loss of the biomass of producers according to the predation relationship between producers and pests, the influence relationship of seeds during the sowing stage of producers, and the pollution accumulation of pesticides in the soil.

[0017] Based on the stochastic disturbance term of biomass, and in combination with the logistic growth equation, the equation for the impact of growth rate, and the loss equation, construct the differential equation for the ecological quantity of producers.

[0018] Preferably, the construction of the differential equation for the weed biomass includes:

[0019] Construct an equation for the growth part of the weed biomass according to the competition relationship between producers and weeds.

[0020] Construct a weed growth equation according to the impact of seasonal climate.

[0021] Construct a direct impact equation of pesticides on weeds according to the inhibitory relationship of pesticides on weeds.

[0022] Based on the growth part equation, the weed growth equation, and the direct impact equation, a differential equation for weed biomass is constructed.

[0023] Preferably, the construction of the impact equation of a chemical substance on species biomass includes:

[0024] Construct a cumulative equation for the impact of the number of times of applying a medicament on soil pollution;

[0025] Based on the impulse function, construct a biological change amount equation for weeds and pests after applying the medicament;

[0026] Based on a one-dimensional linear function, construct an impact equation of the medicament on bird biomass;

[0027] Based on the cumulative equation, the biological change amount equation, and the impact equation on bird biomass, construct an impact equation of a chemical substance on species biomass.

[0028] Preferably, after constructing the impact equation of a chemical substance on species biomass, it further includes:

[0029] Retrieve the soil pollution concentrations of several detection areas at different agricultural stages in the agricultural cycle from the historical storage database, where the soil pollution concentration includes the first detection concentration after each spraying of the medicament and the second detection concentration at the end of the corresponding preset volatilization period after spraying the medicament;

[0030] Mark according to the medicament spraying time point on the concentration curve, determine the daily light intensity within the preset volatilization period of the marked point, and determine the set post-volatilization concentration of the corresponding medicament type at the corresponding agricultural stage based on the light intensity set according to the stage - medicament type - intensity set - volatilization comparison table;

[0031] Based on the set post-volatilization concentration of each marked point and the medicament concentration difference between the first detection concentration and the second detection concentration, construct a concentration difference array for the same detection area at the corresponding agricultural stage , where, represents the set post-volatilization concentration of the i-th marked point in the u-th agricultural stage; represents the first detection concentration of the i-th marked point in the u-th agricultural stage; represents the second detection concentration of the i-th marked point in the u-th agricultural stage; represents the concentration difference coefficient of the i-th marked point in the u-th agricultural stage; represents the number of times of spraying the medicament involved in the u-th agricultural stage of the corresponding detection area;

[0032] Construct a concentration volatilization difference function according to the concentration difference arrays at different agricultural stages;

[0033] Retrieve the effective duration of the pharmaceutical type corresponding to each marked point from the historical storage database for the first growth density of the producer after the corresponding drug spraying time and the second growth density of the producer before the corresponding pharmaceutical spraying;

[0034] Obtain the historical final growth state of the producer in each agricultural stage from the historical storage database, and determine the growth loss function in combination with the involved first growth density and second growth density;

[0035] ;

[0036] Among them, represents the historical final growth state of the p-th detection area in the u-th agricultural stage; represents the state judgment value of the p-th detection area in the u-th agricultural stage; represents the first growth density corresponding to the i-th marked point of the p-th detection area in the u-th agricultural stage; represents the second growth density corresponding to the i-th marked point of the p-th detection area in the u-th agricultural stage; represents the density judgment function, and ; represents the growth loss function of all detection areas in the u-th agricultural stage; represents the total number of detection areas involved;

[0037] Generate a pollution fine-tuning function based on the growth loss function and the concentration volatilization difference function, and adjust the influence equation of the chemical substance on the species biomass based on the pollution fine-tuning function.

[0038] Preferably, construct a concentration volatilization difference function according to the concentration difference array in different agricultural stages, including:

[0039] Construct a stage volatilization difference function based on all detection areas in the corresponding agricultural stage;

[0040] ;

[0041] According to Adjust the stage volatilization difference function in each agricultural stage, where represents the variance based on all Ru;

[0042] ;

[0043] Among them, represents the variance of the difference coefficient of the p-th detection area in the u-th agricultural stage based on the corresponding concentration difference array ; Indicates the existence of the p-th detection area in the u-th agricultural stage quantity; Indicates the stage volatilization difference function in the u-th agricultural stage determined based on all detection areas; Indicates all the minimum value in; Indicates the concentration volatilization difference function in the u-th agricultural stage obtained after adjusting the corresponding Ru; Indicates the variance threshold.

[0044] Preferably, optimizing the organic agricultural strategy includes:

[0045] Determine the sensitivity index of the first model, and extract the to-be-optimized indicators whose sensitive values do not meet the corresponding set standards from all sensitivity indicators;

[0046] Determine the first influencing factor based on each to-be-optimized indicator from the indicator-factor mapping table;

[0047] Conduct a global strategy analysis on the organic agricultural strategy to determine the involved strategic layout factors;

[0048] Project the strategic layout factors and the first influencing factors into the factor mapping table respectively, obtain the second influencing factors mapped to each strategic layout factor, and determine the sub-adjustment suggestions based on the mapped second influencing factors according to the layout implementation rules;

[0049] Optimize the organic agricultural strategy based on all sub-adjustment suggestions, obtain the optimized agricultural strategy and issue it.

[0050] Compared with the prior art, the beneficial effects of the present application are as follows:

[0051] Construct an agricultural food web model (AFW) and a BRS evaluation model to understand the changes in the ecosystem and promote the sustainable development of agriculture, which can simulate the relative changes in the biomass of producer species before and after the use of chemical substances; use the BRS evaluation model to calculate the Shannon-Wiener index and the stability index S of the agricultural ecosystem after applying chemical substances. The AFW model can reveal the intricate ecological relationships between different species and accurately present the impact of human intervention on the ecosystem. At the same time, the model can also visualize the mutual relationships between species in the agricultural ecosystem; the model is flexible and can not only analyze the chain reactions generated by introducing other species into the ecosystem, but also evaluate the necessity and impact of various chemical substances in the ecosystem. It can comprehensively evaluate the possible impacts of these interference factors on the ecosystem.

[0052] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description and the drawings.

[0053] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0054] The drawings are used to provide a further understanding of the present invention, and constitute a part of the description. They are used in conjunction with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0055] Figure 1 is a flowchart of a method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model in an embodiment of the present invention;

[0056] Figure 2 is a curve graph of the stability change in an embodiment of the present invention;

[0057] Figure 3 is a comparison graph of the producer biomass before and after applying chemical substances in an embodiment of the present invention;

[0058] Figure 4 is a comparison graph of the insect biomass before and after applying pesticides in an embodiment of the present invention;

[0059] Figure 5 is a comparison graph of the bird biomass with and without applying pesticides in an embodiment of the present invention;

[0060] Figure 6 is a comparison graph of the total biomass with and without applying chemical substances in an embodiment of the present invention;

[0061] Figure 7 is a comparison graph of the bird biomass before and after introducing a new consumer in an embodiment of the present invention;

[0062] Figure 8 is a comparison graph of the insect biomass before and after introducing a new consumer in an embodiment of the present invention. Detailed Embodiments

[0063] The preferred embodiments of the present invention will be described below with reference to the drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustration and explanation of the present invention, and are not intended to limit the present invention.

[0064] The present invention provides a method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model, as Figure 1 shown, including:

[0065] Step 1: Use Model 1 to simulate the changes in the biomass of each species before and after the use of chemicals in the agricultural cycle. Among them, Model 1 is an agricultural food web model, and the agricultural food web model is: a competition model and a Lotka - Volterra model;

[0066] Step 2: Use Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of chemicals. Among them, Model 2 is a BRS evaluation model;

[0067] Step 3: Use Model 1 to simulate and analyze the changes in the ecosystem after the introduction of species. At the same time, use Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of species;

[0068] Step 4: Obtain the factor set affecting the stability of the agricultural ecosystem, and combine the simulation results of Model 1 and Model 2 to determine the organic agriculture strategy;

[0069] Step 5: Conduct a sensitivity analysis on Model 1. When Model 1 meets the set criteria, send the organic agriculture strategy to the agricultural side. Otherwise, optimize the organic agriculture strategy. Among them, the AFW - BRS model is Model 1 and Model 2.

[0070] Preferably, the competition model consists of a differential equation for the ecological quantity of producers, a differential equation for the biomass of weeds, and an equation for the impact of chemicals on the biomass of species;

[0071] The Lotka - Volterra model consists of the Lotka - Kolmogorov equation. Among them, the Lotka - Kolmogorov equation is used to model the change pattern between the biomass of pests and birds.

[0072] In this embodiment, the ecosystem is an agricultural system with wheat - corn rotation.

[0073] In this embodiment, the chemical is the pesticide used, and the agricultural agent is a herbicide, an insecticide, etc.

[0074] In this embodiment, based on the competition model and the Lotka - Volterra model, the state of the ecosystem is presented from the perspective of biomass, and the relationship between producers and consumers is analyzed. A sine function is used to simulate the growth rate of producers to reflect seasonal changes. Considering the agricultural cycle, herbicides are applied on the 40th day, 85th day, 210th day, and 339th day, and insecticides are applied on the 20th day, 70th day, 150th day, 235th day, and 331st day, with a dosage of 50 milliliters per acre.

[0075] In this embodiment, a BRS evaluation model was developed based on biomass, diversity, and stability. In this model, the Shannon-Wiener diversity index was introduced to measure ecosystem diversity, and the variance-based stability metric S was adopted. The biomass calculated in Model 1 was used to solve for the diversity index and stability metric.

[0076] In this embodiment, to study the impact of chemical substances on the ecosystem, Model I was used to simulate the changes in the biomass of various species during the agricultural cycle and evaluate the plant health status, and Model II was used to evaluate the ecosystem stability. The results showed that when chemical substances were used, the stress indices of wheat and corn were 0.2 and 0.167 respectively, and the plants were healthy; the average biomasses of insects, bats, and birds were 27.3, 12, and 3 respectively, and the stability index S was 142.9 (<150), indicating good ecosystem stability. When chemical substances were not used, the biomasses of insects, bats, and birds increased to 212.3 and 70.2 respectively, and the stability index was 140.2, and the ecosystem stability was better.

[0077] To explore the impact of introducing rabbits and eagles and replacing chemical substances with bats or frogs on the ecosystem stability, Model 1 was used to analyze the changes in the ecosystem after introducing the species, and Model 2 was used to calculate the stability index. The results were 139.8, 136.6, and 139.2 respectively. The results showed that introducing rabbits and eagles significantly improved the ecosystem stability; in terms of biological control, the ecosystem stability of introducing bats was better than that of introducing frogs.

[0078] In this embodiment, the factor set affecting the stability of the agricultural ecosystem includes: the changing factors of the ecosystem (environment, weather), the influencing factors of organic agricultural practices (pest control, producer health, plant reproduction, biodiversity, cost-benefit), etc.

[0079] In this embodiment, the sensitivity analysis is related to the stability index of the ecosystem. For example, when the growth rates of wheat and corn are 0.3 and 0.2 respectively, the value of the stability index of Model 1 is calculated to be 142.9. When the growth rates of wheat and corn are adjusted from 0.3 and 0.2 to 0.4 and 0.3 respectively, the value of the stability index of Model 1 is calculated to be 137.6. When the growth rates of wheat and corn are adjusted from 0.3 and 0.2 to 0.4 and 0.1 respectively, the value of the stability index of Model 1 is calculated to be 140.2. When the growth rates of wheat and corn are adjusted from 0.3 and 0.2 to 0.1 and 0.1 respectively, the value of the stability index of Model 1 is calculated to be 143.4. When the growth rates fluctuate within a certain range, the total biomass and stability of the ecosystem change little, and the trends are basically the same, indicating that the stability of this model is very good, as specifically Figure 2 shown.

[0080] In this embodiment, the Agricultural Food Web model (AFW) can simulate the relative changes in the biomass of producer species before and after the use of chemicals. For example, Figure 3 As shown, under the action of herbicides and pesticides, the biomass of the two producers fluctuates during the agricultural cycle and then stabilizes at around 1000 kg / m² and 800 kg / m² respectively. Due to seasonal factors, wheat grows slowly in winter and its overall growth rate is lower than that of corn. Due to factors such as weeds, pests, birds, and the accumulation of soil pollution, there is a complex relationship between wheat and producers, so its biomass does not reach the maximum value. In addition, after the application of herbicides, the number of weeds drops sharply to a certain level and then slowly rebounds.

[0081] To study the health status of plants, a stress index is introduced:

[0082] ;

[0083] where SI represents the stress index; is the biomass of the producer under normal conditions; is the biomass of the producer under stress conditions, that is, the biomass of the producer at harvest.

[0084] The stress index (SI) reflects the degree of biomass reduction of the producer when under stress. The larger the SI value, the more severe the stress, the more biomass is lost, and the worse the health status of the producer. When, this index indicates that the producer is not affected by stress and is in a healthy growth state. Calculating the safety index (SI index) for corn and wheat respectively gives: 0.167 compared to 0.2, which indicates that under the influence of herbicides, corn and wheat are in a relatively healthy growth state.

[0085] In this embodiment, the Agricultural Food Web model (AFW) can simulate the relative changes in the biomass of consumer species before and after the use of chemicals. For example, Figure 4 Shown is the comparison of insect biomass before and after the application of pesticides, as Figure 5 shown is the comparison of bird biomass with and without the application of pesticides.

[0086] Due to the predatory relationship between insects and birds, the biomass is dynamically changing. After the application of pesticides, newly hatched insects are quickly killed and some eggs survive. The insect biomass drops sharply to zero and remains unchanged for some time. Due to food shortage, the number of birds then decreases. After the eggs hatch, the insect biomass rebounds and the number of birds gradually increases. However, with the increase in the frequency and dosage of chemical agents, the accumulation of chemicals in birds intensifies, and even though the insect population recovers, the bird biomass ultimately still shows a downward trend.

[0087] After the use of chemicals, the average biomass of insects, bats, and birds was 27.3, 12.3 respectively. Without the use of chemicals, the biomass of insects, bats, and birds increased to 212.3, 70.2 respectively, indicating that the use of chemicals significantly reduced the biomass of insects, bats, and birds.

[0088] In this embodiment, the Shannon-Wiener index and stability index S of the agro-ecosystem after the application of chemicals were calculated using the BRS evaluation model.

[0089] As Figure 6 shown, the overall fluctuations of the ecosystem species show an upward trend. When using chemicals, due to the elimination of weeds and pests, the image values dropped sharply, but over time, due to the stability of the ecosystem's resistance, the values gradually recovered. This indicates that chemicals have a temporary inhibitory effect on ecosystem stability, but ultimately the ecosystem can recover itself.

[0090] By using herbicides and insecticides, the stability index S was calculated to be 142.9 (less than 150), indicating good ecosystem stability; when herbicides and insecticides were not used, the stability index was 140.2, which also indicates good ecosystem stability.

[0091] In this embodiment, the stability index of the agro-ecosystem after introducing rabbits and eagles S was calculated using the BRS evaluation model, and a new ecological balance can gradually be formed.

[0092] As Figure 7 shown and as Figure 8 shown, the introduction of rabbits and eagles changed the biomass of the ecosystem. Rabbits prey on producers, resulting in a decrease in their biomass; rabbits compete with insects for resources, resulting in a decrease in the number of insects. The number of birds also decreased due to the reduction of food insects and being preyed on by eagles.

[0093] The beneficial effects of the above technical solutions are: constructing an agricultural food web model (AFW) and a BRS evaluation model to understand the changes in the ecosystem and promote the sustainable development of agriculture, which can simulate the relative changes in the biomass of producer species before and after the use of chemicals; calculating the Shannon-Wiener index and stability index S of the agro-ecosystem after the application of chemicals using the BRS evaluation model. The AFW model can reveal the intricate ecological relationships between different species and accurately present the impact of human intervention on the ecosystem. At the same time, the model can also visualize the mutual relationships between species in the agro-ecosystem; the model is flexible and can not only analyze the chain reactions generated by introducing other species into the ecosystem but also evaluate the necessity and impact of various chemicals in the ecosystem. It can comprehensively evaluate the possible impacts of these interfering factors on the ecosystem.

[0094] The present invention provides a method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model. The construction of the differential equation of the producer ecological quantity includes:

[0095] Construct a logistic growth equation for the growth part of the producer according to the relative utilization space between the producer and the farmland area in the ecosystem and in combination with the competition relationship between the producer and the weeds.

[0096] Construct an equation for the influence of seasonal factors on the growth rate of the producer using a sine function.

[0097] Construct a loss equation for the biomass of the producer according to the predation relationship between the producer and the pests, the influence relationship of the seeds of the producer in the sowing stage, and the pollution accumulation of the pesticide in the soil.

[0098] Based on the random disturbance term of the biomass and in combination with the logistic growth equation, the equation for the influence of the growth rate, and the loss equation, construct the differential equation of the producer ecological quantity.

[0099] Preferably, the construction of the differential equation of the weed biomass includes:

[0100] Construct an equation for the growth part of the weed biomass according to the competition relationship between the producer and the weeds.

[0101] Construct a weed growth equation according to the influence of seasonal climate.

[0102] Construct a direct influence equation of the pesticide on the weeds according to the inhibitory relationship of the pesticide on the weeds.

[0103] Based on the growth part equation, the weed growth equation, and the direct influence equation, construct the differential equation of the weed biomass.

[0104] Preferably, the construction of the equation for the influence of a chemical substance on the biomass of a species includes:

[0105] Construct an accumulation equation for the soil pollution caused by the number of times of using the pesticide.

[0106] Based on the impulse function, construct an equation for the biological change amount of the weeds and pests after using the pesticide.

[0107] Based on a one-dimensional linear function, construct an equation for the influence of the pesticide on the biomass of birds.

[0108] Based on the accumulation equation, the equation for the biological change amount, and the equation for the influence on the biomass of birds, construct the equation for the influence of the chemical substance on the biomass of the species.

[0109] In this embodiment, the biomass differential model based on the random disturbance term is:

[0110] ;

[0111] wherein, is the biomass of the i-th population at time t; is the growth fraction of the biomass of the i-th population; is Gaussian white noise with a mean of 0 and a variance of a2; is the proportion of biomass loss of the i-th species;

[0112] The logistic growth equation is: ;

[0113] wherein, respectively represent the growth rates of the biomass of wheat and corn; respectively represent the biomass of wheat, corn, and weeds; respectively represent the maximum biomass of wheat, corn, and weeds; respectively represent the competition coefficients of weeds against wheat and corn.

[0114] The equation for the influence of the growth rate is:

[0115] ;

[0116] wherein, respectively represent the maximum growth rates of wheat and corn;

[0117] The equation for biomass loss is: ;

[0118] wherein, represents the predation intensity of pests on producers; represents the predation intensity of birds on the seeds of producers; respectively represent the biomass of pests and birds; represents the influence of soil pollution accumulation on biomass.

[0119] The differential equation for the ecological quantity of producers:

[0120] .

[0121] The equation for the growth part of the weed biomass is:

[0122] ;

[0123] wherein, represents the growth rate of weeds; represents the competition coefficient of producers against weeds;

[0124] The weed growth equation is:

[0125] ;

[0126] wherein, represents the maximum growth rate of weeds;

[0127] The equation for the direct effect of the agent on weeds:

[0128] ;

[0129] where, is the inhibition coefficient of the agent on weeds.

[0130] In this embodiment, the formula for modeling the change pattern between the pest and bird biomasses based on the Lotka - Volterra equation is as follows:

[0131] ;

[0132] where, are the natural growth rates of pests and birds respectively; represents the predation intensity on pests; represents the predation intensity on birds; represents the predation intensity of birds on crop seeds; represent the effects of insecticides and herbicides on the pest and bird biomasses respectively.

[0133] In this embodiment, the cumulative equation for soil pollution is:

[0134] ;

[0135] where, represents the cumulative amount of soil pollution; represent the conversion coefficients respectively; represent the number of uses and the usage amount of insecticides respectively; represent the number of uses and the usage amount of herbicides respectively.

[0136] The equation for the biological change amount of weeds and pests after using the agent is:

[0137] ;

[0138] where, represent the pulse intensities at time respectively; represents the pulse function, and ;

[0139] The equation for the effect of the agent on the bird biomass is:

[0140] ;

[0141] where, is the scaling factor.

[0142] The equation for the impact of chemical substances on species biomass is as follows:

[0143] .

[0144] The beneficial effects of the above technical solution are: An agricultural food web model (AFW) is constructed based on the differential equation of producer ecological quantity, the differential equation of weed biomass, the equation for the impact of chemical substances on species biomass, and the Lotka - Kolmogorov equation, providing a basis for subsequent decision - making.

[0145] The present invention provides a method for evaluating the stability of an agricultural ecosystem based on the AFW - BRS model. After constructing the equation for the impact of chemical substances on species biomass, it further includes:

[0146] Retrieving the soil pollution concentrations of several detection areas at different agricultural stages during the agricultural cycle from the historical storage database, where the soil pollution concentrations include the first detection concentration after each pesticide spraying and the second detection concentration at the end of the corresponding preset volatilization period of the pesticide spraying;

[0147] Marking on the concentration curve according to the pesticide spraying time point, determining the daily light intensity within the preset volatilization period of the marked point, and determining the set volatilized - after concentration of the corresponding pesticide type under the light intensity set for the corresponding agricultural stage based on the stage - pesticide type - intensity set - volatilization comparison table;

[0148] Based on the set volatilized - after concentration of each marked point and the pesticide concentration difference between the first detection concentration and the second detection concentration, constructing a concentration difference array for the same detection area at the corresponding agricultural stage , where represents the set volatilized - after concentration of the i - th marked point in the u - th agricultural stage; represents the first detection concentration of the i - th marked point in the u - th agricultural stage; represents the second detection concentration of the i - th marked point in the u - th agricultural stage; represents the concentration difference coefficient of the i - th marked point in the u - th agricultural stage; represents the number of times of pesticide spraying involved in the u - th agricultural stage of the corresponding detection area;

[0149] Constructing a concentration volatilization difference function based on the concentration difference arrays at different agricultural stages;

[0150] Retrieving the first growth density of the producer after the corresponding pesticide spraying time and the second growth density of the producer before the corresponding pesticide spraying from the historical storage database for the effective duration of the pesticide type corresponding to each marked point;

[0151] Obtain the historical final growth status of producers in each agricultural stage from the historical storage database, and determine the growth loss function in combination with the involved first growth density and second growth density;

[0152]

[0153] Among them, represents the historical final growth status of the p-th detection area in the u-th agricultural stage; represents the status judgment value of the p-th detection area in the u-th agricultural stage; represents the first growth density corresponding to the i-th annotation point of the p-th detection area in the u-th agricultural stage; represents the second growth density corresponding to the i-th annotation point of the p-th detection area in the u-th agricultural stage; represents the density judgment function, and ; represents the growth loss function of all detection areas in the u-th agricultural stage; represents the total number of detection areas involved;

[0154] Generate a pollution fine-tuning function based on the growth loss function and the concentration volatilization difference function, and adjust the influence equation of chemical substances on species biomass based on the pollution fine-tuning function.

[0155] In this embodiment, the historical database contains the soil pollution concentration, growth density, historical final growth status, standard growth status in each stage, etc. before and after each spraying in different detection areas and different agricultural stages, which are all pre-stored, and the detection areas can be different farmland areas. For example, farmland k1, farmland k2, farmland k3, etc. in Village A, and the value of M1 is greater than 20.

[0156] In this embodiment, the agricultural stages include: sowing stage, seedling growth stage, pollination and maturity stage, etc.

[0157] In this embodiment, the three stages of the wheat agricultural cycle correspond to September to October, October to May, and May to June respectively, while the three stages of the corn agricultural cycle correspond to June to July, July to August, and August to September respectively.

[0158] In this embodiment, the detection of soil concentration is based on the measurement by a concentration sensor to determine the residual pesticide concentration in the soil.

[0159] In this embodiment, the preset volatilization periods corresponding to different pesticides are 3 days for some pesticides and 7 days for some pesticides, and their clear records are shown in the instructions of different pesticides.

[0160] In this embodiment, the concentration curve is obtained by plotting all measured concentrations in chronological order for the corresponding detection area at different agricultural stages, and the chemical spraying time point refers to the time when the chemical starts to be sprayed.

[0161] In this embodiment, the stage - chemical type - intensity set - volatilization comparison table contains different chemical types sprayed at different agricultural stages, and the remaining concentration after volatilization of this chemical type under the combined continuous light intensity, that is, the set concentration after volatilization, is pre - stored.

[0162] In this embodiment, what is involved in different agricultural stages is different, but within the same agricultural stage set for different detection areas is the same.

[0163] In this embodiment, there is a concentration volatilization difference function and a growth loss function for each agricultural stage.

[0164] In this embodiment, the producer refers to wheat or corn.

[0165] In this embodiment, the historical final growth state refers to the historical actual growth state at the last moment of the corresponding agricultural stage, and all are pre - collected and stored.

[0166] In this embodiment, the pollution fine - tuning function = (1 - growth loss function of the corresponding agricultural stage / ) × concentration volatilization difference function.

[0167] In this embodiment, from the fine - tuning - adjustment comparison table, obtain the value combinations of the fine - tuning function at different agricultural stages, and match based on this table to obtain the biological adjustment amount. This table contains the value combinations of fine - tuning at different stages and the biological adjustment amounts matched with these combinations, which are pre - set. And the biological adjustment amount is generally between - 2%×Sw and 2%×Sw, where Sw is the biomass after the chemical.

[0168] In this embodiment, the adjustment is: the equation of the influence of the chemical substance on the species biomass + biological adjustment amount.

[0169] The beneficial effects of the above - mentioned technical solution are: starting from the soil pollution concentration and combining with the light intensity to determine the chemical concentration difference and then constructing the concentration volatilization difference function. At the same time, starting from the growth density and growth state of each agricultural stage to determine the growth loss function, and then combining the two to construct the pollution fine - tuning function, so as to effectively adjust the equation of the influence on the species biomass and ensure the accuracy of Model 1.

[0170] The present invention provides an agricultural ecosystem stability assessment method based on the AFW - BRS model. Constructing a concentration volatilization difference function according to the concentration difference array at different agricultural stages, including:

[0171] Construct a stage volatilization difference function based on all detection regions in the corresponding agricultural stage;

[0172]

[0173] According to Adjust the stage volatilization difference function for each agricultural stage, where represents the variance based on all Ru;

[0174]

[0175] where represents the variance of the difference coefficient of the p-th detection region in the u-th agricultural stage based on the corresponding concentration difference array ; represents the number of existing in the p-th detection region in the u-th agricultural stage; represents the stage volatilization difference function in the u-th agricultural stage determined based on all detection regions; represents all the minimum value in; represents the concentration volatilization difference function in the u-th agricultural stage obtained after adjusting the corresponding Ru; represents the variance threshold;

[0176] In this embodiment, takes the value of 0.1.

[0177] The beneficial effects of the above technical solution are: through the stage volatilization difference function of all detection regions based on the same agricultural stage, and then combined with for function adjustment to obtain the concentration volatilization difference function, which provides strong support for the adjustment of biomass.

[0178] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, which optimizes the organic agricultural strategy, including:

[0179] Determine the sensitivity index of the first model, and extract the to-be-optimized index whose sensitive value does not meet the corresponding set standard from all sensitivity indexes;

[0180] Determine the first influencing factor based on each to-be-optimized index from the index-factor mapping table;

[0181] Conduct a global strategy analysis on the organic agricultural strategy to determine the strategy layout factors involved;

[0182] Project the policy layout factors and the first influencing factors into the factor mapping table respectively to obtain the second influencing factors mapped to each policy layout factor;

[0183] Input the same second influencing factor and all the policy local factors related to the same second influencing factor into the layout implementation model to determine the sub-adjustment suggestions for the same second influencing factor;

[0184] Optimize the organic agriculture policy based on all the sub-adjustment suggestions to obtain the optimized agriculture policy and issue it.

[0185] In this embodiment, the sensitivity indicators include not only the stability indicator but also the accuracy indicator. The set standard corresponding to the stability indicator is that the sensitive value of the stability indicator is less than 150.

[0186] The sensitive value of the accuracy indicator is greater than 95%.

[0187] In this embodiment, assume that the sensitive value of the accuracy indicator is 90%. At this time, the accuracy indicator is regarded as the indicator to be optimized.

[0188] In this embodiment, the indicator-factor mapping table includes different sensitivity indicators and the influencing factors matched with the indicators. For example, light factors, humidity factors, temperature factors, etc. At this time, they are regarded as the first influencing factors.

[0189] In this embodiment, the global policy analysis is to extract the parameters of the policy by making decisions on the organic agriculture policy to obtain the policy layout parameters, which are regarded as the policy layout factors and can be directly analyzed and extracted from the organic agriculture policy.

[0190] In this embodiment, the factor mapping table includes the placement positions of different policy layout factors, the placement positions of the first influencing factors, etc. That is, only by placing the factors in the relevant positions of the mapping table can the second influencing factors (a part of the first influencing factors) mapped to the policy layout factors be obtained.

[0191] In this embodiment, the layout implementation model is trained on a neural network model with different combinations of the first influencing factors and different policy layout factors under this factor and the adjustment results as samples. Therefore, the sub-adjustment suggestions for the same second influencing factor can be directly obtained.

[0192] In this embodiment, optimizing is achieved by supplementing all the sub-adjustment suggestions to the organic agriculture policy.

[0193] In this embodiment, the sub-adjustment suggestions are, for example: do not use synthetic fertilizers, pesticides, herbicides and other synthetic substances, or the number of times of using pesticides later should be reduced to half or less of the previous number of times of using pesticides.

[0194] The beneficial effects of the above technical solution are as follows: By determining the index to be optimized, the first influencing factor is determined in combination with the index-factor mapping table, and the second influencing factor is determined in combination with the strategy layout factor and the projection result. Then, the strategy is optimized according to the sub-adjustment suggestions to obtain a more reasonable strategy.

[0195] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model, characterized in that: include: Step 1: using model 1 to simulate the changes in biomass of various species before and after the use of chemical substances in the agricultural cycle, wherein the model 1 is an agricultural food web model, and the agricultural food web model is: a competition model and a LotkaVolterra model; Step 2: using Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of the chemical substance, wherein the Model 2 is a BRS evaluation model; Step 3: Use Model 1 to simulate and analyze the changes in the ecosystem after the introduction of the species. At the same time, use Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of the species. Step 4: Obtain the set of factors that affect the stability of agricultural ecosystems, and determine the organic agricultural strategy by combining the simulation results of Model 1 and Model 2; Step 5: Perform sensitivity analysis on the model 1. When the model 1 meets the set standard, the organic agriculture strategy is sent to the agricultural end. Otherwise, the organic agriculture strategy is optimized. The AFW-BRS model is model 1 and model 2. The competition model is composed of a differential equation of producer ecological quantity, a differential equation of weed biomass, and an equation of the effect of chemical substances on species biomass; The LotkaVolterra model is composed of the Lotka-Kolmogorov equation, wherein the Lotka-Kolmogorov equation is used to model the variation pattern between pest and bird biomass.

2. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 1, characterized in that: The construction of differential equations of producer ecological quantities includes: According to the relative utilization space of producers and farmland areas in the ecosystem, and combined with the competition relationship between producers and weeds, a blocked growth equation for the producer growth part is constructed; Use the sine function to construct an equation for the effect of seasonal factors on producer growth rate; According to the predation relationship between producers and pests, the influence of producers on seeds at the sowing stage, and the accumulation of pesticide pollution on soil, the loss equation of producers' biomass is constructed. Based on the random disturbance term of biomass, and combined with the blocked growth equation, the growth rate influence equation and the loss equation, the differential equation of the producer's ecological quantity is constructed.

3. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 1, characterized in that: Construction of differential equations for weed biomass, including: According to the competition between producers and weeds, the growth equation of weed biomass was constructed; Construct a weed growth equation based on the effects of seasonal climate; According to the inhibitory relationship between pesticides and weeds, the direct effect equation of pesticides on weeds is constructed; Based on the growth part equation, the weed growth equation and the direct impact equation, a differential equation for the weed biomass is constructed.

4. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 1, characterized in that: The construction of equations for the effects of chemicals on species biomass includes: Construct a cumulative equation for soil pollution caused by the number of times the agent is used; Based on the impulse function, the equation of biological change of weeds and pests after using pesticides was constructed; Based on the one-dimensional linear function, the equation of the effect of the drug on the biomass of birds was constructed; Based on the accumulation equation, the biological change equation and the equation for the impact on bird biomass, an equation for the impact of chemical substances on species biomass is constructed.

5. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 4, characterized in that: After constructing the equation for the effect of chemicals on species biomass, it also includes: Retrieving soil pollution concentrations of several detection areas at different agricultural stages in the agricultural cycle from a historical storage database, wherein the soil pollution concentration includes a first detection concentration after each spraying of the pesticide and a second detection concentration of the spraying pesticide at the end of a corresponding preset volatilization cycle; Mark the concentration curve according to the spraying time of the pesticide, determine the light intensity of the marked point every day during the preset volatilization cycle, and determine the set volatilization concentration of the corresponding pesticide type under the agricultural stage of the corresponding marked point based on the light intensity set according to the stage-pesticide type-intensity set-volatilization comparison table; Based on the set volatilization concentration of each marked point and the concentration difference between the first detection concentration and the second detection concentration, a concentration difference array of the same detection area in the corresponding agricultural stage is constructed. ,in, represents the set volatilization concentration of the ith annotation point in the uth agricultural stage; represents the first detected concentration of the i-th annotation point in the u-th agricultural stage; represents the second detected concentration of the i-th annotation point in the u-th agricultural stage; represents the concentration difference coefficient of the ith annotation point in the uth agricultural stage; Indicates the number of times the corresponding detection area is sprayed with pesticides in the u-th agricultural stage; Construct a concentration-volatilization difference function based on the concentration difference arrays under different agricultural stages; Retrieve from the historical storage database the first growth density of the producer after the corresponding drug spraying time and the second growth density of the producer before the corresponding drug spraying time corresponding to the drug type corresponding to each marked point; Obtaining the historical final growth state of the producer at each agricultural stage from the historical storage database, and determining the growth loss function in combination with the first growth density and the second growth density involved; ； in, represents the historical final growth state of the p-th detection area under the u-th agricultural stage; represents the state judgment value of the p-th detection area in the u-th agricultural stage; represents the first growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the second growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the density judgment function, and ; represents the growth loss function of all detected areas under the u-th agricultural stage; Indicates the total number of detection areas involved; Based on the growth loss function and the concentration volatilization difference function, a pollution fine-tuning function is generated, and based on the pollution fine-tuning function, an equation for the influence of chemical substances on species biomass is adjusted.

6. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 5, characterized in that: According to the concentration difference array under different agricultural stages, the concentration volatility difference function is constructed, including: Construct the phase volatilization difference function based on all the detection areas under the corresponding agricultural stages; ； according to The phase volatility difference function under each agricultural stage is adjusted, where: represents the variance based on all Ru; ； in, Indicates the p-th detection area under the u-th agricultural stage based on the corresponding concentration difference array The coefficient of variance of the difference; Indicates that the pth detection area exists in the uth agricultural stage the number of represents the stage volatility difference function under the u-th agricultural stage determined based on all detection areas; Indicates all The minimum value in ; It represents the concentration volatilization difference function under the u-th agricultural stage after adjusting the corresponding Ru; Represents the variance threshold.

7. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 1, characterized in that: Optimizing the organic farming strategy includes: Determine the sensitivity index of the model 1, and extract the index to be optimized whose sensitivity value does not meet the corresponding set standard from all the sensitivity indexes; Determine the first influencing factor based on each indicator to be optimized from the indicator-factor mapping table; Conduct a global strategic analysis of the organic farming strategy to determine the strategic layout factors involved; Projecting the strategic layout factor and the first influencing factor into a factor mapping table respectively, obtaining a second influencing factor mapped with each strategic layout factor, and determining a sub-adjustment suggestion based on the mapped second influencing factor according to a layout implementation rule; The organic farming strategy is optimized based on all sub-adjustment suggestions, and an optimized farming strategy is obtained and issued.

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