Method for predicting income of three-party participation live pig breeding mode

Abstract

The invention belongs to the technical field of agricultural economic management, and discloses a method for predicting income of a three-party participation live pig breeding mode, which comprises the following steps of: 1, acquiring associated variables in the three-party participation live pig breeding mode, and establishing a system dynamics model; and 2, inputting the historical data into the system dynamics model, carrying out analogue simulation in a preset period, and circularly adjusting parameters until the deviation between the output result of the system dynamics model and the historical data is smaller than a preset threshold value. And a third step, constructing a tripartite game matrix, and obtaining a balance point through game analysis. And a fourth step of adjusting a peasant household participation proportion, a breeding enterprise participation proportion and a government participation proportion according to the balance point, inputting the system dynamics model again, and predicting enterprise income, peasant household income and government income. The dynamic model of the system disclosed by the invention has relatively high prediction capability and applicability. The balance point of three-party revenue maximization is determined through cooperation of system dynamics and game analysis, and the revenue can be effectively predicted.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural economic management, and more specifically, the present invention relates to a method for predicting the benefits of a three-party pig farming model. Background Art

[0002] The integrated farming and breeding model for pigs is a sustainable agricultural production and breeding model. This model forms an ecological cycle through the resource utilization of manure and the planting system. Plant products provide feed for pig farming, while waste generated by pig farming is converted into fertilizer or energy for agricultural planting. This ecological agriculture model achieves efficient resource utilization, ecological environmental protection, and the synergistic improvement of economic benefits.

[0003] The integrated farming and breeding model involves farmers, breeding companies, and the government. Farmers earn income by growing corn and providing feed, while breeding companies earn income by raising pigs. The government provides funding or tax support, which benefits from environmental protection and economic growth.

[0004] The integrated farming model not only considers pig farming itself but also considers manure treatment and the full development and utilization of biogas as key factors influencing business decisions. In terms of integration with crop production, the recycling of manure into corn cultivation is also considered. Localizing feed supply significantly reduces the cost of purchasing corn raw materials.

[0005] The integrated farming and breeding model for pigs is characterized by uncertainty in dynamic decision-making and the influence of multiple factors. Maximizing benefits has become a research topic for scholars. To further explore this integrated farming and breeding model, scholars have established dynamic models and used computer-based simulations as a basis for adjustments and decision-making.

[0006] For example, the flow rate basic tree is used to simulate the operation of the ecological agricultural scale production system, identify the causal relationship between the overall state and each component, and test the reliability of the system.

[0007] Another example is leveraging ecological theory and the principles of the food chain, applying the multi-level theory of material circulation within the ecosystem of aquaculture areas and species symbiosis to agricultural production research, establishing a three-dimensional ecological agricultural circular economy model based on the comprehensive utilization of biogas. Research focuses on evaluating the necessity of integrated farming and breeding pig farming models, as well as optimizing and evaluating pig farming models involving the joint participation of enterprises, governments, and farmers. Optimizing and accurately predicting the benefits for all parties involved is fundamental to this research.

[0008] In view of this, there is an urgent need to provide a technical solution to predict the benefits of the three-party participation in pig farming model, establish a prediction model that can accurately reflect the actual situation of the three-party participation in pig farming model, adjust the three-party participation ratio to the optimal value, and provide accurate prediction results. Summary of the Invention

[0009] In order to at least solve one or more of the technical problems mentioned above, the present invention provides a method for predicting the income of a three-party pig farming model, including: a first step, obtaining the associated variables in the three-party pig farming model and establishing a system dynamics model; a second step, inputting historical data into the system dynamics model, performing simulation at a preset period, and cyclically adjusting the parameters until the deviation between the output result of the system dynamics model and the historical data is less than a preset threshold; a third step, obtaining the farmer participation ratio, the breeding enterprise participation ratio, the government participation ratio in the historical data and the enterprise income, farmer income, and government income in the output result of the system dynamics model, constructing a three-party game matrix, and obtaining the equilibrium point through game analysis; a fourth step, adjusting the farmer participation ratio, the breeding enterprise participation ratio, and the government participation ratio according to the equilibrium point, re-inputting the data into the system dynamics model, and predicting the enterprise income, farmer income, and government income.

[0010] According to one embodiment of the present invention, establishing a system dynamics model includes: constructing a causal loop diagram and a stock-flow diagram based on associated variables; wherein the associated variables include at least the participation ratio of farmers, the participation ratio of breeding enterprises, the participation ratio of government, corn yield, corn change, corn price, number of pigs, pig growth rate, pig price, enterprise income, farmer income, and government income.

[0011] According to one embodiment of the present invention, the causal loop diagram includes at least: a planting income causal loop consisting of corn yield, corn change, corn price, and farmer income; a breeding income causal loop consisting of pig number, pig growth rate, pig price, and corporate income; and a cross causal loop formed by the association of the planting causal loop and the breeding causal loop.

[0012] According to one embodiment of the present invention, a causal loop of planting yield is formed by the participation ratio of farmers, the participation ratio of breeding enterprises, the participation ratio of the government, and the change in corn area; a causal loop of breeding yield is formed by the participation ratio of farmers, the participation ratio of breeding enterprises, the participation ratio of the government, and the change in the number of live pigs.

[0013] According to one embodiment of the present invention, the preset period is an integer multiple of the pig breeding period.

[0014] According to one embodiment of the present invention, the preset period is any one of 24 months and 36 months.

[0015] According to one embodiment of the present invention, the preset threshold is 5-10%.

[0016] According to one embodiment of the present invention, the historical data includes the values ​​of multiple variables of the three parties involved in the pig farming model in a preset period.

[0017] According to one embodiment of the present invention, in the second step, historical data of at least two preset periods are included and at least two simulations are performed.

[0018] According to one embodiment of the present invention, in the third step, the equilibrium points of the game analysis do not include a zero-value equilibrium point of a state of invalid work.

[0019] In the present invention, a system dynamics model is established by establishing the associated variables of the three-party participation in the pig farming model, and the output results of the system dynamics model are verified using historical data. The parameters are adjusted to improve the predictive ability and applicability of the system dynamics model, and effectively present the simulation and operation effects under the influence of multiple factors. By adding the three-party participation ratio to the system dynamics model, the influence of the three-party participation ratio on the benefits can be increased. By using the simulation results of the system dynamics model to conduct a game analysis of the three-party participation ratio, the balance point for maximizing the benefits of the three parties can be determined. By re-inputting the results of the game analysis into the system dynamics model, the benefits of the optimized three-party participation in pig farming model can be effectively predicted. The system dynamics model of the present invention has strong predictive ability and applicability. The system dynamics and game analysis are used to collaboratively determine the balance point for maximizing the benefits of the three parties, and the benefits can be effectively predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0021] Figure 1 A schematic diagram showing the steps of a method for predicting the benefits of a three-party pig farming model. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0023] It should be understood that the terms "include" and "comprising" used in the description and claims of the present invention indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the present invention. As used in the specification and claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should further be understood that the term "and / or" as used in the specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0025] As used in this specification and claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0026] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 A schematic diagram showing the steps of a method for predicting the benefits of a three-party pig farming model.

[0028] like Figure 1 As shown, a method for predicting the benefits of a three-party pig farming model includes: a first step S1, obtaining associated variables in the three-party pig farming model and establishing a system dynamics model; a second step S2, inputting historical data into the system dynamics model, performing simulation at a preset period, and cyclically adjusting parameters until the deviation between the output result of the system dynamics model and the historical data is less than a preset threshold; a third step S3, obtaining the farmer participation ratio, the breeding enterprise participation ratio, and the government participation ratio in the historical data and the enterprise income, farmer income, and government income in the output result of the system dynamics model, constructing a three-party game matrix, and obtaining a balance point through game analysis; a fourth step S4, adjusting the farmer participation ratio, the breeding enterprise participation ratio, and the government participation ratio according to the balance point, re-inputting the data into the system dynamics model, and predicting the enterprise income, farmer income, and government income.

[0029] The tripartite pig farming model of this invention involves three types of participants: the pig farming company that conducts the pig farming, the corn farmers who provide the pig feed, and the government that provides subsidies and other related policy support. Different willingness and proportion of participation among the three parties will have different impacts on the benefits of each party.

[0030] In the three-party pig farming model, associated variables refer to variables that can form positive feedback reinforcement loops and negative feedback reinforcement loops, including variables in the pig farming production chain and variables in the corn planting production chain as well as cross-variables between the two.

[0031] Correlated variables were extracted from the positive and negative feedback loops within the pig farming and corn cultivation production chains, as well as the causal relationships between the two. For example, the amount of production input by pig farming enterprises is correlated with economic profit, and the number of pigs produced and profits form a reinforcing positive feedback loop. As the number of pigs farmed increases, the amount of waste biomass generated during the farming process also increases. The increased anaerobic fermentation of biomass improves the energy efficiency of biogas, reduces energy input, and increases profits, which in turn increases the number of pigs farmed, forming a positive feedback loop. Increased anaerobic fermentation also increases biogas manure generated by the farms, which in turn increases the area of ​​corn fields used for biogas manure, increasing corn production. This increase in local feedstock reduces the need for corn imports, significantly increasing corporate profits and boosting the number of pigs farmed, forming a positive feedback loop. Increased corn production in the joint production system increases the amount of corn grown by farmers, boosting their income. Furthermore, increased anaerobic fermentation of biomass reduces ecological pollution in the farming area, improves farmers' environmental satisfaction, and enhances their support for the project. This increases farmers' enthusiasm for production and increases corn production, forming a reinforcing positive feedback loop. In line with environmental protection policies and tax requirements, the government will increase investment and promote the advancement of recycling to strengthen the positive feedback loop.

[0032] After obtaining the associated variables, the profit relationships of each stakeholder are quantitatively analyzed, including quantifiable variables related to profit. In the present invention, the associated variables include at least the farmer participation ratio, the livestock enterprise participation ratio, the government participation ratio, corn yield, corn change, corn price, pig population, pig growth rate, pig price, corporate profit, farmer profit, and government profit. The farmer participation ratio, livestock enterprise participation ratio, and government participation ratio reflect the willingness and level of participation of each participant. Corporate profit, farmer profit, and government profit reflect the profit status of each participant.

[0033] Quantifiable variables related to benefits may also include pollution emissions, biomass pollution, biomass utilization, etc.

[0034] After obtaining the associated variables, causal loop diagrams and stock-flow diagrams are constructed based on the associated variables, thereby establishing a system dynamics model. A causal loop diagram depicts the positive and negative feedback relationships between variables based on the causal relationships between parameters. A stock-flow diagram describes the relationship between stock variables and flow variables.

[0035] In the present invention, the causal loop diagram includes at least: a planting income causal loop consisting of corn yield, corn change, corn price, and farmer income; a breeding income causal loop consisting of pig population, pig growth rate, pig price, and enterprise income; and a cross causal loop formed by the correlation between the planting causal loop and the breeding causal loop. The causal loop diagram also includes a planting yield causal loop consisting of the farmer participation ratio, the breeding enterprise participation ratio, the government participation ratio, and the change in corn area; and a breeding yield causal loop consisting of the farmer participation ratio, the breeding enterprise participation ratio, the government participation ratio, and the change in pig population.

[0036] The cross-causal loop reflects the relationship and mutual influence between corn planting and pig farming. The planting yield causal loop and the breeding yield causal loop reflect the impact of the participant's participation ratio on pig production and corn production.

[0037] After determining the associated variables, based on the causal relationship diagram, measurable node flow rate data is selected to construct a stock-flow diagram. The coefficients in the stock-flow diagram can be set to preset values, for example, based on experience, and then adjusted based on simulation results. The stock-flow diagram incorporates complex causal relationships between breeding and production. For example, corn procurement costs not only influence the area of ​​corn planted in joint production, but also the cost of pig farming. The stock-flow diagram also incorporates causal relationships between the benefits of each participant. For example, for the government, the amount of government subsidies is positively affected by investment attractiveness, which in turn is positively affected by the profits of the breeding enterprise. For breeding enterprises, capital investment in pig farming is positively affected by profits and is proportional to the coverage rate of the joint production model. For farmers, satisfaction is positively influenced by their planting income, their breeding income, and the income of local residents. This satisfaction rate directly influences their willingness to cooperate. The degree to which farmers implement joint production increases the amount of corn they plant and their returns.

[0038] In the second step, the system dynamics model is run to simulate the system. The preset period is set to an integer multiple of the pig farming cycle, such as 24 or 36 months. This approach ensures that the simulation results correspond to the pig farming cycle, facilitating accurate assessment of whether the system dynamics model coefficients need adjustment and by how much.

[0039] Cyclic adjustment involves running multiple simulations using different historical data. After each simulation, the system dynamics model parameters are adjusted based on the deviation between the output and the historical data. Preferably, the parameters are adjusted until the deviation between the output and the historical data is less than 5-10%.

[0040] The historical data includes the values ​​of multiple variables of the three parties involved in the pig farming model in a preset period. These include both input variables, i.e., subsidy intensity, market price, etc., and output variables, i.e., corporate income, farmer income, government income, etc. In the process of adjusting the system dynamics model, the output results can also use variables such as the number of pigs and corn production. Preferably, multiple simulations are performed, and different variables are used as the output results of each simulation. Preferably, historical data of at least two preset periods are included, and at least two simulations are performed. The historical data of the two preset periods may partially overlap or may not overlap at all.

[0041] In the present invention, in order to predict the maximum benefit, when the simulation results of the system dynamics model are close to the historical data, the game analysis method is further used to determine the dynamic stable state in which the benefits of each participant are stable and maximized, as well as the parameter requirements.

[0042] In the third step, the game players are identified as the three parties involved in the pig farming model: farmers, breeding companies, and the government. It is assumed that all three parties are boundedly rational, meaning that each party is able to adjust its participation strategy based on actual circumstances, rather than simply making a single decision and then adjusting it accordingly. The goal is to find a stable equilibrium solution through dynamic game deduction to achieve the optimal decision for each party.

[0043] Although the three parties belong to the same complete industrial chain environment, the information feedback received by each party is delayed in time. Enterprises can obtain market sales feedback information the fastest, farmers' income from corn planting will not be obtained until the end of the planting period, and the government's human, financial and land investment will not be fed back until the summary of each operation period. Therefore, the three parties only have limited and time-delayed information.

[0044] All three parties aim to maximize their own interests and have the ability to continuously learn and make decision adjustments based on actual production and operation conditions, forming a dynamically evolving system.

[0045] For enterprises, there are two strategies to choose from: cooperation and non-cooperation. Cooperation means reaching cooperation with farmers and the government in the production area, purchasing corn as feed raw materials, and providing good jobs, living and production environment; non-cooperation is the opposite.

[0046] For the government side, there are two strategies to choose from: encouragement and discouragement. Encouragement means providing positive support to the enterprise side, such as financial support, project facilitation policies, and land planning approvals; discouragement is the opposite.

[0047] For the farmer side, there are two strategies to choose from: participation and non - participation. Participation means changing the originally planted crops to corn for pig farming to provide a feed source for pig breeding; non - participation is the opposite.

[0048] In the third step, the process of the three - party game analysis is as follows:

[0049] Suppose the decision - making probability of the farmer choosing "participation" is \(x\), and the decision - making probability of "non - participation" is \(1 - x\). The decision - making probability of the enterprise choosing "cooperate" is \(y\), and the decision - making probability of "non - cooperate" is \(1 - y\); the probability of the government choosing "encourage" is \(z\), and the decision - making probability of "discourage" is \(1 - z\).

[0050] Probability assumptions: The operating profit income is \(F\). When the government "encourages" and the farmer "participates", the system revenue increment is \(T_1\); while the revenue increment of the farmer "not participating" is \(T_2\). The investment cost of the government "encouraging" is recorded as \(C_0\), the capital investment provided by the government "encouraging" to the enterprise is \(S_1\), and the capital investment for the land subsidy of the farmer's planted crops is \(S_2\). When the government "discourages" and the enterprise adopts the "cooperate" strategy, the external effect on the profit income is \(N_1\), and when the enterprise adopts the "non - cooperate" strategy and the farmer adopts the "participation" strategy, the external effect on the profit income is \(N_2\).

[0051] Probability assumptions: The normal income of the enterprise is \(Y\). When the enterprise adopts the "cooperate" strategy, when the government adopts the "encourage" strategy and the farmer adopts the "participation" strategy, the increment of the enterprise's operating profit income is \(O_1\), and when the government adopts the "discourage" strategy and the farmer adopts the "participation" strategy, the increment of the enterprise's operating profit income is \(O_2\). When the government adopts the "encourage" strategy and the "discourage" strategy, the enterprise costs are \(C_1\) or \(C_2\) respectively; when the enterprise adopts the "cooperate" strategy, the purchase profit of the farmer is \(K\); \(\beta\) represents the probability of the local farmers' participation in corn planting supply when the enterprise adopts the "non - cooperate" strategy, where \(0\lt\beta\lt1\).

[0052] Probability assumptions: When the enterprise adopts the "cooperate" strategy, the basic profit income of the farmer is \(E_0\). When the enterprise adopts the "non - cooperate" strategy, the basic profit income of the farmer is \(E_1\), and the farmer's cost is \(C_3\); when the farmer adopts the "non - participation" strategy, the profit income is \(E_2\), and the cost is \(C_4\).

[0053] In idealized production, the revenue values are all positive. All the above - assumed parameters are greater than zero, and \(T_1\gt T_2\), \(N_1\gt N_2\), \(O_1\gt O_2\), \(C_2\gt C_1\), \(E_0\gt E_1\), \(E_1 - C_3\lt E_2 - C_4\). Based on the above probability assumptions, the three - party game revenue matrix in the game process can be obtained.

[0054] Table 1 shows the payoff matrix of the three-party game.

[0055] Table 1.

[0056]

[0057] Based on the three-party game matrix, the game dynamic equation analysis is performed for the benefits of the three parties respectively:

[0058] Analysis of the dynamic equation of government replication:

[0059] Let U1 be the expected return of the government choosing the "encouragement" strategy, U2 be the expected return of the government choosing the "non-encouragement" strategy, and U0 be the average expected return of the government, then:

[0060] U1=y(T1x-S2x+F-C0-S1)+(1-y)(T2x-S2x+F-C0)

[0061] =y(T1x-T2x-S1)+T2x-S2x+F-C0

[0062] U2=y(N1x+F)+(1-y)(N2x+F)=xy(N1-N2)+N2x+F

[0063] U0=z[y(T1x-T2x-S1)+T2x-S2x+F+P-C0]+(1-z)xy(N1-N2)+(1-z)N2x

[0064] U1-U0=(1-z)[xy(T1-T2-N1+N2)-yS1+x(T2-S2-N2)-C0]

[0065] F(z)=dz / dt=z(1-z)[xy(T1-T2-N1+N2)-yS1+x(T2-S2-N2)-C0]

[0066] From the stability theorem of the replicated dynamic equation:

[0067] Ⅰ. When y = C0 - x(T2 - S2 - N2) / x(T1 - T2 - N1 + N2) - S1 = y*, F(z) ≡ 0, indicating that z is in a stable state for all probabilities between 0 and 1. That is, no matter how likely the government is to choose "encouragement", the decision at this time is an evolutionarily stable strategy.

[0068] Ⅱ. When 0<y<C0-x(T2-S2-N2) / x(T1-T2-N1+N2)-S1=y*, F′(z)|z=0<0, F′(z)|z=1>0. According to mathematical analysis, z=0 is the evolutionary stable point. That is, when the probability of an enterprise choosing the "cooperation" strategy is less than y*, the government is more likely to make a strategic decision to adopt the "disencouragement" strategy.

[0069] III. When 0<C0-x(T2-S2-N2) / x(T1-T2-N1+N2)-S1=y*<y<1, F′(z)|z=0>0, F′(z)|z=1<0. According to mathematical analysis, z=1 is the evolutionary stable point. That is, when the probability of enterprises choosing the "cooperation" strategy is greater than y*, the government is more likely to make a strategic decision to adopt the "encouragement" strategy.

[0070] In summary, when the government encourages the reduction of investment capital costs, the incremental economic contribution of the benefits generated by taking encouraging decisions increases, and the input S1 provided by the government to enterprises and the input S2 provided to farmers are within a reasonable range that meets the third condition, the government decision-making choice will shift from a "dis-encouragement" strategy to an "encouragement" strategy.

[0071] Analysis of the dynamic equation of enterprise replication:

[0072] Let V1 be the expected return of the enterprise choosing the "cooperation" strategy, V2 be the expected return of the enterprise choosing the "non-cooperation" strategy, and V0 be the average expected return of the enterprise during the operating period, then:

[0073] V1=Y+xz(O1-O2)+x(O2-K-E0)+z(S1-C1+C2)-C2

[0074] V2=xz(βO1-E1)+xY+(1-z)(βO2-E1)+(1-z)Y=xzβ(O1-O2)+xβO2-xE1+Y

[0075] V0=y[Y+xz(O1-O2)+x(O2-K-E0)+z(S1-C1+C2)-C2+(1-y)[xzβ(O1-O2)+xβO2-xE1+Y]

[0076] V1-V0=(1-y)[xz(1-β)(O1-O2)+x(1-β)O2-x(K+E0-E1)+z(S1-C1+C2)-C2]

[0077] G(y)=dy / dt=y(1-y)[xz(1-β)(O1-O2)+x(1-β)O2-x(K+E0-E1)+z(S1-C1+C2)-C2]

[0078] From the stability theorem of the replicated dynamic equation:

[0079] Ⅰ. When x = C2 - z(S1 - C1 + C2) / z(1 - β)(O1 - O2) + (1 - β)O2 - K + E1 - E0, G(y) ≡ 0, indicating that y is in a stable state for all probabilities between 0 and 1. That is, no matter how likely the firm is to choose "cooperation," the decision at this time is an evolutionarily stable strategy.

[0080] Ⅱ. When 0<x<C2-z(S1-C1-C2) / z(1-β)(O1-O2)+(1-β)O2-K+E1-E0=x*, G′(y)|y=0<0, G′(y)|y=1>0. According to the mathematical relationship analysis, y=0 is the evolutionary stable point. That is, when the probability of farmers choosing the "participation" strategy is less than x*, enterprises are more likely to make a "non-cooperative" strategic decision.

[0081] III. When 0<C2-z(S1-C1+C2) / z(1-β)(O1-O2)+(1-β)O2-K+E1-E0=x*<x<1, G′(y)|y=0>0, G′(y)|y=1<0. According to the mathematical relationship analysis, y=1 is the evolutionary stable point. That is, when the probability of farmers choosing the "participation" strategy is greater than x*, enterprises are more likely to make the "cooperation" strategic decision.

[0082] In summary, when an enterprise adopts a "cooperation" strategy, the required operating costs are reduced, the government encourages more investment funds, the financial subsidies or purchase profits provided to farmers are reduced, and the benefits generated by farmers' participation when cooperative decisions are made become larger. Within a reasonable range that meets condition III, the enterprise's decision-making choice will shift from a "non-cooperation" strategy to a "cooperation" strategy.

[0083] Analysis of the dynamic equation of farmer replication:

[0084] Let W1 be the expected return of the farmer who chooses the “participation” strategy, W2 be the expected return of the farmer who chooses the “non-participation” strategy, and W0 be the average expected return of the farmer during the planting cycle, then:

[0085] W1=yz(E0-E1+K)+z(E1+S2-C3)+(1-z)y(E0-E1+K)+(1-z)(E1-C3)

[0086] =y(E0-E1+K)+zS2+E1-C3

[0087] W2=z(E2-C4)+(1-z)(E2-C4)=E2-C4

[0088] W0=x[y(E0-E1+K)+zS2+E1-C3]+(1-x)(E2-C4)

[0089] W1-W0=(1-x)[y(E0-E1+K)+zS2+E1-C3+C4]

[0090] H(x)=dx / dt=x(1-x)[y(E0-E1+K)+zS2+E1-C3+C4]

[0091] From the stability theorem of the replicated dynamic equation:

[0092] Ⅰ. When z = E2 - E1 - C4 + C3 - y(E0 - E1 + K) / S2 = z*, H(x) ≡ 0, indicating that x is in a stable state for all probabilities between 0 and 1. That is, no matter how high the probability of farmers choosing "participation", the decision at this time is an evolutionarily stable strategy.

[0093] II. When 0<z<E2-E1-C4+C3-y(E0-E1+K) / S2=z*, H′(x)|x=0<0, H′(x)|x=1>0. According to mathematical analysis, x=0 is the evolutionary stable point. That is, when the probability of the government choosing the "encouragement" strategy is less than z*, farmers are more likely to make the "non-participation" strategy decision.

[0094] III. When 0<z<E2-E1-C4+C3-y(E0-E1+K) / S2=z*<z<1, H′(x)|x=0>0, H′(x)|x=1<0. According to mathematical analysis, x=1 is the evolutionary stable point. That is, when the probability of the government choosing the "encouragement" strategy is greater than z*, farmers are more likely to make the "participation" strategy decision.

[0095] When the subsidies or purchase profits received by farmers increase and the basic profit benefits obtained from "non-participation" decrease, within a reasonable range that meets the third condition, farmers' decision-making choices will shift from the "non-participation" strategy to the "participation" strategy.

[0096] According to the above analysis, the three sets of replicated dynamic equations are composed into a dynamic evolution system. Let F(z) = 0, G(y) = 0, H(x) = 0 and calculate the local equilibrium points of the dynamic system: P1(0,0,0), P2(1,0,0), P3(0,1,0), P4(0,0,1), P5(1,1,0), P6(1,0,1), P7(0,1,1), P8(1,1,1). For these 8 equilibrium points, there is the Jacobian matrix:

[0097]

[0098] in:

[0099] F′ x=z(1-z)[y(T1-T2-N1+N2)+T2-S2-N2]

[0100] F′ y =z(1-z)[x(T1-T2-N1+N2)-S1]

[0101] F′ z =(1-2z)[xy(T1-T2-N1+N2)-yS1+x(T2-S2-N2)-C0]

[0102] G′ x =y(1-y)[z(1-β)(O1-O2)+(1-β)O2-K+E1-E0]

[0103] G′ y =(1-1y)[xz(1-β)(O1-O2)+x(1-β)O2-x(K+E0-E1)+z(S1-C1+C2)-C2]

[0104] G′ z =y(1-y)[x(1-β)(O1-O2)+S1-C1+C2]

[0105] H′ x =(1-2x)[y(K+E0-E1)+zS2+E1-E2-C3+C4]

[0106] H′ y =x(1-x)(K+E0-E1)

[0107] H′ z =x(1-x)S2

[0108] By analyzing the stability of each equilibrium point, the three-party evolutionary game system involving enterprises, governments, and farmers always has an evolutionary stable point (0,0,0). At this point, the system is in an inoperative state, unable to ultimately achieve a comprehensive agricultural production model of large-scale pig farming with enterprise production, farmer planting, and government support. Therefore, in this invention, the equilibrium points analyzed in the game do not include the zero-value equilibrium point of the inoperative state.

[0109] For the equilibrium point (1,0,1), if T2-S2-N2-C0>0, (1-β)O1+S1+E1-E0-C1-K<0, E1-E2-C3+C4+S2>0 are satisfied at the same time, that is, when the eigenvalues ​​λ1, λ2, and λ3 are all negative, this point is a stable point. At this time, although the enterprise does not choose an active cooperation strategy and cannot maximize the economic benefits of farmers' corn planting, the government's investment cannot see investment returns through farmers' planting in the short term, which will reduce the government's willingness to support investment.

[0110] For the equilibrium point (1,1,0), if T1-S1-S2-N1-C0<0, (1-β)O2+E1-E0-C2-K>0, E0-E2+K-C3+C4>0 are satisfied at the same time, that is, when the eigenvalues ​​λ1, λ2, and λ3 are all negative, this point is a stable point, that is, farmers choose to participate, pig production enterprises actively cooperate, and the government does not encourage. The collaborative cooperation between the two parties can increase profits to a certain extent, but due to the lack of government encouragement, operating costs increase, so this stable state is not ideal.

[0111] For the equilibrium point (1,1,1), if T1-S1-S2-N1-C0>0, (1-β)O1+S1+E1-E0-C1-K>0, and S2+E0-E2-C3+C4+K>0 are satisfied at the same time, that is, when the eigenvalues ​​λ1, λ2, and λ3 are all negative, this point is a stable point, that is, with government encouragement, corporate cooperation, and farmer participation, the benefits of each participant are stable and maximized. This is the most ideal dynamic stable state of the system.

[0112] In the fourth step, the participation ratios of farmers, breeding enterprises and the government can be adjusted according to the equilibrium point so that they can evolve to the equilibrium point (1,1,1), which is the most ideal stable state.

[0113] For example, based on the flow position and flow rate pairs of the three-party game model among farmers, enterprises, and the government, a stock-flow diagram of the three-party decision-making is drawn. The system dynamics modeling software Vensim PLE is used to perform numerical simulations to demonstrate the dynamic evolution of the three-party game strategy. Historical data or current data is input to determine whether it can evolve to an equilibrium point within a preset time period, and the participation ratio of each party is adjusted in real time. Once the participation ratio of each party is determined, it is input as a fixed value into the system dynamics model obtained in the second step. Enterprise revenue, farmer revenue, and government revenue are used as outputs to obtain the revenue of the three-party pig farming model.

[0114] In the present invention, a system dynamics model is established by establishing the associated variables of the three-party participation in the pig farming model, and the output results of the system dynamics model are verified using historical data. The parameters are adjusted to improve the predictive ability and applicability of the system dynamics model, and effectively present the simulation and operation effects under the influence of multiple factors. By adding the three-party participation ratio to the system dynamics model, the influence of the three-party participation ratio on the benefits can be increased. By using the simulation results of the system dynamics model to conduct a game analysis of the three-party participation ratio, the balance point for maximizing the benefits of the three parties can be determined. By re-inputting the results of the game analysis into the system dynamics model, the benefits of the optimized three-party participation in pig farming model can be effectively predicted. The system dynamics model of the present invention has strong predictive ability and applicability. The system dynamics and game analysis are used to collaboratively determine the balance point for maximizing the benefits of the three parties, and the benefits can be effectively predicted.

[0115] Although a number of embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art may devise numerous modifications, variations, and alternatives without departing from the concept and spirit of the present invention. It should be understood that in practicing the present invention, various alternatives to the embodiments of the present invention described herein may be employed. The appended claims are intended to define the scope of the present invention and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for predicting the benefits of a three-party pig farming model, characterized in that: include: The first step is to obtain the associated variables in the three-party pig farming model and establish a system dynamics model; In the second step, the historical data is input into the system dynamics model, a simulation is performed at a preset period, and parameters are adjusted cyclically until the deviation between the output result of the system dynamics model and the historical data is less than a preset threshold; The third step is to obtain the farmer participation ratio, the breeding enterprise participation ratio, the government participation ratio in the historical data, and the enterprise income, farmer income, and government income in the output results of the system dynamics model, construct a three-party game matrix, and obtain the equilibrium point through game analysis; The fourth step is to adjust the farmer participation ratio, the breeding enterprise participation ratio, and the government participation ratio according to the balance point, re-input the system dynamics model, and predict the enterprise income, farmer income, and government income.

2. The method according to claim 1, characterized in that Building a system dynamics model involves: constructing a causal loop diagram and a stock-flow diagram based on the associated variables; Among them, the associated variables include at least the participation ratio of farmers, the participation ratio of breeding enterprises, the participation ratio of government, corn yield, corn change, corn price, number of pigs, pig growth rate, pig price, enterprise income, farmer income, and government income.

3. The method according to claim 2, characterized in that The causal loop diagram at least includes: The causal loop of planting benefits consists of corn yield, corn change, corn price, and farmers' income; The causal loop of breeding income consisting of pig numbers, pig growth rate, pig prices, and corporate profits; A cross-causal loop formed by the connection between the planting causal loop and the breeding causal loop.

4. The method according to claim 3, characterized in that The causal loop diagram also includes: The causal loop of planting yield is composed of the participation ratio of farmers, the participation ratio of breeding enterprises, the participation ratio of government, and the change in corn acreage; The causal loop of breeding output is composed of the participation ratio of farmers, the participation ratio of breeding enterprises, the participation ratio of government, and the change in the number of live pigs.

5. The method according to claim 1, wherein The preset period is an integer multiple of the pig breeding period.

6. The method according to claim 5, characterized in that The preset period is any one of 24 months and 36 months.

7. The method according to claim 1, characterized in that The preset threshold is 5-10%.

8. The method according to claim 1, characterized in that The historical data includes the values ​​of multiple variables of the three parties involved in the pig farming model during the preset period.

9. The method according to claim 1, characterized in that In the second step, historical data of at least two preset periods are included, and at least two simulations are performed.

10. The method according to claim 1, characterized in that In the third step, the equilibrium points of the game analysis do not include a zero-value equilibrium point of a state of ineffective work.

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