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Abstract
Grain security is one of the most important issues worldwide. Many developing countries, including China, have adopted the Agriculture Support Price (ASP) program to stimulate farmers’ enthusiasm for growing grain, to ensure self-sufficiency in grain and the stable development of the grain market. To propose decision support for the government in designing a more reasonable support price in the ASP program, we formulate an agent-based model to simulate the operation of the wheat market in the harvest period. To formulate the formation process of the market price influenced by farmers’ expected sale price, processors’ expected purchase price, and the ASP, the time series and regression methods are adopted. Based on the proposed market price model, to quantitatively analyze the grain transaction process and the ASP program’s impacts on market agents, we develop an agent-based simulation model to describe the adaptive evolution and interaction among market agents. Furthermore, we validate and implement the simulation model with public wheat market data. Finally, insights and suggestions about the decision of the ASP program are provided.
Keywords
Agent-based simulation (ABS)
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wheat market
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agriculture support price
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decision support
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public policy
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Jingsi Huang, Fan Zhang, Jie Song, Wei Li.
An Agent-based Simulation Model of Wheat Market Operation: The Benefit of Support Price.
Journal of Systems Science and Systems Engineering, 2022, 31(4): 437-456 DOI:10.1007/s11518-022-5527-7
| [1] |
AkkayaD, BimpikisK, LeeH. Government interventions to promote agricultural innovation. Manufacturing & Service Operations Management, 2021, 23(2): 437-452
|
| [2] |
AlizamirS, IravaniF, MamaniH. An analysis of price vs. revenue protection: Government subsidies in the agriculture industry. Management Science, 2019, 65(1): 32-49
|
| [3] |
BalmannA. Farm-based modelling of regional structural change: A cellular automata approach. European Review of Agricultural Economics, 1997, 24(1): 85-108
|
| [4] |
BlundellR, StokerT M. Models of aggregate economic relationships that account for heterogeneity. Handbook of Econometrics, 2007, 6: 4609-4666
|
| [5] |
BorodinV, BourtembourgJ, HnaienF, LabadieN. Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 2016, 254(2): 348-359
|
| [6] |
Box G E, Jenkins G M, Reinsel G C, Ljung G M (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
|
| [7] |
BrändleJ M, LangendijkG, PeterS, BrunnerS H, HuberR. Sensitivity analysis of a land-use change model with and without agents to assess land abandonment and long-term re-forestation in a Swiss mountain region. Land, 2015, 4(2): 475-512
|
| [8] |
BrusaferriA, MatteucciM, PortolaniP, VitaliA. Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices. Applied Energy, 2019, 250: 1158-1175
|
| [9] |
ChangX, LiJ, RodriguezD, SuQ. Agent-based simulation of pricing strategy for agri-products considering customer preference. International Journal of Production Research, 2016, 54(13): 3777-3795
|
| [10] |
DantwalaM L. Incentives and disincentives in Indian agriculture. Indian Journal of Agricultural Economics, 1967, 22(902-2016-67198): 1-25
|
| [11] |
FAO (2020). Migrant workers and the covid-19 pandemic. Food and Agriculture Organization of the United Nations. Tech. Rep.
|
| [12] |
Fox K A (1956). The contribution of farm price support programs to general economic stability. Policies to Combat Depression 295–356.
|
| [13] |
GagliardiD, NigliaF, BattistellaC. Evaluation and design of innovation policies in the agro-food sector: An application of multilevel self-regulating agents. Technological Forecasting and Social Change, 2014, 85: 40-57
|
| [14] |
GuptaS, DawandeM, JanakiramanG, SarkarA. Distressed selling by farmers: Model, analysis, and use in policy-making. Production and Operations Management, 2017, 26(10): 1803-1818
|
| [15] |
HappeK, KellermannK, BalmannA. Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior. Ecology and Society, 2006, 11(1): 49
|
| [16] |
HeZ, XiongJ, NgT S, FanB, ShoemakerC A. Managing competitive municipal solid waste treatment systems: An agent-based approach. European Journal of Operational Research, 2017, 263(3): 1063-1077
|
| [17] |
HuangJ, SongJ. Optimal inventory control with sequential online auction in agriculture supply chain: An agent-based simulation optimisation approach. International Journal of Production Research, 2018, 56(6): 2322-2338
|
| [18] |
HuangJ, YangG. Understanding recent challenges and new food policy in China. Global Food Security, 2017, 12: 119-126
|
| [19] |
HyndmanR J, KhandakarY. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 2008, 27(3): 1-22
|
| [20] |
KazazB, WebsterS, YadavP. Interventions for an artemisinin-based malaria medicine supply chain. Production and Operations Management, 2016, 25(9): 1576-1600
|
| [21] |
KazemA, SharifiE, HussainF K, SaberiM, HussainO K. Support vector regression with chaos-based fire-fly algorithm for stock market price forecasting. Applied Soft Computing, 2013, 13(2): 947-958
|
| [22] |
LeeT R, ChengH F. Application of gray theory to predict prices of agricultural products — A case of Adzuki beans. Journal of Agriculture and Forestry-Taichung, 2000, 49(2): 83-92
|
| [23] |
LiJ, LiuW, SongZ. Sustainability of the Adjustment Schemes in China’s Grain Price Support Policy — An Empirical Analysis Based on the Partial Equilibrium Model of Wheat. Sustainability, 2020, 12(16): 6447
|
| [24] |
Li X (2009). Chinese soybean producers lose out [EB/OL], 2009, http://www.chinadaily.com.cn/bizchina/2009-04/20/content_7693661.htm Accessed April 20, 2009.
|
| [25] |
Lopez R E (1986). Structural models of the farm household that allow for interdependent utility and profit maximization decisions. Agricultural Household Models-Extensions, Applications, and Policy: 306–325.
|
| [26] |
Macal C M, North M J (2006). Tutorial on agent-based modeling and simulation part 2: How to model with agents. Proceedings of the 2006 Winter Simulation Conference.
|
| [27] |
MacalC M, NorthM J. Tutorial on agent-based modelling and simulation. Journal of Simulation, 2010, 4(3): 151-162
|
| [28] |
Mahmood I, Arabnejad H, Suleimenova D, Sassoon I, Marshan A, Serrano-Rico A, Louvieris P, Anagnostou A, Taylor S J, Bell D, Groen D (2020). FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions. Journal of Simulation: 1–19.
|
| [29] |
MaL L, JinY P, ZhangA L. Analysis on the influencing factors of grain price fluctuation in China. Price: Theory & Practice, 2011, 10: 23-24
|
| [30] |
Meter K (2006). Evaluating farm and food systems in the US. Systems Concepts in Evaluation: An Expert Anthology: 141–159.
|
| [31] |
Nakajima C (2012). Subjective Equilibrium Theory of the Farm Household. Elsevier.
|
| [32] |
NegahbanA, YilmazL. Agent-based simulation applications in marketing research: An integrated review. Journal of Simulation, 2014, 8(2): 129-142
|
| [33] |
PeiJ H, LiY P. Analysis of wheat production cost and income in hebei province. Chinese Journal of Agricultural Resources and Regional Planning, 2015, 36(7): 41-45
|
| [34] |
Ramaswami B, Seshadri S, Subramanian K V (2018). The welfare economics of storage-based price supports. Working Paper.
|
| [35] |
ReardonT, MishraA, NuthalapatiC S, BellemareM F, ZilbermanD. COVID-19’s disruption of India’s transformed food supply chains. Economic and Political Weekly, 2020, 55(18): 18-22
|
| [36] |
SauvageauG, FrayretJ M. Waste paper procurement optimization: An agent-based simulation approach. European Journal of Operational Research, 2015, 242(3): 987-998
|
| [37] |
Sengupta J K (1985). Risk in supply response: An econometric application. Information and Efficiency in Economic Decision: 171–198.
|
| [38] |
SubbaraoD, GovernorR B I. The challenge of food inflation. Reserve Bank of India Bulletin, 2011, 55: 2029-2039
|
| [39] |
TangC S, WangY, ZhaoM. The implications of utilizing market information and adopting agricultural advice for farmers in developing economies. Production and Operations Management, 2015, 24(8): 1197-1215
|
| [40] |
TripathiA K. Agricultural price policy, output, and farm profitability — examining linkages during post-reform period in India. Asian Journal of Agriculture and Development, 2012, 10(1362-2016-107639): 91-111
|
| [41] |
UtomoD S, OnggoB S, EldridgeS. Applications of agent-based modelling and simulation in the agri-food supply chains. European Journal of Operational Research, 2018, 269(3): 794-805
|
| [42] |
VarshneyD, RoyD, MeenakshiJ V. Impact of COVID-19 on agricultural markets: Assessing the roles of commodity characteristics, disease caseload and market reforms. Indian Economic Review, 2020, 55(1): 83-103
|
| [43] |
WanX M, LuoA J. Analysis on the causes and effects of unstable grain prices. International Economics and Trade Research, 2007, 23(1): 41-46
|
| [44] |
WossenT, BergerT. Climate variability, food security and poverty: Agent-based assessment of policy options for farm households in Northern Ghana. Environmental Science & Policy, 2015, 47: 95-107
|
| [45] |
XuJ, HuangE, HsiehL, LeeL H, JiaQ S, ChenC H. Simulation optimization in the era of Industrial 4.0 and the Industrial Internet. Journal of Simulation, 2016, 10(4): 310-320
|
| [46] |
XuJ L, ChuY F, FengL. Analysis on the differences and influencing factors of grain selling behavior of different scale farmers — Based on the survey data of 320 farmers in Anhui province. Rural Economy, 2018, 11: 12-19
|
| [47] |
YuW, JensenH G. Trade policy responses to food price crisis and implications for existing domestic support measures: The case of China in 2008. World Trade Review, 2014, 13(4): 651-683
|
| [48] |
YuanH B, OuyangT. Research on the correlation between the agriculture support price and farmers’ income: Taking Hunan province as an example. Journal of Hunan Agricultural University (Social Science), 2011, 12(3): 6-10
|
| [49] |
ZhangG Q. Response and income effect of farmers’ grain storage and sale behavior under the policy of minimum grain purchase price. Agricultural Economy, 2014, 7: 86-112
|
| [50] |
ZhouY H, ZouL G. Study on the price relationship between Chinese soybean futures market and national soybean futures market. Journal of Agrotechnical Economics, 2007, 1: 55-62
|
| [51] |
ZhuD. Analysis on the change of farmers’ grain selling behavior in major grain production areas. Economy and Management, 2011, 25(5): 10-13
|
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