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Academic Journal of Agricultural Sciences, 2022, 3(2); doi: 10.38007/AJAS.2022.030203.

The Regional Spread of Wheat, Corn and Other Agricultural Products

Author(s)

Yan Ma

Corresponding Author:
Yan Ma
Affiliation(s)

Accounting and Finance, Xi’an Peihua University, Xi’an 710000, Shaanxi, China

Abstract

Price stability has a bearing on the well-being of people and is one of the main goals of China's macroeconomic policy. As an important leading indicator of changes in the overall price level, excessive changes in the price of agricultural products will have an important impact on the overall price level, which will adversely affect people's production and life and affect the stable operation of the national economy. The difficulty of buying and selling agricultural products in China is becoming increasingly prominent, which seriously affects farmers' income and the stability of agricultural economic order. As an indispensable component of the price mechanism, agricultural product price transmission is of great significance for the timely discovery of the difficulty of buying and selling in the process of commodity transactions. Based on the above background, the research content of this article is the study of agricultural product regional price differences and regional price fluctuations. This article uses wheat as an example, with the help of modern economics theories and research methods, starting from the supply and demand relationship of the wheat market in China and external shock factors, to establish an analytical framework for the fluctuation and impact of wheat prices in China, by analyzing the causes and mechanisms of wheat price fluctuations, The mechanism, mode and degree of influence of the main influencing factors are clarified, and the transmission effect of wheat price fluctuations in China and the supply response behavior of wheat producers are analyzed on this basis. Finally, experimental simulations show that the correlation coefficient between Beijing and Shijiazhuang, Hefei and Nanjing is the largest, which is 0.991, and the correlation coefficient between Zhengzhou and Guangzhou is the smallest, which is 0.957. It can be seen that the distance between the markets has an effect on the correlation degree of wheat market prices. Larger.

Keywords

Agricultural Products, Regional Spread, Price Fluctuation law, Granger Causality Test, Price Space Transmission

Cite This Paper

Yan Ma. The Regional Spread of Wheat, Corn and Other Agricultural Products. Academic Journal of Agricultural Sciences (2022), Vol. 3, Issue 2: 26-37. https://doi.org/10.38007/AJAS.2022.030203.

References

[1] Kopittke P M , Dalal R C , Finn D .(2017). “Global Changes in Soil Stocks of Carbon, Nitrogen, Phosphorus, and Sulphur as Influenced by Long‐term Agricultural Production”, Global Change Biology, 23(6),pp.2509-2519. https://doi.org/10.1111/gcb.13513

[2] Papadopoulou M P , Charchousi D , Tsoukala V K .(2016). “Water Footprint Assessment Considering Climate Change Effects on Future Agricultural Production in Mediterranean Region”, Desalination & Water Treatment, 57(5), pp.2232-2242. https://doi.org/10.1080/19443994.2015.1049408

[3] Katarzyna Smędzik-Ambroży, Majchrzak A .(2017). “EU Agricultural Policy and Productivity of Soil in Countries Varying in Terms of Intensity of Agricultural Production”, Nephron Clinical Practice, 21(1), pp.250-258. https://doi.org/10.1515/manment-2015-0092

[4] Izmailov A Y .(2019). “Intelligent Technologies and Robotic Means in Agricultural Production”, Herald of the Russian Academy of Sciences, 89(2), pp.209-210. https://doi.org/10.1134/S1019331619020072

[5] Wei Y , Wang X , Wang R .(2018). “Design and Implementation of Agricultural Production Management Information System Based on Web GIS”, Nongye Gongcheng Xuebao/transactions of the Chinese Society of Agricultural Engineering, 34(16), pp.139-147.

[6] Mohammad Ali Tofigh , Zhendong Mu, Intelligent Web Information Extraction Model for Agricultural Product Quality and Safety System, Journal of Intelligent Systems and Internet of Things, 2021, Vol. 4, No. 2, pp: 99-110 (Doi: https://doi.org/10.54216/JISIoT.040203)

[7] Bellamy A S , Svensson O , Brink P J V D .(2018). “Insect Community Composition and Functional Roles Along a Tropical Agricultural Production Gradient”, Environmental Science and Pollution Research, 25(14), pp.13426-13438. https://doi.org/10.1007/s11356-018-1818-4

[8] Oliveira P A D , Rodrigues S A , Padovani C R .(2017). “Association between Agricultural Production Value and the Use of Rural Area within the Municipalities in the State of São Paulo, Brazil”, Journal of Agricultural ence and Technology B, 7(3),pp.147-157. https://doi.org/10.17265/2161-6264/2017.03.002

[9] Tavera Romero CA, Castro DF, Ortiz JH, Khalaf OI, Vargas MA. Synergy between Circular Economy and Industry 4.0: A Literature Review. Sustainability. 2021; 13(8):4331. https://doi.org/10.3390/su13084331 https://doi.org/10.3390/su13084331

[10] Szpyrka E , Magdalena Słowik-Borowiec, Matyaszek A .(2016). “Pesticide Residues in Raw Agricultural Products from the South-eastern Region of Poland and the Acute Risk Assessment”, Roczniki Państwowego Zakładu Higieny, 67(3),pp.237-245.

[11] Houbing Song, Ravi Srinivasan, Tamim Sookoor, Sabina Jeschke, Smart Cities: Foundations, Principles and Applications. ISBN: 978-1-119-22639-0, Hoboken, NJ: Wiley, 2017, pp.1-906.

[12] Yang G , Wang D , Wang J .(2017). “Correlation Analysis of Price Fluctuation of Stock Market by Stochastic Interacting System”, Beijing Jiaotong Daxue Xuebao/Journal of Beijing Jiaotong University, 41(3), pp.120-126. https://doi.org/10.17775/CSEEJPES.2017.0018

[13] Zhang J P , Bian L , Zhang P .(2016). “An Empirical Study of Effect of Raw Material Price Fluctuation of China's Listed Rare Earth Permanent Magnet Companies Based on Stochastic Frontier Analysis”, Chinese Rare Earths, 37(1),pp.151-158.