Academic Journal of Computing & Information Science, 2022, 5(8); doi: 10.25236/AJCIS.2022.050810.
Zhixuan Han, Zhen Yan
School of Mathematics and Statistics, Guangxi Normal University, Guilin, China
To analyze the influencing factors of grain yield change at present stage and predict the possible grain problems in Jiangsu province is not only beneficial to explore the development trend of grain problems in Jiangsu Province, but also can provide theoretical basis for the government's grain production decision-making. This article selected cities in Jiangsu province in 2001-2020 food production and its related influencing factors of data, the first structure, spatial panel data model to analyze the indexes which influence the production of food, and then through the ARIMA - GM joint model changes of grain production in Jiangsu province in the next five years to forecast, and comparing the future population forecast data of Jiangsu province, To study the change of grain supply and demand and potential problems in Jiangsu province in the next five years. The results show that agricultural technical efficiency, land scale management and effective labor input are the important factors restricting grain yield at present. At the same time, through the prediction of grain output, combined with the forecast results of population in Jiangsu Province, and compared with the growth rate, it can be seen that the growth rate of grain output in the highly developed areas of Jiangsu province, such as Nanjing, Suzhou, Wuxi and other cities, is far less than the growth rate of population. It can be seen that in some areas of Jiangsu Province in the next few years, the problem of grain supply and demand may gradually become prominent, become the "heart trouble" restricting economic development. Finally, some feasible suggestions are put forward for the possible grain production problems in Jiangsu Province.
Spatial panel model; Advanced technology progress; ARIMA-GM
Zhixuan Han, Zhen Yan. Forecast of Grain Supply and Demand in Jiangsu Province and Analysis of Its Influencing Factors-- based on Spatial Panel Model and ARIMA-GM Joint Model. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 8: 65-72. https://doi.org/10.25236/AJCIS.2022.050810.
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