Grey Prediction On Grain Production Of Henan Province

Agriculture is the foundation of national economy, while food is the basic foundation. Henan Province is a major agricultural province that is of large population, and its economy leaves room for prosperity. In the national economy and the people's livelihood of Henan province, food has an important strategic position. Analyzing the change law of the current development of grain production in Henan province and predicting its development trend not only provides decision-making basis for Henan province to make food policy and implement the food production control system, but also is of great practical significance for national food security.Based on the in-depth analysis of the food production system, on the one hand, the paper studies the change law of food production system from the aspect of the relationship between the grain yield and its influence factors and puts forward countermeasures to further improve the production capacity in Henan province combining with the feature that solving small sample size and poor information problems of grey system theory. On the other hand, combining the uncertainty and extension advantages of grey system with the full interdependency advantage of econometrics, the paper puts forward two combined prediction models: grey-linear regression model and grey-econometrics model to study the grain production of Henan province. The results of empirical research show that the model is of a high fitting accuracy and prediction accuracy. The results of prediction show that the model not only is of a better fitting accuracy, but also can describe the development trend in the future of grain production well, and it is of good extrapolation properties.The paper uses grey-one-dimensional linear regression prediction model and grey-econometrics prediction model to study the grain production system of Henan province from the aspects of the total output of grain as well as the summer and autumn grain production respectively. The two models can reflect the essence of the change in system better, and they can be used as effective tools for forecasting the grain production and provide better research methods to predict the grain production. But grain production is a very complex process, and it is also affected by the macro level factors such as policies and so on, nevertheless, these effects can not be expressed in the constructed model. So, in the future, it needs further exploration and research in these areas.