Prediction of soil physical and chemical properties based on improved Elman neural network

Global climate change has a huge impact on the future development of the world. Scientists all over the world are actively seeking ways to reduce the adverse effects of climate change, and the increase in CO2 is one of the important causes of global warming. Therefore, it is the current mission and obligation of mankind to understand and study the global carbon cycle process and deal with the results of climate change. Therefore, it is of great significance to improve the carbon sink function of terrestrial ecosystems. Grassland is the largest terrestrial ecosystem in the world and also in my country. The carbon dynamics of grassland ecosystems are closely related to the carbon cycle process of terrestrial ecosystems. Grazing is one of the most important management methods of grassland, and different grazing methods will have different impacts on the carbon source and carbon sink function of grassland ecosystems. In order to achieve the goal of carbon neutrality at peak carbon, it is necessary to configure a reasonable grazing strategy. The basis for formulating a grazing strategy is the prediction and analysis of soil chemical and physical properties. In this paper, the improved Elman neural network is used to predict the physical and chemical properties of soil.