Due to the long production cycle, agriculture was not a business, which was attractive for investors for a long time. It is prone to natural risks and huge crop losses during cultivation, harvesting and storage, and the inability to automate biological processes and the lack of progresses in improving productivity and innovation. The use of IT in agriculture was limited to the use of computers and software mainly for financial management and business transactions tracking. Today, in industrialized countries, agriculture is put on an industrial basis. This means not only the use of powerful agricultural equipment, advanced agronomic methods and highly effective chemicals, but also the involvement of the most modern computer technologies. The introduction of computer technology in agriculture is somewhat behind industry, but today we can observe massive introduction of computer technology in agriculture in the United States, Europe and Russia. Agriculture is a perfect field to apply the information technologies. In this case, for effective and sustainable functioning of business enterprises in the new conditions, it is necessary to apply advanced information technologies to identify their internal reserves and attract external investments. This article discusses the importance of implementing information technology in the agro-industrial complex, agricultural problems, the need for the use of GIS systems will also be considered, the features of some systems will be analyzed. Based on the study, the authors propose solutions to some problems using GIS systems.

Agriculture has a greater impact on the environment than any other economic sector in the world. There is a problem of forecasting greenhouse gas emissions depending on various factors of agricultural activity. It is proved that the ability to make forecasts of the amount of greenhouse gas emissions would significantly increase the rational use of agricultural resources. The use of an artificial neural network as a forecasting method in the field of agriculture can allow predicting the environmental impact of greenhouse gases in advance. In addition, this method of data analysis can be an alternative solution of agroecological problems. The article clarifies the features of constructing artificial neural networks for the analysis of dynamic data in the field of agricultural ecology. The approach to solving the problem of forecasting greenhouse gases emissions -an artificial neural network is considered. The problem of forecasting greenhouse gases is considered as a problem of forecasting time series.As an example, an artificial neural network has been designed to predict CO 2 eqemissions from agriculture in the Russian Federation. The data for training the neural network were taken from the Food and Agriculture Organization open database (FAOSTAT).The test results of the developed neural network are presented.The conclusion about the possibility of further appl ication of the development (the model of an artificial neural networkfor predicting СО 2 eq emissionsin agriculture) is given.

No abstract

No abstract

No abstract