In a small-plot field experiment, the effect of different forms of silicon compounds (tetraethoxysilane (TES) and sodium silicate) and processing methods (seeds and plants) on disease incidence and productivity of spring barley varieties was studied and the effect of diseases on productivity was evaluated. It was found that the use of silicon-containing compounds separately and in a mixture with a reduced consumption rate of fungicide led to a decrease in the infestation of barley with root rot and leaf spots. The lowest degree of disease development was observed when using a fungicide and mixtures of TES and sodium silicate with a reduced consumption rate of fungicide. The maximum yield increases were obtained by combining two types of treatment with silicon - seeds and plants. When processing seeds, the use of sodium silicate was more effective. The relationship between the development of diseases and the productivity of spring barley, primarily grain biomass, is estimated in most cases as strong and medium.
The article examines theoretical approaches to the interpretation of the term “digitalization”, substantiates the relevance of digitization of agriculture and digitalization of industry management at the federal and regional levels, describes the possibilities and prospects of the Systems of State Information Support in the Field of Agriculture (SSIS A) created by the Ministry of Agriculture of Russia, outlines its goals creation, describes the architecture of the individual components of the System, its main functions, as well as the place of the SSIS A in the provision on their base of states of agricultural services in the Russian Federation.
Gross agricultural output is a generalised physical output indicator in an industry that includes hundreds of different types of products, as well as the result of the interaction of production factors. This study provides a comparative analysis of methods based on "Gradient boosting of regression trees" in the Python programming language to identify the optimal values of the model parameters with the subsequent construction of a predictive model based on indicators that affect the production of gross agricultural output. The purpose of this study is to build a regression model for predicting gross agricultural output at actual prices for 2020. To achieve this goal, the methods of regression analysis, forecasting, gradient boosting, etc., were used. The gradient boosting of regression trees was solved for the conditions of the Ryazan Oblast. 4 models were created, 2 of which were based on the preliminary data processing. As a result of the construction of all models, the optimal values of the parameters were found and the results of the correctness on the model on the test set were obtained. It was found that the gradient boosting of regression trees gives adequate regression models for predicting the target variable, in particular, the indicator of gross agricultural output. The investigated indicator is a complex result of the interaction of many factors that are common for agricultural production. Thus, the gradient boosting of trees is most suitable for forecasting complex open systems. Such a method can be used to forecast the production of gross agricultural output not only of individual regions but also of the state as a whole. Based on the "test_score" model, which showed the correctness of 99% (0.994) on the test set, the gross agricultural output in all categories of farms in 2020 amounted to RUB 19187.84 million.
Currently, Internet of Things technologies are increasingly used in agriculture. One of the distinctive features of the industry is the territorial dispersion of means of production and objects of labor, so the use of Internet of Things technologies gives the greatest effect here. In the context of the need to strengthen food security for such product groups as milk and dairy products, as well as beef, an important factor in increasing production is a strong feed base. The potential of livestock productivity can be fully realized only through the use of high-quality feed. Since 65-80% of the feed ration of livestock consists of grass feed (hay, haylage, green fodder, silage), this is exactly the segment of feed production where it is necessary to activate the reserves of its development. Obtaining high-quality grass feed depends largely on establishing the timing of mowing grass, when the ratio between the decreasing protein content and the increasing amount of fiber is optimal. Failure to comply with the optimal timing of harvesting grass feed can lead to a decrease in livestock productivity by up to 20%. Taking into account the large areas of forage crops, this task can be solved by using the Internet of Things technology with the use of remote sensing of crops and unmanned aerial vehicles (drones).
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