The article presents three-year data on the productivity of spring durum wheat varieties in the European continental climate, in order to select the most promising of them and determine the possibilities of breeding and technological adaptation in the region. The experiment involved five varieties of spring durum wheat as B 200, B 205, B 209, B Niva, and Luch 25 taken from Russian Middle Volga regions. Indicators of the best productive bushiness were noted in varieties B Niva and B 209. It was found that the number and weight of seeds in the ear, depending on the variety, varied proportionally to the length of the ear. The best indicators of the structure of the crop were established in varieties B 209 and B Niva. The weight of 1000 seeds was 42.32 to 53.3 g for different varieties of durum wheat. Taking into account the biological yield, the advantage of the variety B Niva over the control variant was revealed. The best formation of grain gluten was observed in agroclimatic conditions in 2015 and 2016: for three years the best indicators of gluten was on average 30.4 %and 29.95%, respectively, for varieties B Niva and B 200.
Best practices of farmers using modern digital technologies demonstrate high results achieved both in crop production and in animal husbandry. Efficiency is expressed in increasing the yield, labor productivity, reducing costs, and what is more, in preserving soil fertility and protecting the environment. However, the need to digitize managerial and analytical processes based on Big Data, Data Science implementation and the ability to interpret the obtained analytical material and make qualified decisions based on a scientific approach are often missed the memo. In light of this, the purpose of the study was to analyze the readiness of various company unit categories employed in the agro-industrial complex of Russia to use big data and process it. Based on the results obtained, a matrix for determining the potential for the transition of companies to the use and analytics of Big Data was built. According to the results of which, it can be argued that, on average, about 45% of the analyzed companies have a high potential for the transition to digital development, and an average level of potential is 24%. In the context of the categories of farms, the results for the surveyed agricultural cooperatives, traders and exporters are higher than the average indicators.
In recent years, in the conditions of economic sanctions and food embargo in Russia, the issue of import substitution has become acute. These phenomena are directly related to agricultural products and, in particular, to the production of hops. Currently, about 90% of commercial hops are imported to the Russian market from abroad. Current trends in the improvement of modern hop-growing technology require the introduction of high-tech innovations into the cultivation process. Its productivity and product quality depend on timely and proper technological operations in the production of hops. Given the increasing prices of energy, it is necessary to introduce improved low-cost technologies in the production of hops. The most important aspect at the present stage is the mechanization of such a process as hanging supports using mobile towers. The use of these towers can increase productivity in this operation in 5-6 times compared to manual labor. The tower must contain a horizontal frame with a trailed device, a working platform, hydraulic cylinders for its lifting, a system of guide rings for support beams. A small ladder should be provided for lifting workers to the site. As a result of the design, the optimal parameters of the scissor-type perspective tower were determined, providing the required stability, both static and dynamic.
Relevance. According to Rosstat, for 2021 share of small agribusiness in the gross harvest of potatoes was 77.8%, vegetables — 71.6%, in production of raw milk — 43.8%, livestock and poultry (in live weight) — 21.9%. However, according to the 2021 census, compared to 2016, the number of small businesses in Russia decreased by an average of 25%. The number of agricultural organizations that are not small businesses increased by 26.3% over the specified period. In order to support small agribusiness, the authors have developed an economic model for calculating the profitability of business concepts for these categories of farms, aimed at automating the assessment of the effectiveness of doing business and investment.Methods. To build the concept of calculations, methods of comparative, statistical analysis, economic and mathematical approach were used. To implement the methodology for calculating the profitability, the basic algorithms of financial mathematics and the functions of the financial category built into the spreadsheet processor MS Excel were used.Results. The model allows to evaluate the cost of investments, credit funds; plan the number of staff; recalculate financial results taking into account the use of loans and subsidies; calculate taxes. In order to test the model, an assessment was made of the effectiveness of investing in dairy cattle breeding in the Chuvash Republic. The model was run 88 times to calculate the payback period for investments in the construction and launch of a dairy farm with a population of 250 head in the main herd with different productivity of cows and applied state support. According to the results of calculations, with an average and high productivity of cows (6500–9000 kg), taking into account the use of the main areas of subsidizing the industry available in the republic, the return on investment can come in 4 years.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.