PROGRAM FOR THE RATIONAL CHOICE OF HIGHLY COST-EFFECTIVE ADAPTIVE TECHNOLOGY OF GRAIN CULTIVATION FOR VARIOUS CONDITIONS OF THE EUROPEAN PART OF THE RUSSIAN FEDERATION
Abstract
Currently, a promising area of agricultural development in Russia and in the world is the development of software for digitalization of agriculture. Based on the analysis of regional scientific researches on the use of the most effective combinations of resource-saving agricultural techniques in grain cultivation technologies, a software application has been developed; it allows users to automatically generate individualized adaptive agricultural techniques for cultivating 12 crops for 8 regions of the European part of the Russian Federation, based on the entered values of key indicators of agricultural landscape. The work consistently addresses the issues of the need for digitalization of agriculture, describes the results of ongoing research on this topic, outlines the directions for further research on such developments, and, based on authors’ research presents the stage-by-stage process of developing and testing application software. As a result of the research, a finished product was created and tested i.e. a computer program that solves not only the problem of increasing the profitability of grain production, but also ensuring the environmental orientation of the technologies due to the efficient use of fertilizers, fuel and chemical plant protection products, choice of the optimal variety or hybrid of crops, and used agricultural machinery, taking into account the requirements of import substitution and preliminary calculation of the cost-effectiveness of the chosen technology, which is extremely important and relevant at present. The proposed software package consists of a client-server application for personal computers, a Web application, a mobile application for smartphones based on the Android operating system, two databases (for personal computers and for the online version of the application).
References
2. (2019) Digital transformation of Russian agriculture,Rosinformagrotech
3. Scherbina, T.A. (2019)Digital transformation of Russian agriculture: experience and prospects,Russia: development trends and prospects, vol. 14,pp 450-453
4. (2018) Digitalization of agricultural production in Russia for the period 2018-2025,Research of the cooperative project “German-Russian agrarian and political dialogue”
5. (2016) Strategy of scientific and technological development of the Russian Federation
6. Yakushev,V.V., Yakushev, V.P. (2018)Prospects for "smart agriculture" in Russia,Bulletin of the Russian Academy of Sciences,vol. 88, No 9, pp 773–784
7. Stepnih, N.V., Zargaryan, A.M., ZhukovamO.A. (2017)A computer program for the design of technologies of cultivation of agricultural crops, Agrarian Bulletin of the Urals, vol. 3,pp 54-58
8. Isakova, S.P., Lapchenko, E.A.(2016)Web-based complex based on a mathematical model of optimal machine and tractor fleet formation, Siberian Bulletin of agricultural science,vol. 5 (252),pp 76–82
9. Anderson, R., Keshwani,D., Guru, A. etc. (2018) An integrated modeling framework for crop and biofuel systems using the DSSAT and GREET models, Enviromental modeling and Software,vol. 108,pp 40–50
10. Dzotsi, K. A., Basso, B., Jones, J. W. (2013) Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT, Ecological Modelling,vol. 260, pp 62–76
11. Lopez-Requelme, J., Pavon-Pulido, N., Navarro-Hellin, H. A. (2017)Software architecture based on FIWARE cloud for precision agriculture, Agricultural water management,vol. 183,pp 123–135
12. (2018) Results of the all-Russian agricultural census of 2016,Federal state statistics service,vol. 1
13. (2019) State register of selection achievements approved for use. Vol. 1. "Plant Varieties" (official publication), Rosinformagrotech
14. Pykhtin, A.I., Gostev, A.V., Alimli, D.A. (2018)Model, algorithm and software for automated selection of varieties and hybrids of cereals, Proceedings of Southwest State University. Series: Management, computer engineering, computer science. Medical instrumentation, vol. 3 (28),pp 25-34.
15. Pykhtin, I.G., Gostev, A.V., Pykhtin, A.I. (2017) Software decision support in the cultivation of crops, Journal of Engineering and Applied Sciences, vol. 12(20), pp 5338-5342
16. Gostev, A.V., Pykhtin, A.I. (2017) Structure of costs and expenditures in agro technologies of different intensity levels, Journal of Applied Engineering Science, vol. 15(4), pp 463-466
