Russian Federation
The article shows various aspects of increasing the efficiency of production of products of the plant growing sub-sector based on the introduction of precision farming technologies. An economic model for determining the potential for the introduction of precise technologies is presented, including the analysis of variable factors of production and the determination of the marginal product of labor in crop production, taking into account the acreage, yield, variable and fixed costs, the level of fertilization, plant protection products, profit and other factors. A significant regional differentiation in the level of use of precision farming technologies is revealed. The key factors that determine regional differentiation in the size of areas where elements of precision farming are used and the level of use of digitalization means in agricultural production have been identified. On the example of the Saratov region, the economic efficiency of the introduction of a precision farming system, including systems of parallel driving, differentiated sowing, differentiated fertilization, and harvesting logistics, has been proved. The directions of improving the system of state management of crop production using precision farming technologies based on the geographic information system of the region, including natural and cost indicators of crop production, distributed by geographic coordinates and aggregated with databases of commodity producers and cadastral land registration, have been developed.
agriculture, precision farming, economic efficiency, control, sown area, plant growing, regional differentiation
1. Vorotnikov I.L., Neyfel'd V.V. Effektivnost' primeneniya cifrovyh tehnologiy v upravlenii zemel'nymi resursami municipal'nymi obrazovaniyami Saratovskoy oblasti // Agrarnyy nauchnyy zhurnal. 2018. № 6. S. 76-81.
2. Precision Farming Facts, Statistics and Use Cases. URL: https://www.motus.com/precision-farming/ (data obrascheniya: 12.01.2021 g.).
3. Vedomstvennyy proekt «Cifrovoe sel'skoe hozyaystvo»: oficial'noe izdanie. M. : FGBNU «Rosinformagroteh», 2019. 48 s.
4. Jani K., Chaudhuri M., Patel H., Shah M. Machine learning in films: an approach towards automation in film censoring J. Data. Inf. Manag., 2019 (2019),https://doi.org/10.1007/s42488-019-00016-9
5. Tanha T., Dhara S., Nivedita P., Hiteshri Y., Manan S. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides // Artificial Intelligence in Agriculture, Volume 4, 2020, Pages 58-73. https://doi.org/10.1016/j.aiia.2020.04.002.
6. Bogdanov A.V., Zaripova, I.F., Voloshina, A.D., Strobykina, A.S., Kulik, N.V., Bukharov, S.V., Voronina, Ju.K., Khamatgalimov, A.R., and Mironov, V.F. Synthesis and antimicrobial activity evaluation of some novel water-soluble isatin-3-acylhydrazones // Monatsh. Chem., 2018, vol. 49, p. 111-117. doi 1https://doi.org/10.1007/s00706-017-2049-y
7. Shahzadi R., Tausif M., Ferzund J., Suryani M. Internet of things based expert system for smart agriculture Int. J. Adv. Comput. Sci. Appl., 7(9) (2016), pp. 341-350
8. Doherty P., Rudol P. A Advances in Artificial Intelligence // Lecture Notes in Computer Science, Vol. 4830, Springer, Berlin, Heidelberg (2007), pp. 1-13,https://doi.org/10.1007/978-3-540-76928-6_1
9. Bhaskaranand M., Gibson J. Low-complexity video encoding for UAV reconnaissance and surveillance Proc. IEEE Military Communications Conference (MILCOM) (2011), pp. 1633-1638
10. Bucci G., Bentivoglio D., Belletti M., Finco A. Measuring a farm's profitability after adopting precision agriculture technologies: A case study from Italy. ACTA IMEKO. 2020. №9. pp.65 74.
11. Godwin R., Richards T.E., Wood G.A., Welsh J.P., Knight S.M. (2003). An Economic Analysis of the Potential for Precision Farming in UK Cereal Production. Biosystems Engineering. 84. 533-545.https://doi.org/10.1016/S1537-5110(02)00282-9.
12. Ukolova N.V., Vasilieva E.V., Monakhov S.V., Shikhanova J.A., Korostelev V.G. Models and mechanism of technology transfer under conditions of digitalization of agricultural economy: theory and methodology // Revista Inclusiones. 2020. Vol. 7. № S4-1. pp. 436-446.
13. Vorotnikov I.L., Ukolova N.V., Monakhov S.V., Shikhanova Yu.A., Neifeld V.V. Economic aspects of the development of the "Digital agriculture" system // Scientific Papers. Series: Management, Economic Engineering and Rural Development. 2020. Vol. 20. №1. pp. 633-638.
14. Posevnye ploschadi sel'skohozyaystvennyh kul'tur. EMISS Gosudarstvennaya statistika [Elektronnyy resurs] // URL: https://fedstat.ru/indicator/31328 (data obrascheniya: 17.01.2020 g.).
15. Tochnoe zemledelie: sostoyanie i perspektivy / E. V. Truflyak, N. Yu. Kurchenko, A. S. Kreymer. Krasnodar: KubGAU, 2018. 27 s.
16. Rasporyazhenie Pravitel'stva Rossiyskoy Federacii ot 26 yanvarya 2017 goda N 104-r (Ob utverzhdenii perechnya sub'ektov Rossiyskoy Federacii, territorii kotoryh otnosyatsya k neblagopriyatnym dlya proizvodstva sel'skohozyaystvennoy produkcii territoriyam) (s izmeneniyami na 26 dekabrya 2017 goda) [Elektronnyy resurs] // URL: http://docs.cntd.ru/document/456038774 (data obrascheniya 06.01.2020 g.)
