Information Technology of Optimized Agro-biological State Management of Agricultural Lands

• Autorzy: Snytyuk V. , Brovarets O.
• Języki publikacji: EN

Abstrakty

EN

Available techniques for dealing with uncertainties in the agro-industrial complex and their use for describing and assessing the adequacy of the decisions taken are incomplete, and often ineffective, as they usually do not take into account the combination of "field-machine-technological material", which prevents acceptance effective solutions for managing agro-biological potential of agricultural land and, as a consequence, obtaining the maximum economic efficiency of agricultural production. Reliable estimation of variables of agricultural production parameters using the "field-machine-technological material" model makes it possible to provide optimal control of available technical equipment (machinery, sowing machines, etc.), agro-biological (humus content, presence of nutrients, micro-and macro elements, etc. in soil or plant ) and technological resources for making adequate decisions and managing agro-biological potential of agricultural lands, which will provide the necessary economic efficiency. The task is achieved by ensuring the proper quality of the implementation of technological operations that are an integral indicator of economic efficiency and allow providing the necessary economic efficiency through optimal and efficient management of technical means for optimal action on the agro-biological potential of the field and the use of available technological resources. Such control is possible with the use of information and technical systems of local operational monitoring, which are located on machine-tractor units and provide effective control of technological operations by acting on the executive bodies of agricultural machines on the basis of data characterizing the agro-biological state of the soil environment. Information and technical systems of local operational monitoring of the agro-biological state of agricultural lands are used in the following cases: - before performing a technological operation, - simultaneously with the implementation of the technological operation (sowing, fertilizer application, etc.), - during the growing season and after harvesting. This opens new prospects for organic farming using such "smart" agricultural machines.

Słowa kluczowe

EN

soil; samples; size; variability; uncertainty; adequacy; operational monitoring; information and technical system

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2019
• Tom: Vol. 8, No 2
• Strony: 27--38
• Opis fizyczny: Bibliogr. 30 poz., rys., wykr., wz.

Twórcy

autor

Snytyuk V.

• Department of Intellectual and Information Systems Taras Shevchenko Kyiv National University

autor

Brovarets O.

• Department of Informational, Technical and Natural Sciences Kyiv Cooperative Institute of Business and Law

Bibliografia

• 1. Hertz A. Chad and John D. Hibbard. 1993. A Preliminary Assessment of the Economics of Variable Rate Technology for Applying Phosphorus and Potassium in Corn Production. In: Farm Economics iss. 14, Department of Agricultural Economics, University of Illinois, Champaign-Urbana. 218- 231.
• 2. Medvedev V. V. 2007. Soil heterogeneity and precision farming. Part I. Introduction to the problem. Kharkov. 296.
• 3. Ivanov Yu. P, Sinyakov A. N., Filatov I. V. 1984. . Integration of information and measuring devices LA. 207 p.
• 4. Druzhba-Nova. Available at http://druzhba-nova.com/ru/index.html.
• 5. Kbo-agro. Available at http://kbo-agro.com.ua.
• 6. Geonics Limited. Available at www.geonics.com.
• 7. Veristech Available at http://www.veristech.com.
• 8. Patent No. 66982 dated January 25, 2012, bullet. No. 2, IPC B62D 01/00.
• 9. Vadyunina A. F. 1937. To the assessment of electrical conductivity as a method for determining soil moisture. Soil Science. No. 3. :391-404.
• 10. Wilcox G. G. 1947. Determination of electrical conductivity of soil solutions. Soil Science. v. 63. 107 p.
• 11. Ewart G. Y., Baver L. D. 1950. Salinity Effects on soil moisture electrical resistance relationships. Soil Sciene. Soc. Amer. v. 15: 56-63.
• 12. Vorobev N. I. 1955. To the question of conductometric determination of soil and soil salinity. Soil Science. №4. - p. 103.
• 13. Rhoades J. D. Schifgaarde J. Van. 1967. An electrical conductivity probe for determining soil salinity. Soil Scien. Soc. Amer. J. № 5: 647-651.
• 14. Kopikova L. P. 1979. Experience in the use of electrical conductivity for the preparation of detailed soil and soil maps. Bulletin of the VIUA. No. 43: 21-23.
• 15. GOST 26423-85. Soils. Methods for the determination of electrical conductivity, pH and dense residue of the aqueous extract. p. 7.
• 16. Pansu M. Gautheyrou J. 2006. Handbook of soil analysis. Mineralogical, organic and inorganic methods. Springer-Verlag Berlin Heidelberg. 993 p.
• 17. Agrotehnology. Available at http://agrotehnology.com/tochnoe-zemledelie/ideologi/o-chem-rasskazyvaet-udelnaya-elektroprovodnost-pochvy.
• 18. Gukov Ya. S. Linnik N. K., Mironenko V. G. 2001. Automated system of locally dosed application of fertilizers, ameliorants and plant protection products. In: Proceedings of the 2nd MNPK on the problems of differential application of fertilizers in the coordinate farming system. Ryazan: 48-50.
• 19. Brovarets O. 2002. From the Straightforward to the Global Smart Agriculture. Engineering and Technology of Agroindustrial Complex. No. 10 (85): 28-30.
• 20. Adamchuk V. V., Moiseenko V. K., Kravchuk V. I., Voytyuk D. G. 2002. Technique for future agriculture. In: Mechanization and electrification of agriculture. Glevakha: NSC "IMESG". Ed. 86: 20-32.
• 21. Modern trends in the development of agricultural machinery designs. Editors V. I. Kravchuk, M. I. Grytsyshina, S. M. Koval – Kyiv, Agrarian Science, 2004. 398 p.
• 22. Ormadzhi K. S. 1991. Quality control of field work. M .: Rosagropromizdat. 191.
• 23. Pontryagin L. S., Boltyansky V. G., Gamkrelidze R. V., Mishchenko E. F. 1983. Mathematical theory of optimal processes. M .: "Science. 392.
• 24. Burachek V. G. Zheleznyak O. O., Zatserkovny V. I. 2011. Geoinformation analysis of spatial data. Nizhyn: Aspect-Polygraph Publishing Ltd. 440.
• 25. Maslo I. P. Mironenko V. G. 1999. Automated system of locally-doped fertilizer and chemical plant protection products. UAAN: Development-production. Kyiv, Agrarian Science: 348-349.
• 26. Ivakhnenko A. G., Yurachkovsky Yu. P. 1987. Modeling of complex systems by experimental data. Mosow, Radio and communication. 120.
• 27. Snytyuk V. Ye. 2005. Evolutionary simulation of the process of recovery of missing values in data tables In: Proceedings VII intern. sci. pract. conf. "System Analysis and Information Technologies". Kyiv: NTUU of Ukraine "KPI", 157.
• 28. Wasserman F. 1992 Neurocomputer engineering: theory and practice - Moscow, Mir. 240.
• 29. Brovarets O. 2019. Method of calculation of specific electrical conductivity of agro-biological soil environment by stationary contact method of operating electrodes of information and technical system of local operating monitoring. Econtechmod. Vol 8, no 1: 3-10.
• 30. Brovarets O., Chovnyk Yu. 2017. Technical – economic models of business management in the processes of agricultural production. Econtechmod. Vol 6, no 3: 61-70.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).