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The use of the methods of a system analysis as a tool of mathematic modeling in beet production
The most typical peculiarities of the sown areas, in particular sugar beet fields, are the availability of a great number of systematized heterogeneous elements with complicated functional interrelations, which are combined in an agro-production process, aimed at getting high quality agricultural output. A comprehensive implementation of this process is supported by the solution of a set of tasks by separate elements of a system process, which are important for the achievement of the goal.
A system approach envisages the use of the three major groups of methods: field observations; field trials in natural conditions; laboratory experiments; modeling itself and simulation experiment. Field observations consist in a researcher’s non-interference in the processes which take place in natural conditions. On the contrary, a laboratory experiment combines the methods in which a researcher deliberately causes changes in the system. The use of these two methods appears to be the most efficient when they are designed and carried out based on a scientific theory. Models can be a form of the expression of theoretical ideas.
Hence, the third group of the used methods includes modeling, i.e., construction, checking (verification) and improvement (optimization) of the models, as well as the interpretation of the results received with their help.
The use of complex simulation approaches is to increase the adequacy of agro-ecological predictions due to much better and more complete application of empirical data. Simulation approaches allow the formalization of any empirical data about the object with help of ECM (electronic-calculating machines - computers). Cause-effect chains in simulation approaches are not followed to the end. This makes it possible to analyze interconnections in the conditions of a large dimension and incomplete information about their structure, to use the knowledge about the subject area effectively. The structure of simulation approaches, as a rule, includes an analytical description of an object, blocks of expert evaluations, simulation and processing of the results of the computational experiment.
Methods – prediction of bio-productivity of the fields of sugar beet crop rotation using the methods of a system analysis as a tool of mathematical modeling.
Results and discussions – researches of the interconnections which have an effect on the features that are formed during sugar beet growth and development are presented in the form of correlative series. Each point of a series shows the strength of a concrete correlative link between studied features and other factors which either influence or are connected with it.
A close correlation link is recorded between field emergence and plant density after full germination (r=0.42), between field emergence and leaf mass on July 1(r=0.37), and reversed connection between field emergence and yield capacity – r =
-0.37. A close correlation link was recorded between yield capacity of sugar beets and plant density before harvesting (r=0.69); such factors as leaf mass (r=0.41–0.42), sum of active temperatures (r= 0.34), precipitation (r= -0.33), particularly recorded on August 1 and September 1, also had an impact on yield capacity formation, an average correlation link was found between them.
The following factors influenced sugar content in sugar beets: plant density before harvesting (r=0.42), root crop mass before harvesting (r=0.33), yield capacity (r=0.34), precipitation on July 1 (r=0.46), HTC (hydro-thermal coefficient) on July (r=0.44), i.e., an average positive correlation was recorded between these studied features. Strong positive correlation links were found between sugar yield, yield capacity, plant density before harvesting and sugar content of root crops, (r=0.95), (r=0.68) and (r=0.60), respectively.
Key words: sugar beets, system analysis, simulation approach, descriptive models, correlative series, bio-productivity.
https://orcid.org/0000-0002-5860-52861. Vergunova, I. M. (2008). Matematychni modeli poverhnevogo zabrudnennja u gruntah [Mathematical models of sub-soil contamination in soil]. Kyiv, NNC «IAE», 148 p.
2. Baljuk, S. A., Medvedjev, V. V. (2012). Ekologichnyj stan gruntiv Ukrai'ny [Ecological state of Ukrainian soil]. Ukrai'ns'kyj geografichnyj zhurnal [Ukrainian Geographical Journal], no. 2, pp. 38–42.
3. Goncharuk, V. Je., Ljance, G. T., Chaplja, Je. Ja, Chernuha, O. Ju. (2014). Matematychni modeli ta eksperymental'ni dani pro posherennja radionuklidiv u gruntah [Mathematical models and experimental data on the entrainment of radionuclides in soils]. L'viv, Rastr-7, 244 p.
4. Vynograds'ka, V. D. (2014). Prognozuvannja zabrudnennja sil's'kogospodars'koi' produkcii' 137Cs z vykorystannjam modeli povedinky radionuklidu v systemi «grunt-roslyna» [Prediction of contamination of agricultural products 137Cs using the model of behavior of radionuclide in the system "soil-plant"]. Bulletin ZHNAEU. Zagal'na ekologija ta radioekologija [General ecology and radioecology], no. 2 (42), Vol. 1, pp. 13-20.
5. Nazemceva, O. Ja., Lazarenko, D. O. (2013). Modeljuvannja migracii' pestycydiv u gruntah vid dzherel postijnogo zabrudnennja [Simulation of migration of pesticides in soils from sources of constant pollution]. Vostochno-Evropejskyj zhurnal peredovyh tehnologyj [Eastern-European Journal of Eenterprise Technologies], no. 10 (64), Vol. 4, pp. 12-16.
6. Lakin, G. F. (1990). Biometrija [Biometrics]. Moscow, High school, 352 p.
7. Tjurin, Ju. P., Makarov, A. A. (1995). Analiz dannyh na komp'jutere [Analysis of data on the computer]. Moscow, INFRA-M, Finances and statistics, 384 p.
8. Fedorov, V. D., Gil'manov, T. G. (1980). Jekologija [Ecology]. Moscow, MSU, 464 p.
9. Riznichenko, G. Ju., Rubin, A. B. (1993). Matematicheskie modeli biologicheskih produkcionnyh processov [Mathematical models of biogeochemical prodectious protozoa]. Moscow, Moscow State University Publishing House, 301 p.
10. Grjaznov, V. P. Grishin, N. N. (1992). Razrabotka komp'juternoj sistemy "Jekoterra" dlja ucheta jekologicheskogo faktora pri vyrabotke reshenij [The development of the "Ekoterra" system for the calculation of the eco-logical factor in the production of solutions]. Jekol. osnovy optimizacii urban. i rekreac. sredy: Tez. dokl. mezhd. rab. Soveshh [Ecological basics optimization of urban and recreational environment: Theses of the reports of the international working partnership]. Tolyatti, pp. 33-36.
11. Lapko, A. V., Krohov, C. V., Chencov, C. I., Fel'dman, L. A. (1996). Obuchajushhiecja sistemy obrabotki informacii i prinjatija reshenij [Influencing systems of information and decision making]. Novosibirsk, Science, 284 p.
12. Pegov, C. A., Homjakov, P. M. (1991). Modelirovanie razvitija jekologicheskih sistem [Modulation of the development of eco-logical systems]. Leningrad, Gidrometeoizdat, 217 p.
13. Zhang, W., Zhang, L., Mao, S., Qiu, R. Migration and Stabilization of Multiple Heavy Metals in an Aged Contaminated Soil under a Constant Voltage Electric Field. Soil and Sediment Contamination. 2014, Vol. 23, Issue 5, pp. 540-556.
14. Voloha, M. P. (2013). Modeljuvannja tehnologichnyh procesiv pidgotovky g'runtu i nasinnja do sivby cukrovyh burjakiv [Modeling of technological processes of preparation of soil and seeds for the sowing of sugar beets]. Konstrujuvannja, vyrobnyctvo ta ekspluatacija sil's'kogospodars'kyh mashyn [Design, Production and Exploitatin of Agricultural Machines]. Issue 43 (I), pp. 246-252.
15. Neela, Patel, Manish, Thaker, Chandrika, Chaudhary. Study of Some Agricultural Crop Production Planning Condition through Fuzzy Multi-Objective Linear Programming Mathematical Model. International Journal of Science and Research (IJSR). 2016, Vol. 5, Issue 4, pp. 1329-1332.
16. P. Lavanya, Kumari, G. Krishna, Reddy, T. Giridhara, Krishna. Optimum Allocation of Agricultural Land to the Vegetable Crops under Uncertain Profits using Fuzzy Multiobjective Linear Programming IOSR. Journal of Agriculture and Veterinary Science (IOSR-JAVS) e-ISSN: 2319-2380, p-ISSN: 2319-2372. 2014, Vol. 7, Issue 12, Ver. I, pp. 19-28.
17. Ion Raluca, Andreea, Turek Rahoveanu, Adrian. Linear Programming in Agriculture: Case Study in Region of Development South-Mountenia. International Journal of Sustainable Economies Management. 2012, Vol. 1, No 1, pp. 51-60.
18. Lavanya, Kumari P., Vijaya, Kumar K. Some aspects of Operations Research using Solver. International Journal of Advanced Science, Engineering and Technology. ISSN.2319-5924. 2012, Vol. 1, Issue 1, pp. 8-16. Retrieved from: www.bipublication.com
19. Nordin Hj. Mohamad, Fatimah Said (2011). A mathematical programming approach to crop mix problem. African Journal of Agricultural Research, Vol. 6, No 1, pp. 191-197.
20. Kadhirvel, K., Balamurugan, K. Method for solving Unbalanced Assignment Problems using Triangular Fuzzy Numbers. 2013, Vol. 3, Issue 5, pp. 359-363.
21. K. Sangeetha, H. Haseena, Begum, M. Pavithra. Ranking of triangular fuzzy number method to solve an unbalanced assignment problem. Journal of Global Research in Mathematical Archives. 2014, Vol. 2, no. 8, pp. 6-11.
22. Olga Grigorenko. Involving Fuzzy Orders for Multi-Objective Linear Programming Mathematical Modelling and Analysis. 2012, Vol. 17, Issue 3, pp. 366-382.
23. S. K. Bharatі, A. K. Nishad, S. R. Singh. Solution of Multi-Objective Linear Programming Problems in Intuitionistic Fuzzy Environment Proceedings of the Second International Conference on Soft Computing for Problem Solving. 2012, pp. 61-171.
24. E. V. Ivokhin, Almodars Barraq Subhi Kaml. Single-objective linear programming problems with fuzzy coefficients and resources. Journal of Computational & Applied Mathematics. 2013, no. 1(111), pp. 117-125.
25. Ziaee, Saman, Kavand, Hadis, Kalbali, Elham, Soltanii, Samira. The Determination of Optimal Cropping Pattern Using Mathematical Programming with an Emphasis on Sustainable Agriculture (Case Study: Boroujerd City) J. Appl. Environ. Biol. Sci., 4(3s). 2014, pp. 21-25.
26. Salah R., Agha, Latifa G., Nofal, Hana A., Nassar. Multi-criteria governmental crop planning problem based on an integrated AHP-PROMETHEE approach International Journal of Applied Management Science. 2012, Vol. 4, Issue 4. Retrieved from: DOI: 10.1504/IJAMS.2012.049926
27. Bharati, S. K., Singh, S. R. Intuitionistic Fuzzy Optimization Technique in Agricultural Production Planning: A Small Farm Holder Perspective International Journal of Computer Applications (0975 – 8887). 2014, Vol. 89, no. 6, pp. 17-23.
28. Bharati, S. K., Singh, S.R. Solving Multi-Objective Linear Programming Problems Using Intuitionistic Fuzzy Environment Optimization Method: a Comparative Study, International Journal of Modeling and Optimization. 2014. Retrieved from: DOI: 10.7763/IJMO.2014.V4.339.
29. Dubey, Dipti, Chandra, Suresh, Mehra, Aparna. Fuzzy linear programming under interval uncertainty based on IFS representation. Fuzzy Sets and Systems. 2012, Vol. 188, no. 1, pp. 68-87.
30. Nachammai, A. L., Thangaraj, P. Solving intuitionistic fuzzy linear programming problem by using similarity measures. European Journal of Scientific Research. 2012, Vol. 72. no. 2, pp. 204-210.
31. Nagoorgani, P.K. A new approach on solving intuitionistic fuzzy linear programming problem. Applied Mathematical Sciences. 2012, Vol. 6, no.70, pp. 3467-3474.
32. Sinha, P. Analysis of optimal crop combination under limited resourse allocation: Goal programming approach to smallholder farmers in North Bihar, Ph. D. Thesis, Banasthali University India. 2013.
33. Cavchenko, V. V., Sinjavskij, A. Ju. Izmenenie biopotenciala i urozhajnosti sel'skohozjajstvennyh kul'tur pri predposevnoj obrabotke semjan v magnitnom pole [The change in the biopotential and yield of agricultural crops during presowing seed treatment in a magnetic field]. Vestnik VIJeSH [VIESH Institute Herald], no. 2(11), pp. 33–37.
34. Homjakov, D. M., Homjakov, P. M. (1996). Osnovy sistemnogo analiza [The main system analysis]. Moscow, Publishing house of the Faculty of Mechanics and Mathematics, MSU, 107 p.
35. Karpuk, L. M., Prycjazhnjuk, O.I. (2014). Matematychni modeli rostu ta rozvytku roslyn cukrovyh burjakiv zalezhno vid klimatychnyh faktoriv [The mathematical methods of rooting and the development of rockling of sugar beets are dependent on climatic factors]. Sugar beet, no. 6, pp. 13–15.
36. Karpuk, L.M., Krykunova, O.V., Prycjazhnjuk, O.I., Polishhuk, V.V. (2014). Modeljuvannja procesiv rostu ta rozvytku burjakiv cukrovyh zalezhno vid kompleksnogo vplyvu klimatychnyh faktoriv [The modeling of the production of sugar beets and the development of sugar beets is dependent on the influence of climatic factors]. Zbirnyk naukovyh prac' “Agrobiologija” [Collected works “Agrobiology”]. Bila Tserkva, Issue 2 (113), pp. 26-29.
37. Karpuk, L. Prysiazhnyuk, O. Construction of multiple regressive models of sugar beet growth and development. Visnyk Harkivc'kogo nacional'nogo agrarnogo universytetu [Bulletin of Kharkiv National Agrarian University]. Kharkiv, Issue 2, 2014, pp. 74–82.
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