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2019, vol. 66, br. 2, str. 437-456
A business model in agricultural production in Serbia, developing towards sustainability
(naslov ne postoji na srpskom)
aUniversity of Ljubljana, Faculty of Economics, Ljubljana, Slovenia
bInstitut za opštu i fizičku hemiju, Beograd
cInstitut za prehrambene tehnologije, Novi Sad

e-adresazecevic.mila@yahoo.com, latopezo@yahoo.co.uk, marija.bodroza@fins.uns.ac.rs, tea.brlek@fins.uns.ac.rs, jelena.krulj@fins.uns.ac.rs, jovana.kojic@fins.uns.ac.rs, bosko.maric@fins.uns.ac.rs
Novi proizvodi cerealija i pseudocerealija iz organske proizvodnje (MPNTR - 46005)

(ne postoji na srpskom)
Agricultural production is a Serbian main economic sector, presenting a base for the food industry. By analysing the public available data of the agriculture sector, applying a newly developed business model it is possible to assess the current situation and to realize the relation between variables, which can also be used for prediction of future trends in agricultural production and food industry. Within this paper an attempt was made to develop a novel artificial neural network model for better understanding the relation between the observed parameters and to estimate the efficiency in sustainability achievement and sector potential the well-known Cobb-Douglas production model was compared to the newly developed model. The presented models could be used to achieve the transformation towards a circular bioeconomy, by developing the national strategies for sustainable agricultural production, with the aim of better utilization of resources and reduction of wastes.
*** (2014) Strategy of Agriculture and Rural Development of the Republic of Serbia 2014 -2024. Official Gazette of Republic of Serbia
Apostolov, M. (2016) Cobb-Douglas production function on FDI in Southeast Europe. Journal of Economic Structures, 5(10), 1-28. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2706657
Arsenović, M., Pezo, L., Stanković, S., Radojević, Z. (2015) Factor space differentiation of brick clays according to mineral content: Prediction of final brick product quality. Applied Clay Science, 115: 108-114
Basheer, I.A., Hajmeer, M. (2000) Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1): 3-31
Baye, M.R. (2009) Managerial Economics and Business Strategy. McGraw-Hill, 82-92. ISBN-13: 978-0073375960, ISBN-10: 0073375969
Belović, M.M., Gironés-Vilaplana, A., Moreno, D.A., Milovanović, I.Lj., Novaković, A.R., Karaman, M.A., Ilić, N.M. (2016) Tomato (Solanum LycopersicumL.) Processing Main Product (Juice) and by-Product (Pomace) Bioactivity Potential Measured as Antioxidant Activity and Angiotensin-Converting Enzyme Inhibition. Journal of Food Processing and Preservation, 40(6): 1229-1237
Biam, C.K., Okorie, A., Nwibo, S.U. (2016) Economic efficiency of small scale soyabean farmers in Central Agricultural Zone, Nigeria: A Cobb-Douglas stochastic frontier cost function approach. Journal of Development and Agricultural Economics, 8(3): 52-58
Chattopadhyay, P.B., Rangarajan, R. (2014) Application of ANN in sketching spatial nonlinearity of unconfined aquifer in agricultural basin. Agricultural Water Management, 133: 81-91
Cobb, C.W., Douglas, P.H. (1928) A Theory of Production. American Economic Review, 18: 139-165, http://www2.econ.iastate.edu/classes/econ521/Orazem/Papers/cobb-douglas.pdf
Cook, D.C., Carrasco, L.R., Paini, D.R., Fraser, R.W. (2011) Estimating the social welfare effects of New Zealand apple imports. Australian Journal of Agricultural and Resource Economics, 55(4): 599-620
Četojević-Simin, D.D., Velićanski, A.S., Cvetković, D.D., Markov, S.L., Ćetković, G.S., Tumbas, Š.V.T., Vulić, J.J., Čanadanović-Brunet, J.M., Djilas, S.M. (2015) Bioactivity of Meeker and Willamette raspberry (Rubus idaeus L.) pomace extracts. Food Chemistry, 166: 407-413
Debertin, D.L. (2012) Agricultural Production Economics. Amazon Createspace, Second Edition, ISBN-13 978-1469960647
Echevarria, C. (1998) A Three-Factor Agricultural Production Function: The Case of Canada. International Economic Journal, 12(3): 63-75
European Association for Bioindustries (EuropaBio) (2011) Building a Bio-based Economy for Europe in 2020. Brussels, http://www.scirp.org/(S(oyulxb452alnt1aej1nfow45))/reference/ ReferencesPapers.aspx?ReferenceID=1919890
European Bioeconomy Panel (2014) 2nd Plenary Meeting, Summary of Discussions, 12-13 February. https://ec.europa.eu/research/bioeconomy/pdf/ bioeconomy-panel-summary-2nd-meeting_en.pdf
European Commission (2012) Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Innovating for Sustainable Growth: A Bioeconomy for Europe. Brussels, COM, http://ec.europa.eu/research/ bioeconomy/pdf/bioeconomycommunicationstrategy_b5_brochure_web.pdf
European Commission (2017) Expert Group Report: Review of the EU Bioeconomy Strategy and its Action Plan. Brussels, COM
Ghoshal, P., Goswami, B. (2017) Cobb-Douglas Production Function for Measuring Efficiency in Indian Agriculture: A Region-wise Analysis. Economic Affairs, 62(4): 573-573
Grieu, S., Faugeroux, O., Traoré, A., Claudet, B., Bodnar, J.L. (2011) Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials. Energy and Buildings, 43(2-3): 543-554
Heijman, W. (2016) How big is the bio-business? Notes on measuring the size of the Dutch bio-economy. NJAS - Wageningen Journal of Life Sciences, 77: 5-8
Hennig, C., Brosowski, A., Majer, S. (2016) Sustainable feedstock potential: A limitation for the bio-based economy?. Journal of Cleaner Production, 123: 200-202
Houthakker, H.S. (1955) The Pareto Distribution and the Cobb-Douglas Production Function in Activity Analysis. Review of Economic Studies, 23(1): 27-27
Hu, X., Weng, Q. (2009) Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10): 2089-2102
Johnson, T.G., Altman, I. (2014) Rural development opportunities in the bioeconomy. Biomass and Bioenergy, 63: 341-344
Kalt, G., Baumann, M., Lauk, C., Kastner, T., Kranzl, L., Schipfer, F., Lexer, M., Rammer, W., Schaumberger, A., Schriefl, E. (2016) Transformation scenarios towards a low-carbon bioeconomy in Austria. Energy Strategy Reviews, 13-14, 125-133. https://www.energyagency.at/fileadmin/dam/pdf/projekte/klimapolitik/Del.5.2_Transformation_scenarios_towards_a_low-carbon_bi.pdf
Karlović, S., Bosiljkov, T., Brnčić, M., Ježek, D., Tripalo, B., Dujmić, F., Džineva, I., Skupnjak, A. (2013) Comparison of artificial neural network and mathematical models for drying of apple slices pretreated with high intensity ultrasound. Bulgarian Journal of Agricultural Sciences, 19, 1372-1377. http://www. agrojournal.org/19/06-30.pdf
Kollo, T., von Rosen, D. (2005) Advanced Multivariate Statistics with Matrices. Dordrecht: Springer, ISBN 978-1-4020-3419-0
Loiseau, E., Saikku, L., Antikainen, R., Droste, N., Hansjürgens, B., Pitkänen, K., Leskinen, P., Kuikman, P., Thomsen, M. (2016) Green economy and related concepts: An overview. Journal of Cleaner Production, 139: 361-371
Madamba, P.S. (2002) The Response Surface Methodology: An Application to Optimize Dehydration Operations of Selected Agricultural Crops. LWT - Food Science and Technology, 35(7): 584-592
Mishra, A.K., Das, L. (2017) Total factor productivity with cobb-douglas production function in agriculture: A Study in Cuttack district, Odisha. South Asian Journal of Marketing & Management Research, 7(8): 20-20
Montaño, J.J., Palmer, A. (2003) Numeric sensitivity analysis applied to feedforward neural networks. Neural Computing & Applications, 12(2): 119-125
Montgomery, D.C. (1984) Design and analysis of experiments. New York: John Wiley & Sons, 2nd ed. 978-978, ISBN: 978-0-471-72756-9
Muizniece, I., Timma, L., Blumberga, A., Blumberga, D. (2016) The Methodology for Assessment of Bioeconomy Efficiency. Energy Procedia, 95: 482-486
Pandey, S., Piggott, R.R., Macaulay, T.G. (1982) The elasticity of aggregate Australian agricultural supply: Estimates and policy implications. Austialian Journal Agricultural economics, 26(3): 202-219, http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8489.1982.tb00413.x/pdf
Pezo, L.L., Ćurčić, B.Lj., Filipović, V.S., Nićetin, M.R., Koprivica, G.B., Mišljenović, N.M., Lević, L.B. (2013) Artificial neural network model of pork meat cubes osmotic dehydration. Hemijska industrija, vol. 67, br. 3, str. 465-475
Pfau, S.F., Hagens, J.E., Dankbaar, B., Smits, A.J.M. (2014) Visions of Sustainability in Bioeconomy Research. Sustainability, 6(3): 1222-1249
Prakash, M.J., Priya, B. (2015) Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil. Ultrasonics Sonochemistry, 23: 192-200
Ramcilovic-Suominen, S., Pülzl, H. (2017) Sustainable development: A 'selling point' of the emerging EU bioeconomy policy framework?. Journal of Cleaner Production, 172: 4170-4180
Randall, A. (2008) Is Australia on a sustainability path? Interpreting the clues. Australian Journal of Agricultural and Resource Economics, 52(1): 77-95, http://ageconsearch.umn.edu/record/117742/files/j.1467-8489.2008.00407.x.pdf
Ribeiro, C.O., Oliveira, S.M. (2011) A hybrid commodity price-forecasting model applied to the sugar-alcohol sector. Australian Journal of Agricultural and Resource Economics, 55(2): 180-198
Soji-Adekunle, A.R., Asere, A.A., Ishola, N.B., Oloko-Oba, I.M., Betiku, E. (2018) Modelling of synthesis of waste cooking oil methyl esters by artificial neural network and response surface methodology. International Journal of Ambient Energy, 1-10
Stajčić, S., Ćetković, G., Čanadanović-Brunet, J., Djilas, S., Mandić, A., Četojević-Simin, D. (2015) Tomato waste: Carotenoids content, antioxidant and cell growth activities. Food Chemistry, 172: 225-232
StatSoft, Inc (2010) STATISTICA (data analysis software system), version 10.0. Available from: http://www.statsoft.com/
Taylor, B.J. (2006) Methods and Procedures for the Verification and Validation of Artificial Neural Networks. New York: Kluwer Academic Publishers, ISBN 978-0-387-29485-8
Trelea, I.C., Raoult-Wack, A.L., Trystram, G. (1997) Note: Application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration). Food Science and Technology International, 3: 459-465
Tumbas-Saponjac, V., Girones-Vilaplana, A., Djilas, S., Mena, P., Cetkovic, G., Moreno, D.A., Canadanovic-Brunet, J., Vulic, J., Stajcic, S., Krunic, M. (2014) Anthocyanin profiles and biological properties of caneberry (Rubusspp.) press residues. Journal of Science of the Food and Agriculture, 94, 2393-2400
Tumbas-Šaponjac, V., Ćetković, G., Čanadanović-Brunet, J., Pajin, B., Djilas, S., Petrović, J., Lončarević, I., Stajčić, S., Vulić, J. (2016) Sour cherry pomace extract encapsulated in whey and soy proteins: Incorporation in cookies. Food Chemistry, 207: 27-33
Turanyi, T., Tomlin, A.S. (2014) Analysis of Kinetics Reaction Mechanisms. Berlin-Heidelberg: Springer, ISBN 978-3-662-44562-4
Ubilava, D., Holt, M. (2013) El Nino southern oscillation and its effects on world vegetable oil prices: Assessing asymmetries using smooth transition models. Australian Journal of Agricultural and Resource Economics, 57, 273-297
Vanzetti, D., Quiggin, J. (1985) A comparative analysis of agricultural tractor investment models. Australian Journal of Agricultural Economics, 29 (2), 122-141
Yuan, Z. (2011) Analysis of agricultural input-output based on Cobb-Douglas production function in Hebei Province, North China. African Journal of Microbiology Research, 5 (32), 5916-5922
Zabaniotou, A., Rovas, D., Delivand, M.K., Francavilla, M., Libutti, A., Cammerino, Α.R., Monteleone, M. (2017) Conceptual vision of bioenergy sector development in Mediterranean regions based on decentralized thermochemical systems. Sustainable Energy Technologies and Assessments, 23, 33-47
Zeng, W., Xu, C., Zhao, G., Wu, J., Huang, J. (2017) Estimation of Sunflower Seed Yield Using Partial Least Squares Regression and Artificial Neural Network Models. Pedosphere

O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/ekoPolj1902437Z
objavljen u SCIndeksu: 16.07.2019.
metod recenzije: dvostruko anoniman
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