A BUSINESS MODEL IN AGRICULTURAL PRODUCTION IN SERBIA, DEVELOPING TOWARDS SUSTAINABILITY
DOI:
https://doi.org/10.5937/ekoPolj1902437ZKeywords:
business model, agricultural production, Cobb-Douglas, Artificial Neural Network, SerbiaAbstract
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 CobbDouglas 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.
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References
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3. Basheer, I. A., Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31. doi.org/10.1016/S0167-7012(00)00201-3.
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6. 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.
7. Cetojevic-Simin, D., Velicanski , A., Cvetkovic, D., Markov, S., Cetkovic, G., Tumbas Šaponjac, V., Vulic, J., Canadanovic-Brunet, J., Djilas, S. (2015). Bioactivity of Meeker and Willamette raspberry (Rubusidaeus L.) pomace extracts. Food Chemistry, 166, 407–413. 10.1016/j.foodchem.2014.06.063.
8. 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. doi.org/10.1016/j.agwat.2013.11.007.
9. 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.
10. Cook, D. C., Carrasco, L. R., Paini, D. R., Fraser, R. W. (2011). Estimating the social welfare effects of New Zealand apple imports. The Australian Journal of Agricultural and Resource Economics. 55, 599–620. doi.org/10.1111/j.1467- 8489.2011.00558.x.
11. Debertin, D. L. Agricultural Production Economics, (Second Edition, Amazon Createspace, 2012, ISBN-13 978-1469960647.
12. Echevarria, C. (1998). A Three-Factor Agricultural Production Function: The Case of Canada. International Economic Journal, 12 (3), 63-75. doi.org/10.1080/10168739800000029.
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17. Ghoshal, P., & Goswami, B. (2017). Cobb-Douglas Production Function For Measuring Efficiency in Indian Agriculture: A Region-wise Analysis. Economic Affairs, 62(4), 573-579.
18. 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, 543–554. doi.org/10.1016/j.enbuild.2010.10.020.
19. 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. doi.org/10.1016/j.njas.2016.03.004.
20. Hennig, C., Brosowski, A., Majer, S. (2016). Sustainable feedstock potential – a limitation for the bio-based economy?, Journal of Cleaner Production, 123, 200- 202. doi.org/10.1016/j.jclepro.2015.06.130.
21. Houthakker, H.S. (1955). The Pareto Distribution and the Cobb–Douglas Production Function in Activity Analysis. The Review of Economic Studies, 23 (1), 27–31. doi.org/10.2307/2296148.
22. 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, 2089–2102. doi.org/10.1016/j.rse.2009.05.014.
23. Johnson, T.G. Altman, I. (2014). Rural development opportunities in the bioeconomy. Biomass Bioenergy, 63, 341-344. doi.org/10.1016/j.biombioe.2014.01.028.
24. 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
25. 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.
26. Kollo, T., von Rosen, D. (2005). Advanced multivariate statistics with matrices. Dordrecht: Springer. ISBN 978-1-4020-3419-0.
27. 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. doi.org/10.1016/j.jclepro.2016.08.024.
28. Madamba, P. S. (2002). The Response Surface Methodology: An Application to Optimize Dehydration Operations of Selected Agricultural Crops. LWT – Food Science and Technology, 35, 584–592. doi.org/10.1006/fstl.2002.0914.
29. Maran, J. P., & 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. doi:10.1016/j.ultsonch.2014.10.019
30. 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), 20-25.
31. Montaño, J. J., Palmer, A. (2003). Numeric sensitivity analysis applied to feedforward neural networks. Neural Computing & Applications, 12, 119–125. 12: 119–125. doi.org/10.1007/s00521-003-0377-9.
32. Montgomery, D. C. (1984). Design and analysis of experiments. (2nd ed.). New York: John Wiley and Sons. ISBN: 978-0-471-72756-9.
33. Muizniece, I., Timma L., Blumberga, A., Blumberga, D. (2016). The methodology for assessment of bioeconomy efficiency. Energy Procedia, 95, 482 – 486. doi.org/10.1016/j.egypro.2016.09.072
34. 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.
35. Pezo, L. L., Ćurčić, B. Lj., Filipović, V. S., Nićetin, M. R., Koprivica, G. B., Mišljenović, N. M., Lević, Lj. B. (2013). Artificial neural network model of pork meat cubes osmotic dehydration. Hemijska industrija, 67, 465–475. http://scindeks.ceon.rs/article.aspx?artid=0367-598X1303465P
36. Pfau, S.F., Hagens, J.E., Dankbaar, B., Smits, A.J.M. (2014). Visions of Sustainability in Bioeconomy Research. Sustainability, 6, 1222-1249. doi:10.3390/su6031222.
37. Ramcilovic-Suominen, S, Pülzl H. (2017). Sustainable development – A selling point of the emerging EU bioeconomy policy framework? Journal of Cleaner Production, doi:10.1016/j.jclepro.2016.12.157
38. Randall, A. (2008). Is Australia on a sustainability path? Interpreting the clues. The Australian Journal of Agricultural and Resource Economics, 52, 77–95. http://ageconsearch.umn.edu/record/117742/files/j.1467-8489.2008.00407.x.pdf.
39. Republic of Serbia Strategy of Agriculture and Rural Development of the Republic of Official Gazette 85/2014, Serbia 2014 - 2024, (2014).
40. Ribeiro, C. O., Oliveir, S. M. (2011). A hybrid commodity price-forecasting model applied to the sugar–alcohol sector. The Australian Journal of Agricultural and Resource Economics, 55, 180–198. 10.1111/j.1467-8489.2011.00534.x.
41. 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.doi:10.1080/01430750.2017.1423378
42. 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. doi: 10.1016/j.foodchem.2014.09.069.
43. StatSoft, Inc. STATISTICA (data analysis software system), version 10.0. Available from: http://www.statsoft.com/ (2010).
44. Taylor, B. J. (2006). Methods and procedures for the verification and validation of artificial neural networks. New York: Springer Science & Business Media. ISBN 978-0-387-29485-8.
45. 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. doi.org/10.1177/108201329700300608.
46. 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. doi.org/10.1002/jsfa.6564.
47. 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. doi.org/10.1016/j.foodchem.2016.03.082.
48. Turanyi, T., Tomlin, A. S. (2014). Analysis of Kinetics Reaction Mechanisms. Berlin Heidelberg: Springer. ISBN 978-3-662-44562-4.
49. Ubilava, D., Holt, M. (2013). El Nino southern oscillation and its effects on world vegetable oil prices: assessing asymmetries using smooth transition models, The Australian Journal of Agricultural and Resource Economics, 57, 273–297. doi.org/10.1111/j.1467-8489.2012.00616.x
50. Vanzetti, D., Quiggin, J. (1985). A comparative analysis of agricultural tractor investment models, Australian Journal of Agricultural Economics, 29 (2), 122- 141. doi.org/10.1111/j.1467-8489.1985.tb00652.x
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2019-06-25
How to Cite
Zecevic, M., Pezo, L., Bodroza-Solarov, M., Brlek, T., Krulj, J., Kojić, J., & Marić, B. (2019). A BUSINESS MODEL IN AGRICULTURAL PRODUCTION IN SERBIA, DEVELOPING TOWARDS SUSTAINABILITY. Ekonomika Poljoprivrede, 66(2), 437–456. https://doi.org/10.5937/ekoPolj1902437Z
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