• Milan Milunović, PhD Budget Department, Ministry of Defense, Belgrade
  • Radovan Damnjanović, PhD General Staff of the Serbian Armed Forces, Belgrade
  • Nedžad Imamović, PhD Ministry of Defense, Belgrade
  • Radan Kostić, PhD Military Academy, University of Defence, Belgrade
  • Mihailo Ćurčić, MA Military Academy, University of Defence, Belgrade
  • Vladimir Ristić, MA University of Defence, Belgrade
  • Dragan Bojanić, MA University of Defence, Belgrade


Purpose. The aim of this study was to examine the usefulness and accuracy of Data Mining techniques on the example of testing the presence of impact evaluation of the quality of the land on the level of income of agricultural holdings on the basis of test samples. Methodology. The study was analysis conducted on a random sample for identifying key factors in the research of impact evaluation of the quality of the land on the level of income of agricultural holdings, on a data set of 179 examples, where the input consists of various variables: factor of erosivity, the power of the land, reducing the pH value, presence of organic matter, then target discrete variables with two descriptive values: at a expected yield and real yield. Results and Conclusions. The results obtained from the experiments agree confirmed a physical and chemical factors properties largely determines the classification results.


Download data is not yet available.


1. Bengio, Y., Delalleau, O., Le Roux, N., Paiement, J. F., Vincent, P., & Ouimet, M. (2004). Learning eigenfunctions links spectral embedding and kernel PCA. Neural Computation, 16(10), 2197-2219.
2. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
3. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press, Chapman and Hall, London.
4. Han, J., & Kamber, M. (2000). Data Mining: Concepts and Techniques, Elsevier, London.
5. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th international joint conference on Artificial intelligence, Montreal, Quebec, Canada — August 20 - 25, 1995, 14(2), 1137-1145.
6. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classifcation techniques. Informatica, 31(3), 249-268.
7. Mihajlovic, M. (2016). Relationship between corporate management and corporate governance. ODITOR, Center for Economic and Financial Research, 2(1), 4-10.
8. Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507-2517.
9. Touw, W. G., Bayjanov, J. R., Overmars, L., Backus, L., Boekhorst, J., Wels, M., & van Hijum, S. A. (2012). Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?. Briefings in bioinformatics, 14(3), 315-326.
10. Hiironen, J., Riekkinen, K. (2016). Agricultural impacts and profitability of land consolidations. Land Use Policy, Elsevier, 55(9), 309-317.
11. Hira, S., Deshpande, P.S. (2015). Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters, Procedia Computer Science, Elsevier, 55(3), 431-439.
12. Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, Elsevier, 143(9), 23-37.
13. Karlen, D. L., Mausbach, M. J., Doran, J. W., Cline, R. G., Harris, R. F., Schuman, G. E. (1997). Soil Quality: A Concept, Defnition, and Framework for Evaluation, Soil Sci. Soc. Am. J., 61(6), 4-10.
14. Mc Farlanea, J.A., Blackwellab, B.D., Mountera, S.W., Grantc, B.J. (2016). From agriculture to mining: The changing economic base of a rural economy and implications for development. Economic Analysis and Policy, Elsevier, 49(3), 56-65.
15. Xuab, Z., Lee, J., Parka, D., Chunga, Y. (2017). Multidimensional analysis model for highly pathogenic avian influenza using data cube and data mining techniques, Biosystems Engineering, Elsevier, 157(10), 109-121.
16. Matei, O., Rusu, T., Petrovan, A., Mihuţc G. (2017). A Data Mining System for Real Time Soil Moisture Prediction. Procedia Engineering, Elsevier, 181(4), 837-844.
17. Pamučar, D., Ćirović, G. (2018). Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering, 1(1), 13-37.
18. Sabarina, K., Priya, N. (2015). Lowering Data Dimensionality in Big Data for the Benefit of Precision Agriculture. Procedia Computer Science, Elsevier, 48(3), 548-554.
19. Rikalović, A., Soares, G.A., Ignjatić, J. (2018). Spatial analysis of logistics center location: A comprehensive approach. Decision Making: Applications in Management and Engineering, 1(1), 38-50.
20. Trgovčević Prokić, M., Počuča, M. (2016). Acquisition of agricultural land, Economics of Agriculture, 63(4), 1281-1296.
21. Vukoje, A. (2013)., Factors of existence as a condition of creating a market position of the company. ODITOR, Center for Economic and Financial Research 1(5), 27-37. [in Serbian: Faktori egzistencije kao uslov stvaranja tržišne pozicije preduzeća. ODITOR, Centar za ekonomska i fnansijska istraživanja]
How to Cite
MILUNOVIĆ, Milan et al. THE POSSIBILITY OF USING DATA MINING IN THE RESEARCH OF AGRICULTURAL HOLDINGS. Economics of Agriculture, [S.l.], v. 65, n. 3, p. 1139-1146, sep. 2018. ISSN 2334-8453. Available at: <>. Date accessed: 28 nov. 2020. doi:

Most read articles by the same author(s)