IMPACT OF DIFFERENT FACTORS ON THE FARMERS DECISION TO INSURE CROP PRODUCTION

Aleksandar Miljatović; Dragana Tekić; Veljko Vukoje; Tihomir Novaković; Todor Marković

doi:10.5937/ekoPolj2102423M

Authors

Aleksandar Miljatović University of Novi Sad, Faculty of Agriculture, Novi Sad http://orcid.org/0000-0002-3978-5832
Dragana Tekić University of Novi Sad, Faculty of Agriculture, Novi Sad http://orcid.org/0000-0002-1924-6196
Veljko Vukoje University of Novi Sad, Faculty of Agriculture, Novi Sad http://orcid.org/0000-0002-0801-9043
Tihomir Novaković University of Novi Sad, Faculty of Agriculture, Novi Sad http://orcid.org/0000-0002-8405-3403
Todor Marković University of Novi Sad, Faculty of Agriculture, Novi Sad http://orcid.org/0000-0002-2706-4034

DOI:

https://doi.org/10.5937/ekoPolj2102423M

Keywords:

agricultural holdings, FADN, logistic regression, subsidy level, economic size

Abstract

The aim of the paper is to consider and analyze the impact of subsidies levels and other economic and general factors on the farmers decision to insure their crops. The paper applies the model of logistic regression in order to determine the statistically significant influence of certain factors on the decision of farms. The subject of the research is general and economic data from agricultural holdings in the FADN sample in Serbia for 2018. The sample includes farms that deal with specialist field crops, specialist grazing livestock and mixed crops-livestock production. The survey was conducted on a sample of 819 households, of which 99 households reported insurance costs (12.1%). The results of the research show that with higher subsidy level the probability that farms will insure their production reduces. On the other hand, with an increase of economic size and farm net value added per annual working unit the probability that farms will be insured also increases.

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References

1. Afroz, R., Akhtar, R., & Farhana, P. (2017). Willingness to pay for crop insurance to adapt flood risk by Malaysian farmers: An empirical investigation of Kedah, International Journal of Economics and Financial Issues, 7(4), 1-9.
2. Auer, J. (2003). Weather Derivatives Heading for Sunny Times, Frankfurt Voice, Deutsche Bank Research, Frankfurt am Main.
3. Coble, K., & Barnett, B. (2013). Why do we subsidize crop insurance?, American Journal of Agricultural Economics, 95(2), 498-504.
4. Di Falco, S., Adinolf, F., Bozzola, M., & Capitanio, F. (2014). Crop insurance as a strategy for adapting to climate change. Journal of Agricultural Economics, 65(2), 485-504. https://doi.org/10.1111/1477-9552.12053
5. Enjolras, G., Capitanio, F., & Adinolf, F. (2012). The demand for crop insurance: Combined approaches for France and Italy. Agricultural Economics Review, 13(1), 5-22.
6. FADN Serbia, Methodology: Retrieved from https://www.fadn.rs/ (September 21, 2020).
7. Falola, A., Ayinde, O.E., & Agboola, B.O. (2013). Willingness to take agricultural insurance by cocoa farmers in Nigeria. International Journal of Food and Agricultural Economics, 1(1), 97-107.
8. Ghazanfar, S., Wen, Z.Q., Abdullah, M., Ahmad, J., & Khan, I. (2015). Farmerswillingness to pay for crop insurance in Pakistan. Journal of Business Economics and Finance, 4(2), 166-179.
9. Herbold, J. (2007). Coping with climate risks – The insurers and reinsurers perspective, 101st EAAE Seminar Management of Climate Risks in Agriculture, July 5-6, Berlin; Munich Re Group.
10. Hosmer, W.D., Lemeshow, S., & Stradivant, X.R. (2013). Applied Logistic Regression, Third Edition, John Wiley & Sons, Inc., Hoboken, New Jersey.
11. Ivanović, S., & Marković, T. (2018). Investment management in agrobusiness, University of Belgrade, Faculty of Agriculture Belgrade-Zemun. [Serbian: Ivanović, S., & Marković, T. (2018). Upravljanje investicijama u agrobiznisu].
12. Jokić, M. (2020). The importance of considering internal audit as a decision-making by top-management of an agricultural company. Oditor – časopis za menadžment, finansije i pravo, 6(3), 123-136. https://doi.org/10.5937/Oditor2003123J
13. Kleinbaum, D., Kupper, L., Muller, K., & Nizam, A. (1998). Applied Regression Analysis and Other Multivariable Methods, Third Edition, Brooks/Cole Publishing Company, Pacific Grove, USA.
14. Kvesić, Lj. (2012). Statistical methods in credit risk management, Ekonomski vjesnik, Faculty of Economics Osijek, 15(2), 319-324.
15. Marković, T. (2013). Weather derivates and risk management in agriculture, University of Novi Sad, Faculty of Agriculture Novi Sad.
16. Miljatović, A., Vukoje, V., & Milić, D. (2020). Production and economic indicators of farm businesses in Vojvodina, Agroekonomika, Faculty of Agriculture Novi Sad, 49(86), 13-22.
17. Poljoprivrednik, specialized journal for the village (2019, May 24). Retrieved from https://www.poljoprivrednik.net/poljoprivreda/agroekonomija/4150-tro (September 10, 2020).
18. Popović, V., Miljković, J.Ž., Subić, J., Jean-Vasile, A., Adrian, N., & Nicolăescu, E. (2015). Sustainable land management in mining areas in Serbia and Romania. Sustainability, 7(9), 11857-11877.
19. Subsidies in agriculture (2017). Retrieved from http://subvencije.rs/biljnaproizvodnja/regresiranje-osiguranja-useva-plodova/ (September 10, 2020).
20. Tarasov, A. (2013). Impact of interest rates on the decision to insure in agricultural production. Studies in Agricultural Economics, 115(1), 1-7.
21. Tekić, D., Mutavdžić, B., Novaković, T., & Pokuševski, M. (2020). Analysis of development of local self-government units in Vojvodina. Economics of Agriculture, 67(2), 431-443. https://doi.org/10.5937/ekoPolj2002431T
22. Trushaj, G., Kushta, E. (2020). Implementation of The Logistic Regression Model and Its Applications. Journal of Advances in Matemathics, 18, 46-51.
23. Vasilescu, I., Cicea, C., Popescu, G., & Andrei, J. (2010). A new methodology for improving the allocation of crops cost production in Romania. Journal of Food, Agriculture and Environment, 8(2), 839-842.
24. Velandia, M, Roderick, M., Rejesus, T., Knight, O., & Sherrick, B.J. (2009). Factors affecting farmers utilization of agricultural risk management tools: The case of crop insurance, forward contracting, and spreading sales. Journal of Agricultural and Applied Economics, 41(1), 107-123.
25. Walker, D., & Smith, T. (2016). JMASM Algorithms and code nine pseudo R indices for binary logistic regression models. Journal of Modern Applied Statistical Methods, 15(1), 848-854.
26. Wang, M., Ye, T., & Shi, P. (2015). Factors affecting farmers crop insurance participation in China. Canadian Journal of Agricultural Economics, 64(3), 479- 492. https://doi.org/10.1111/cjag.12088
27. Was, A., & Kobus, P. (2018). Factors determining the crop insurance level in Poland taking into account the level of farm subsidising, The Common Agricultural Policy of the European Union – the present and the future, Institute of Agricultural and Food Economics, National Research Institute, Warsaw, 125-146.