Experimental analysis of productivity fluctuations in Iranian grain production factors.

Subject Areas : Agricultural Economics Research

Heshmatollah Gholizadeh ¹ , Shahriar Nassabian ² , Reza Moghaddasi ³ , Alireza Amini ⁴

1 - PhD. Student, Department of Economics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Associate Professor, Department of Economics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Agricultural Economics, Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Associate Professor, Department of Economics, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2020-06-22 Accepted : 2022-04-19 Published : 2022-04-21

Keywords: Iranian Agriculture, Using Productivity Indicators, Malmquist, Fare-primont and Hicks-Moorsteen,

Abstract :

Cereals include wheat, barley, rice and grain crops and are important in human and animal nutrition. These products are considered as strategic products and have always been considered by policy makers. The purpose of this study is to analyze the productivity fluctuations of Iranian grain production factors based on various indicators in the period 1988-2017 and the required data have been extracted from the sample census of the Ministry of Jihad Agriculture. In this study, Malmquist, Fare - perimont and Hicks-Moorsteen indices have been used. The results showed that the average changes in total productivity of wheat, barley, rice, corn and cereals of Malmquist index (17, 21, 20, 21, 20) and Fare-permont index (25, 8, 10, 11, 13) and Hicks-Moorsteen index increased by (7, 1, 2, 3, 3), Percentage respectively. The average change of all three indicators is due to the increase of technological changes. Therefore, in order to improve the productivity of grain production products, the application of new technologies and the optimal consumption of inputs should be given more attention

References:

1. Salami, H. (1997). Concepet and measurement of productivity in agriculture. Journal of Agricultural Economics and Development, 5 (18): 7- 32 (In Persian).

 

2. Amini, A., Farhadi Kia, A., and Azouji, A. (2012). Measuring and analyzing productivity indices by separating Iran's economic sectors with a new approach. Journal of Economics and Modeling, 3 (10): 190-236(In Persian).

 

3. Shahnavazi , A. (2018). Investigation of total factors productivity of onion production in Iran. Journal of Agricultural Economics Research, 9 (36): 153-172(In Persian).


4. Khiavi,P.,Moghadasi,R.,and Eskandarpur,B. (2012). Measuring and decomposing total factors productivity growth for sugar beet production in Iran. Journal of sugar beet, 28 (1):95-105 (In Persian).


5. Eslami, M. (2015). Investigating the relationship between agricultural subsidies and productivity shocks with emphasis on agronomy and horticulture. Journal of Agricultural Economics Research, 7 (25): 191-204(In Persian).


6. Nessabian, S ., and S. (2016). The effect of saffron exports on agricultural growth (Case study of Iran and Spain). Journal of Agricultural Economics Research, 8 (31): 17-36 (In Persian).


7. Ghahremanzade, M., Dashti. G., Pahlevani, R., and Sani, F. (2018). Measuring and analyzing total factors productivity growth in Iranian dairy industry. Animal Science Research, 28(3):168-153, (In Persian).


8. Vlontzos, G., Niavis, S., and Manos, B. (2014). A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries, Renewable and Sustainable Energy Reviews, 40: 91-96.


9. Mekonnen, D. K., Spielman, D. J., Fonsah, E. G.,and Dorfman, J. H. (2015). Innovation systems and technical efficiency in developing countries agriculture. Agricultural economics, 46(5): 689-702.


10. Shao, S. (2019). China's agricultural total factor productivity and its influencing factors: An empirical analysis based on provincial panel data from 2005 to 2016. In 3^rd International Conference on Culture, Education and Economic Development of Modern Society (ICCESE). Atlantis Press.


11. Lao, G.,and Mo, B. (2018). Study on the efficiency and total factor productivity of China’s securities companies based on Hicks-Moorsteen TFP index.Technology and Investment, 9(01): 52.‏


12. McErlean, S., and Wu, Z. (2003). Regional labor productivity convergence in China. Food Policy, 28:237- 252.


13. Emami Meybodi, A.(2000) Principles of measuring efficiency and productivity, Trade Research and Studies Institute ,Tehran.


14. Barros, C. P., and Weber, W. L. (2009) Productivity growth and biased technological change in UK airports. Transportation Research Part E: Logistics and Transportation Review, 45(4): 642-653.‏


15. Farrell, M. J. (1957). The measurement of productivity and efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.‏


16. Fare R, Grosskopf S., Norris M.,and Zhang Z. (1996) Productivity growth, technical progress and efficiency changes in industrialised countries. American Economic Review, 84: 66-83.


17. Shephard, R. (2015). Theory of cost and production functions. Princeton University Press.‏


18. Weber, W. L., and Domazlicky, B. R. (1999). Total factor productivity growth in manufacturing: A regional approach using linear programming.Regional Science andUrban Economics, 29(1): 105-122.


19. Dakpo, K. H., Desjeux, Y., Jeanneaux, P., and Latruffe, L. (2017). Productivity, technical efficiency and technological change in French agriculture during 2002-2014: A Färe-Primont index decomposition (No. 263010).30^th internasional Conference of Agricultural Economists, July 28- August 2. 2018. Vancouver.‏


20. O’Donnell, C. J. (2010). Measuring and decomposing agricultural productivity and profitability change. Australian Journal of Agricultural and Resource Economics, 54: 527-560.


21. Solow, R.M. (1957) Technical change and the aggregate production function. The Review of Economics and Statistics, 39: 312-320.


22. Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977) Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6: 21-37.


23. Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982) Multilateral comparisons of output, input, and productivity using superlative index numbers.The Economic Journal, 92: 73-


24. Charnes, A., Cooper, W. W., and Rhodes (1978) Measuring the efficiency of decision- making units. European Journal of Operational Research, 2(6): 429-444


25. O'Donnell, C. (2008) An aggregate quantity-price framework for measuring and decomposing productivity and profitability change (No. WP072008). University of Queensland, School of Economics.‏