Agricultural Economics and Development

Agricultural Economics and Development

Assessing the Changes in Cultivation Area and Profitability of Cotton Influenced by Government Supportive Policies: A Case Study of Gorgan County of Iran

Document Type : Original Article

Authors
F. Rostami Maskopaee 1
A. Keramatzadeh 2
R. Joolaei 3
H. Kashiri 4
1 MSc. Graduate in Agricultural Economics, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
2 Assistant Professor, Department of Agricultural Economics, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
3 Associate Professor, Department of Agricultural Economics, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran.
4 Assistant Professor, Cotton Researches Institute of Iran, Gorgan, Iran
10.30490/aead.2020.252653.0
Abstract
The cotton as a strategic crop has the important role in creating job, foreign currency and increasing income in agricultural sector and related industries which has been considered by government. In the last couple of decades, the cotton cultivation area in Gorgan County which is one of the major cotton-producing regions has decreased considerably duo to high costs of production and continuation of import. Therefor the object of this study is mainly concentrate on assessing the influence of cultivation and profitability of cotton in Gorgan County influenced by government supportive policies using PMP. The data has been collected by filling out 295 questionnaires using the stratified random sampling method from farmers of Gorgan County in 2014-2015. Three cropping systems of cotton are considered in this study, including: mono-cropping, double cropping and intercropping systems and the governments supportive policies are measured in three parts such as cotton pricing policy, cotton production input subsidy policy and direct payment to cotton production policy. The result shows that the cotton production inputs subsidy has positive profitability among different government supportive policies, so that the most profitable one is related to the cotton mechanic harvesting policy with 1.78 percent of the change. According to results, it is recommended to increase cotton cultivation in Gorgan by applying production inputs subsidy.
Keywords
Supportive Policies
Stratified Random Sampling
Positive Mathematical Programming (PMP)
Intercropping
Gorgan (County)
  1. Arfini, F. (2001). Mathematical programming models employed in the analysis of the common agriculture policy. Journal of Edizioni Scientific Italian, 9: 79-125.
  2. Arfini, F., Donati, M. and Paris, Q. (2003). A national PMP model for policy evaluation in agriculture using micro data and administrative information. Paper Presented at the International Conference on Agricultural Policy Reform and the WTO: Where Are We Heading?, Capri, Italy.
  3. Baker, Sh. (1987). Effect of tillage practices on cotton double-cropped with wheat. Agronomy, 79: 513-516.
  4. Bakhshi, A., Daneshvar Kakhki, M. and Moghaddasi, R. (2011). An application of Positive Mathematical Programming Model to analyze the effects of alternative policies to water pricing in Mashhad Plain. Agricultural Economics and Development, 25(3): 284-294. (Persian)
  5. Bakhshi, M. and Peykani, Gh. (2011). The simulation of direct payement supportive policy in agricultural subsectors: application of Positive Programming approach and Entropy Maximum. Economics Research and Agricultural Development, 2-42(4): 501-511. (Persian)
  6. Bakhshi, M., Peykani, Gh., Hosseini, S. and Saleh, A. (2009). Effects of elimination of fertilizer subsidy and application of direct payment policy on cropping pattern and input usage (case study: Sabzevar County). Agricultural Economics, 2: 185-207. (Persian)
  7. Behbood, A. and Najafi, B. (2011). Effects of supportive policies on sunflower supply. Agricultural Economics Research, 3(2): 133-146. (Persian)
  8. Britt, M.D. (2002). Producer response for cotton in United States. MSc. Thesis, Agricultural and Applied Economics. Texas Tech University.
  9. Buysse, J. (2007). Farm-based modeling of the EU sugar reform: impact on Belgian sugar beet supplier. European Review of Agricultural Economics, 34(1): 21-52.
  10. Cortignani, R. and Severini, S. (2009). Modeling farm-level adoption of defect irrigation using Positive Mathematical Programing. Agricultural Water Management, 8: 102-108.
  11. Elward ­Salih, S.M. (2001). Supply response of Sudan׳s cotton industry: implications of government intervention. PhD Philosophy. Economics and Management. University of Putra Malaysia.
  12. FAO (2017). Crop production: global market analysis. Available at http://www.Faostat.org
  13. Garshasbi, A., Yavari, K., Najjarzadeh, R. and Homayounifar, M. (2012). Effect of price and non-price factors on wheat cultivation in provinces of Iran using panel data. Agricultural Economics, 6(2): 189-204. (Persian).
  14. Ghosh, P.K., Manna, M.C., Bandyopadhyay, K.K., Ajay, T.A.K., Wanjari, R.H., Hati, K.M., Misra, A.K.,Acharya, C.L. and Subba, R.A. (2006). Inter-specific interaction and nutrient use in soybean-sorghum intercropping system. Agronomy, 98: 1097-1108.
  15. Heckelei, T. and Britz, W. (2000). Positive Mathematical Programming with multiple data points: a cross sectional estimation procedure. Cahiers Economics et Socioligie Rurales, 57: 28-50.
  16. Heckelei, T., Britz, W. and Zhang, Y. (2012). Positive mathematical programming approach - recent development in literature and applied modelling. Bio-based and Applied Economics, 1(1): 109-124.
  17. Hosseini, S. and Aminravan, M. (2015). Investigating the effect of supportive policies on cotton acreage in Golestan province. Agricultural Economics and Development, 46(2): 197-206. (Persian)
  18. Howitt, R. (1995). A calibration method for agricultural economic production models. Agricultural Economics, 46(2): 147-159.
  19. Howitt, R. (2005). Positive mathematical programming. American Journal of Agricultural Economics, 77(2): 329-342.
  20. Howitt, R.E., Medellin-Azuara, J., Macewan, D. and Lund, R. (2012). Calibrating disaggregate economic models of agricultural production and water management. Science of the Environmental Modeling and Software. 38: 244-258.
  21. Javanmard, A., Rostami, A., Nooraein, M. and Gharekhani. Gh. (2015). Evaluation of agricultural, ecological and economic aspects in mixed cropping of wheat and pea in Maragheh dry condition. Farmer Knowledge and Sustainability Product Journal, 26(1): (Persian)
  22. Joolaei, R., Yousefzadeh Jahromi, H.R. and Shirani Bidabadi, F. (2013). Investigating the potential of competitiveness and support of cotton and rice production in Golestan province. Plant Production, 20(2): 197-216. (Persian)
  23. Keramatzadeh, A. and Ghorbani, N. (2012). Investigating the effect of inputs on cotton production in Golestan province. Journal of Cotton Researches Institute of Iran, 5(3): 2. (Persian)
  24. Maneta, M., Torres, M., Howitt, R., Vosti, S., Wallender, W. and Bassoi, L. (2007). A detailed Hydro-Economic Model for assessing the effects of surface water and groundwater policies: a demonstration model from Brazil. American Agricultural Economics Association, Annual Meeting, July 29- August 1, Portland, Oregon.
  25. Ministry of Agriculture - Jahad (2017). Agricultural information bank. Tehran: Ministry of Agriculture – Jahad, Statistics and Information Technology Department of Planning and Economy.
  26. Mortazavi, A., Abasmiri, S. and Alaei Boroujeni, P. (2013). Investigating the effects of pricing policies on canola production, Economic Research (Growth and Sustainability Development). 13(4): 127-146. (Persian)
  27. Niazi, A. and Abyar, N. (2004). Effects of cultivation development and yield on wheat production increase in Iran. 6th Agriculture Economics Conference, Mashhad, Iran. (Persian)
  28. Paris, Q. and Howitt, R.E. (1998). An analysis of oil posed production problems using Maximum Entropy. American Journal of Agricultural Economics, 80(1): 124-138.
  29. Pishbahar, A. and Khodabakhshi, S. (2014). Effects of elimination agricultural inputs subsidy on cropping pattern in Varamin County. Agricultural Economics Research, 6(2): 53-68. (Persian)
  30. Sadat Barikani, S.H. and Azari, A. (2012). The effects of agricultural support policies on rice production in Iran. Agricultural Economics Researches, 4(3): 185-205. (Persian)
  31. Sadrolashrafi, M. and Alikhani, M. (2010). Analyzing the economic effects of government intervention policies on cotton pricing in Iran. Agricultural Economics, 4(1): 31-49. (Persian)
  32. Sepehrdoost, H. and Emami, S.S. (2017). Comparative advantage of potato production and related government policies in Hamedan. Agricultural Economics Research, 9(33): 51-70. (Persian)
  33. Zare, A., Chizari, A. and Nemati, N. (2010). Analysis of pricing policy in the cotton market of Iran. Agricultural Economics and Development, 18(69): (Persian)