Farmers Preferences to Plant Crops for Bio-Energy production (Case Study: Sugar Beet in north of Khuzestan Province)
Subject Areas : Agricultural ExtensionDavood Momeni Choleki 1 , Reza Moghaddasi 2 , Yaghoub Zeraatkish 3 , Amir Mohamadinezhad 4
1 - Ph.D. Candidate, Department of Economic, Agricultural Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran,
2 - Associated Professor, Department of Economic, Agricultural Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran,
3 - Associated Professor, Department of Economic, Agricultural Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran,
4 - Assistant Professor, Department of Economic, Agricultural Extension and Education, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
Keywords: biomass, farmers, Choice Experiment, preferences,
Abstract :
Bioenergy is one of renewable energy types. The expansion of production this type of energy can create employment and sustainable income for society in addition to reducing pollution caused by fossil fuels and protecting the environment. Therefore, the purpose of this research was to investigate the preferences of sugar beet farmers in the north of Khuzestan province to the presumptive contracts of sugar beet planting to use in production of bioenergy. The statistical population of the research included 1890 sugar beet farmers of Khuzestan province in 2017-18 which 320 people were selected through the stratified sampling method. The required data were collected in person by referring to farmers and completing the questionnaire. Factors influencing the preferences of farmers were identified using the experimental approach of the attribute-oriented declared choice method to determine the important attributes of different sugar beet planting contracts and to estimate the conditional Logit regression model. The estimation results of the Logit model showed that coefficients related to the contract period, the area covered by the contract, the contract price, the cost-sharing in the contract, the product insurance in the contract and experience of sugar beet planting are positive and significant. The coefficient of the interaction of the variables of attitude to energy production, area under sugar beet planting, Experience of planting sugar beet and the area under sugar beet planting with ASC is significant and positive and the coefficient of the interaction of Farmer's risk attitude with ASC is significant and negative. DOR:20.1001.1.22517588.2021.11.1.1.3
10. ChoiceMetrics. (2012). Ngene 1.1.1: User manual and reference guide. Choice Metrics Pty Ltd.
11. Eggert, H and Olsson, B. (2009). Valuing multi-attribute marine water quality”. Marine Policy, 33(2): 201-206.
12. Embaye, Weldensie T. ; Bergtold, Jason S. ; Archer, David ; Flora, Cornelia ; Andrango, Graciela C. ; Odening, Marting ; Buysse, Jeroen. (2018). Examining farmers' willingness to grow and allocate land for oilseed crops for biofuel production," Energy Economics, Elsevier, vol. 71(C), pages 311-320.
13. Ezzatzadegan Jahromi, S. (2012). Investigation of the production of bioethanol using sugar beet and provide solutions to improve this process. Third Iranian Bioenergy Conference (Biomass and Biogas), by Civilica, Tehran, http://civilica.com/doc/169626/
14. Guentang, L.S.B. (2018). Adoption of bioenergy crops, income and contract preferences among farmers in northern Ghana: the case of Jatropha. Doctoral thesis. Department of agricultural economics and agribusiness, University of Ghana, Legon, http://ugspace.ug.edu.gh/handle/123456789/23178
15. Hearne, R. and Salinas, Z. (2002). The use of choice experiments in the analysis of tourist preferences for ecotourism development in Costa Rica”. Journal of Environmental Management, 65:153-163.
16. Hensher, D., Shore, N. and Train, K. (2005); “Households‟ willingness to pay for water service attributes”. Environmental and Resource Economics, 32:509–531.
17. Khodaverdizadeh, M., Khalilian, S.; Hayati, B; Pishbahar, E. (2014). Estimation of monetary value of the functions and services of the Marakan Protected Area using the choice experiment method. Iranian Journal of Applied Economic Studies. Third year, No. 10, Summer 2014.
18. Krah, K., Petrolia, D. R., Williams, A. S., Coble, K. H., Harri, A., Rejesus, R. M. (2015). Producer preferences for contracts on a risky bioenergy crop. Working Paper Number 15 – 5 | November 2015, Department of Agricultural Economics , Mississippi State University.
19. Lancaster. (1996). A new Approach to Consumer Theory, Journal of Political Economy.74,(2): 132-157.
20. Li, H., Berrnes, R.,Bohara, A.,Jenkins- Smith, H., Silva, C. and Weimer,D. (2004) would developing country commitments affecte US household support for a modified Kyoto Protocol. Ecological Economics. 48:329-343
21. Louviere, J. (2000). Why Stated Preference Discrete Chioice Modelling is Not Conjoint Analysis (and what SPDCM IS).", Memetrics White Paper, http://www.memetrics.com/products/SPDCM_whitepaper.pdf.
22. Louviere, J. and Hensher, D. (1982). On the Design and Analysis of Simulated Choice or Allocation Experiments in Travel Choice Modelling”. Transportation Research Record, 890: 11-17.
23. Louviere, J. J. and G. Woodworth (1983). Design and Analysis of Simulated Consumer Choice or Allocation Experiments - An Approach Based on Aggregate Data", Journal of Marketing Research, 20 (4): 350-367.
24. Louviere, J.J., Hensher, D.A. & Swait, J.D. (2000). Stated Choice methods: Analysis and Applications, Cambridge University press.
25. Lusk, J. L., and Coble, K. H. (2005). Risk perceptions, risk preference, and acceptance of risky food. American Journal of Agricultural Economics, 87(2): 393-405.
26. Mansouri, S.; Kolivand, T.; Khodakarami, J. (2015). Energy production from crops and waste of these products: new solutions in the agricultural sector. 4th International Conference on New Approaches to Energy Conservation, Tehran, https://civilica.com/doc/365599.
27. Maung, T., and Gustafson, C. (2011). The economic feasibility of sugar beet biofuel production in centeral north Dakota. Journal of Biomass and Bioenergy , 35:3737-3747.
28. McFadden, D. (1973); “Conditional Logit Analysis of Qualitative Choice Behavior”, pp105-142, Academic Press, New York.
29. Merino-Castello, A. (2003); “ Eliciting Consumer Preferences Using Stated Preference Discrete Choice Models: Contingent Ranking Versus Choice Experiment .UPF Economics and Business Working Paper No. 705, Available at SSRN:https://ssrn.com/abstract=562982
30. Ministry of Jihad Agriculture (2019). Agricultural Statistics of the Crop Year 2017-18. Volume One: Crop Products. Tehran. Ministry of Agricultural Jihad, Deputy Minister of Planning and Economy, Information and Communication Technology Center.
31. Paulrud S, Laitila T.( 2010) Farmers’ attitudes about growing energy crops: A choice experiment approach. Biomass and Bioenergy. 34(12):1770-9.
32. Petrolia, D. R., Hwang, J., Landry, C. E. & Coble, K. H. (2015). Wind Insurance and Mitigation in the Coastal Zone. Land Economics, 91(2), 272-295.
33. Petrolia, D. R., Landry, C. E., & Coble, K. H. (2013). Risk preferences, risk perceptions, and flood insurance. Land Economics, 89(2), 227-245.
34. Rolfe, J., Bennett, J. and Louviere, J. (2000). Choice modellng and its potential application to tropical rainforest preservation”. Ecological Economics 35 (2), 289–302.
35. Sauthoff, S.; Anastassiadis, F.; Mußhoff, O. (2015). Analyzing farmers' preferences for substrate supply contracts for sugar beets, Working Paper, Department for Agricultural Economics and Rural Development, University of Goettingen. No, 1509.
36. Wamisho,K., Laporte, A. D., Ripplinger, D. (2015). Biomass Contracts for Ethanol Production: The Role of Farmer’s Risk Preferences. Selected Paper prepared for presentation at the 2015 Agricultural & Applied Economics Association and Western Agricultural Economics Association Annual Meeting, San Francisco, CA, July 26-28.
37. World Bioenergy Association. (2018). WBA global Bioenergy Statistic 2018, https://worldbioenergy.org/uploads/181017%20WBA%20GBS%202018_Summary_hq.pdf