Investigating willingness of Khozestan agricultural experts to undertake extension and development of biofuels using the neural method

Document Type : علمی - پژوهشی


Department of Agriculture Extension and Education, Ramin Agriculture and Natural Resources University, Khuzestan, Iran.


Introduction: The production and development of biofuels can occur naturally in rural areas in the life of rural people and cause dramatic changes, both directly and indirectly. Energy is essential for socio-economic development and improving the quality of life in Iran and other counties (Demirbas, 2008; Yazdanpanah et al., 2015b). Since the production of biofuels is dependent on the agricultural sector, agricultural professionals and experts represent an important source of information for farmers regarding their adoption and innovation. They can facilitate the adoption of innovations or they can limit their diffusion (Bakhtiyari et al., 2017). The aim of this study is to set out the factors affecting agricultural experts’ willingness for the extension and development of biofuels in Khuzestan Province using the artificial neural networks method, and to determine the best method for predicting agricultural experts’ willingness for this.Materials and methods: The study used a cross-sectional survey design. The HBM was quantitatively tested using the survey methodology to understand professionals’ perceptions. The research sample was selected based on quota stratified random sampling (n= 288). The questions were scored on a 1–5 point scale (very low, low, moderate, high and very high) and the survey was conducted among agricultural professionals during February 2015. Data were collected through personal interviews based on a questionnaire structured to assess the central components of the HBM. Afterwards, the completed questionnaires were collected. The questionnaire's internal reliability was tested using Cronbach's alpha coefficient. All scales indicated excellent reliability, generally between 0.76–0.9. Finally, the validity (final step) of the questionnaire was approved by a panel of experts.Results and discussion: Results showed that energy policy, the perceived benefits of renewable energy, the perceived severity of threats from fossil fuels and the cue to action and self-efficacy constructs can predict experts’ willingness.Conclusion: The agricultural sector is the main sector to produce the inputs and resources for the production of biofuels. Thus, farmers represent key stakeholders in the process of a transition towards a higher share of renewable energy. Taken into account that biofuels are a newly emerging technology, it is crucial to provide farmers with sufficient information about this technology and its benefits and risks, in order to increase their willingness to produce biofuels. Here, agricultural professionals play an important role in informing and educating farmers about innovations. The aim of this study is to provide much-needed empirical data about the attitudes of Iranian agricultural professionals towards biofuels. Our analysis revealed that that energy policy, the perceived benefits of renewable energy, the perceived severity of threats from fossil fuels and the cue to action and self-efficacy constructs can all predict experts’ willingness.


  1. منابع
  2. Ardehali, M. M., 2006. Rural energy development in Iran: non-renewable and renewable resources. Renewable Energy. 31, 655-662.
  3. Bakhtiyari, Z., Yazdanpanah, M., Forouzani, M., and Kazemi, N., 2017. Intention of agricultural professionals toward biofuels in Iran: Implications for energy security, society, and policy. Renewable and Sustainable Energy Reviews. 69, 341-349.
  4. De Fraiture, C., Giordano, M. and Liao, Y., 2008. Biofuels and implications for agricultural water use: blue impacts of green energy. Water Policy. 10, 67.
  5. Demirbas, A., 2008. Biofuels sources, biofuel policy, biofuel economy and global biofuel projections. Energy Conversion and Management. 49, 2106-2116.
  6. Demirbas, A., 2009. Political, economic and environmental impacts of biofuels: A review. Applied Energy. 86, S108-S117.
  7. EbrahimZade, A., Eskandari Sani, M and EsmaeilNejad, M., 2010. Factor Analysis Application in Explanation of Spatial Pattern of Developed and Under- Developed Urban- Regional in Iran. Geography and Development Iranian Journal. 17, 7-28. (In Persian with English abstract).
  8. EbrahimZade, A., Eskandari Sani, M. and EsmaeilNejad, M., 2010. Factor Analysis Application in Explanation of Spatial Pattern of Developed and Under- Developed Urban- Regional in Iran. Geography and Development Iranian Journal. No 17. 7-28. (In Persian with English abstract).
  9. Fallah, S., Azizypuor, M. and Rostami, S., 2014. Necessity and potential of biofuel production from cereal residues. Iranian Journal of Energy. 3, 17 (1). URL: (In Persian).
  10. Ghasemi, S., Karami, E. and Azadi, H., 2013. Knowledge, Attitudes and Behavioral Intentions of Agricultural Professionals toward Genetically Modified (GM) Foods: A Case Study in Southwest Iran. Science and Engineering Ethics. 19, 1201-1227.
  11. Kazemi rad, Z. and Papzan, A., 2011. Rural entrepreneurs predict the outcome Kermanshah city using artificial neural network analysis (ANN). Management Systems. 1, 17-26. (In Persian with English abstract).
  12. Khalilmoghadam, B., Afyuni, M., Abbaspour, K. C., Jalalian, A. and Dehghani, A. A., 2015. Application of regression and neural networks to estimate the saturated hydraulic conductivity Zagros Central. Journal of Water and Soil Science. 17, 217-227. (In Persian with English abstract).
  13. Khalilmoghadam, B., Afyuni, M., Abbaspour, K. C., Jalalian, A., Dehghani, A. A. and Schulin, R., 2009. Estimation of surface shear strength in Zagros region of Iran—a comparison of artificial neural networks and multiple-linear regression models. Geoderma. 153, 29-36.
  14. Koh, L. P. and Ghazoul, J., 2008. Biofuels, biodiversity, and people: understanding the conflicts and finding opportunities. Biological Conservation. 141, 2450-2460.
  15. Lee, J. S. H., Rist, L., Obidzinski, K., Ghazoul, J. and Koh, L. P., 2011. No farmer left behind in sustainable biofuel production. Biological Conservation. 144, 2512-2516.
  16. Moeini, M., Hosseini, H. A., Maleki, F. and Sharifi Rad, G. H. R., 2014. The effect of an educational plan based on the health belief model on salt consumption of the women at hypertension risk. Journal of Urmia Nursing and Midwifery Faculty. 12, 94-100.
  17. Msangi, S., Sulser, T., Rosegrant, M. and Valmonte-Santos, R., 2007. Global scenarios for biofuels: Impacts and implications for food security and water use. In 10th Annual Conference on Global Economic Analysis, Purdue University, Indiana.
  18. Orji, R., Vassileva, J. and Mandryk, R., 2012. Towards an effective health interventions design: an extension of the health belief model. Online Journal of Public Health Informatics. 4(3).
  19. Parcell, J. L. and Westhoff, P., 2006. Economic effects of biofuel production on states and rural communities. Journal of Agricultural and Applied Economics. 38, 377.
  20. Raswant, V., Hart, N. and Romano, M., 2008. Biofuel expansion: challenges, risks and opportunities for rural poor people. How the poor can benefit from this emerging opportunity.
  21. Sheelanere, P. and Kulshreshtha, S. S., 2013. Sustainable Biofuel Production: Opportunities for Rural Development. International Journal of Environment and Resource. 2(1).
  22. SoleimaniRoudi, P., Golian, A. and Sedghi, M., 2011. Compare multiple linear regression models and artificial neural network to predict the amino acids millet (pennisetumglaucum) using approximate analysis. Iranian Journal of Animal Science Research. 3, 363-368. (In Persian with English abstract).
  23. Straub, C. L. and Leahy, J. E., 2014. Application of a Modified Health Belief Model to the Pro‐Environmental Behavior of Private Well Water Testing. JAWRA Journal of the American Water Resources Association. 50, 1515-1526.
  24. Urbanchuk, J. M., 2009. Contribution of the Ethanol Industry to the Economy of the United States.
  25. Van de Velde, L., Verbeke, W., Popp, M. and Van Huylenbroeck, G., 2011. Trust and perception related to information about biofuels in Belgium. Public Understanding of Science. 20, 595-608.
  26. Wheeler, S., 2005. Factors Influencing Agricultural Professionals' Attitudes towards Organic Agriculture and Biotechnology (Doctoral dissertation, ANU, Canberra).1-29.
  27. Yazdanpanah, M., Forouzani, M. and Hojjati, M., 2015a. Willingness of Iranian young adults to eat organic foods: Application of the Health Belief Model. Food Quality and Preference. 41, 75-83.
  28. Yazdanpanah, M., Hayati, D. and Zamani, G. H., 2011. Investigating Agricultural Professionals’ Intentions and Behaviors towards Water Conservation: Using a Modified Theory of Planned Behaviour. Environmental. Science, 9(1).‏
  29. Yazdanpanah, M., Komendantova, N. and Ardestani, R. S., 2015b. Governance of energy transition in Iran: Investigating public acceptance and willingness to use renewable energy sources through socio-psychological model. Renewable and Sustainable Energy Reviews. 45, 565-573.‏
  30. Zagata, L., 2012. Consumers’ beliefs and behavioral intentions towards organic food. Evidence from the Czech Republic. Appetite. 59, 81-89.