Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

The Effect of E-Learning on Farmers’ Attitudes Toward Alternative Crops

Document Type : Original Article

Authors
Z. S. Rahimi 1
S. M. Bagher Hosseini 2
H. Rastgarpour 3
R. Asgari 4
1 Department of Educational Technology, Kharazmi University, Karaj, Iran
2 Associate Professor, Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Tehran, Karaj, Iran
3 Associate Professor, Department of Educational Technology, Kharazmi University, Karaj, Iran
4 Graduate in Crop Ecology, Faculty of Agriculture, University of Tehran, Karaj, Iran
10.47176/TDASS.2023.196
Abstract
This study was conducted in 2022–2023 in Nazarabad village, Alborz Province, to examine the effect of e-learning on farmers’ attitudes toward adopting alternative crops. The statistical population included all farmers residing in provinces suitable for saffron cultivation. Using purposive cluster sampling, 60 farmers from rural areas of Nazarabad County were selected and invited to participate in the training program. Participants were randomly assigned to three groups of 20 each (n = 20): Experimental Group 1 (instruction through educational videos), Experimental Group 2 (web-based instruction), and a control group (traditional training). The research followed a quasi-experimental design using pre-test and post-test with two experimental groups and one control group. The inclusion of two experimental groups allowed for comparison between video-based and web-based training methods. To ensure content validity, the questionnaire was reviewed by faculty members from the Universities of Tehran, Isfahan, and Kharazmi. Reliability was confirmed using Cronbach’s alpha (α = 0.74). Data analysis included descriptive statistics (mean, frequency, percentage) and inferential analysis using the Mann–Whitney U test to examine hypotheses across the three types of attitudes. Findings indicated that web-based training significantly enhanced farmers’ knowledge and information, whereas video-based training had a stronger influence on their affective beliefs. Moreover, both methods had nearly equal effects in motivating farmers to adopt saffron cultivation as an alternative to water-intensive crops.
Keywords
E-learning
Web-based training
Attitude
Alternative crops
Saffron
  • Anderson, J. R., & Feder, G. (2004). Agricultural extension: Good intentions and hard realities. The World Bank Research Observer, 19(1), 41–60. https://doi.org/10.1093/wbro/lkh013
  • Bates, A. W. (Tony). (2019). Teaching in a Digital Age – Second Edition: Guidelines for designing teaching and learning. Vancouver, B.C.: Tony Bates Associates Ltd. Retrieved from https://pressbooks.bccampus.ca/teachinginadigitalagev2/
  • Pretty, J., & Bharucha, Z. P. (2014). Sustainable intensification in agricultural systems. Annals of Botany, 114(8), 1571–1596. https://doi.org/10.1093/aob/mcu205
  • Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
  • Altieri, M. A., & Nicholls, C. I. (2017). The adaptation and mitigation potential of traditional agriculture in a changing climate. Climatic Change, 140(1), 33–45. https://doi.org/10.1007/s10584-013-0909-y
  • Swanson, B. E., & Rajalahti, R. (2010). (As above  already cited; please ensure not duplicated).
  • Garrison, D. R. (2011). E-Learning in the 21st Century: A Framework for Research and Practice (2nd ed.). Routledge.
  • Charatsari, C., Lioutas, E. D., & Koutsouris, A. (2020). Farmer intentions to adopt sustainable practices: The role of knowledge and information technologies. Journal of Rural Studies, 78, 378–388. https://doi.org/10.1016/j.jrurstud.2020.06.031
  • Mishra, A. K., Kumar, A., & Joshi, P. K. (2018). Impact of digital agricultural extension on farm productivity: Evidence from India. Agricultural Economics Research Review, 31(2), 187–196. https://doi.org/10.5958/0974-0279.2018.00016.4
  • Mwantimwa, K. (2021). ICT access and use in agriculture: Evidence from smallholder farmers in Tanzania. Technology in Society, 67, Article 101730. https://doi.org/10.1016/j.techsoc.2021.101730
  • Moore, M. G., & Kearsley, G. (2012). Distance Education: A Systems View of Online Learning (3rd ed.). Wadsworth Cengage Learning.
  • Bandura, A. (1997). Self-Efficacy: The Exercise of Control. W.H. Freeman and Company.
  • Esmaeili Aval, M. (2014). Experts’ perspectives on the design and implementation of web-based agricultural extension services in Iran (Master’s thesis). Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
  • Taghipour, M., Abbasi, E., Chizari, M., & Heydari, A. Q. (2013). Analysis of farmers’ attitudes in Gachsaran County toward the formation of water cooperatives. Cooperation and Agriculture, 6, 1–21.
  • Alibeigi, A. H., Darre Zershki, M., & Khaki, M. R. (2011). Farmers’ attitudes toward modern agricultural practices: A study in Mehriz and Khatam counties. Iranian Journal of Agricultural Economics and Development Research, 42(2), 4.
  • Mirtorabi, M. S., Hosseini, S. M., & Alizadeh, N. (2011). Factors influencing wheat farmers’ attitudes toward wheat waste management: A case study of Hashtgerd farmers. Journal of Agricultural Extension and Education Research, 3(4).
  • Chavas, J., & Nauges, C. (2020). Uncertainty, learning, and technology adoption in agriculture. Applied Economic Perspectives and Policy, 42, 42–53. https://doi.org/10.1002/aepp.13003
  • Elbasi, E., Zaki, C., Topcu, A., Abdelbaki, W., Zreikat, A., Cina, E., Shdefat, A., & Saker, L. (2023). Crop prediction model using machine learning algorithms. Applied Sciences. https://doi.org/10.3390/app13169288
  • Nyasimi, M., Kimeli, P., Sayula, G., Radeny, M., Kinyangi, J., & Mungai, C. (2017). Adoption and dissemination pathways for climate-smart agriculture technologies and practices for climate-resilient livelihoods in Lushoto, Northeast Tanzania. Climate, 5, Article 63. https://doi.org/10.3390/cli5030063
  • Paulrud, S., & Laitila, T. (2010). Farmers’ attitudes about growing energy crops: A choice experiment approach. Biomass & Bioenergy, 34, 1770–1779. https://doi.org/10.1016/j.biombioe.2010.07.007
  • Jimenez, I. A. C., García, L., Violante, M., Marcolin, F., & Vezzetti, E. (2020). Commonly used external TAM variables in e-learning, agriculture and virtual reality applications. Future Internet, 13, Article 7. https://doi.org/10.3390/fi13010007
  • Rezk, N. G., Hemdan, E. E., Attia, A., El-Sayed, A., & El-Rashidy, M. A. (2020). An efficient IoT-based smart farming system using machine learning algorithms. Multimedia Tools and Applications, 80, 773–797. https://doi.org/10.1007/s11042-020-09740-6
Transactions on Data Analysis in Social Science
Volume 5, Issue 4
Autumn 2023
Pages 196-205

  • Receive Date 06 August 2023
  • Revise Date 04 October 2023
  • Accept Date 02 December 2023