Agricultural extension; Climate change; Farmers’ adaptation; Sustainability barriers; Vulnerability; Geography, Planning and Development; Renewable Energy, Sustainability and the Environment; Environmental Science (miscellaneous); Energy Engineering and Power Technology; Management, Monitoring, Policy and Law
Abstract :
[en] Even with significant breakthroughs in the production and delivery of meteorological information, most farmers are not able to utilize such information properly and pertinently. Up to the present time, a standardized scale has not been developed to examine farmers’ sustainability barriers to meteorological information use (BMIU). Furthermore, there is no doubt that identifying indicators and dimensions of sustainability barriers to meteorological information and weather forecasts’ usage by farmers can play a major role in their adaptation and resilience to the risks of climate change. Therefore, the present study aimed to generate and validate a scale for BMIU by farmers through an eight-step approach. Accordingly, the statistical population included 9006 Iranian farmers, 368 of whom were selected as study samples. The principal component factor analysis (PCFA) and second-order confirmatory factor analysis (CFA) were further practiced to develop the scale for meteorological information and weather forecasts’ use. Factor analysis also led to the emergence of five latent factors including “educational–communicative barriers (ECBs)”, “normative barriers (NBs)”, “informational barriers (IBs)”, “infrastructural–political barriers (IPBs)”, and “professional–economic barriers (PEBs)”. The second-order CFA correspondingly confirmed these five factors and their 25 related indicators. Given the challenges facing academic scholars, decision makers, and authorities in the application and facilitation of meteorological information, the developed multidimensional scale in this study along with its implementation steps can be effective in examining the limitations of utilizing such information and measuring its impacts in different agricultural communities.
Disciplines :
Agriculture & agronomy
Author, co-author :
Valizadeh, Naser ; Department of Agricultural Extension and Education, School of Agriculture, Shiraz University, Shiraz, Iran
Haji, Latif; Department of Agricultural Extension and Education, School of Agriculture, Shiraz University, Shiraz, Iran
Bijani, Masoud ; Department of Agricultural Extension and Education, College of Agriculture, Tarbiat Modares University (TMU), Tehran, Iran
Fallah Haghighi, Negin; Department of Technology Development Studies (DTDS), Iranian Research Organization for Science and Technology (IROST), Tehran, Iran
Fatemi, Mahsa; Department of Agricultural Extension and Education, School of Agriculture, Shiraz University, Shiraz, Iran
Viira, Ants-Hannes; Institute of Economics and Social Sciences, Estonian University of Life Sciences, Tartu, Estonia
Parra-Acosta, Yenny Katherine ; Business Administration, Universidad Militar Nueva Granada, Bogotá, Colombia
Kurban, Alishir ; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China ; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, China ; University of Chinese Academy of Sciences, Beijing, China
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement ; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; Department of Geography, Ghent University, Ghent, Belgium ; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
Language :
English
Title :
Development of a scale to remove farmers’ sustainability barriers to meteorological information in Iran
Acknowledgments: The authors hereby express their special gratitude to all the farmers who participated in the interviews and completed the questionnaires with great patience as well as the surveyors and interviewers who did their best in the data collection process. This research paper was partly funded by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20060303) and the Chinese Academy of Sciences President’s International Fellowship Initiative (PIFI grant no. 2021VCA0004).
Sharifzadeh, M.; Zamani, G.H.; Karami, E.; Khalili, D.; Tatnall, A. Climate Information Use: An Actor-Network Theory Perspective. In Technological Advancements and the Impact of Actor-Network Theory; Tantall, A., Ed.; IGI Global: Melbourne, Australia, 2012; pp. 35–60.
Harjanne, A. Servitizing climate Science-Institutional analysis of climate services discourse and its implications. Glob. Environ. Chang. 2017, 46, 1–16. [CrossRef]
Hollas, C.E.; Bolsan, A.C.; Venturin, B.; Bonassa, G.; Tápparo, D.C.; Cândido, D.; Antes, F.G.; Vanotti, M.B.; Szögi, A.A.; Kunz, A. Second-Generation Phosphorus: Recovery from Wastes towards the Sustainability of Production Chains. Sustainability 2021, 13, 5919. [CrossRef]
Hassanpour, B.; Amiri Ardekani, M. Investigation of factors influencing the effectiveness of agro-meteorological information from the viewpoint of wheat farmers in Kohgiluyeh-va-Boyerahmad Province. Iran Agric. Educ. Ext. J. 2020, 15, 187–203. (In Persian)
Borrajo-Millán, F.; Alonso-Almeida, M.-d.-M.; Escat-Cortes, M.; Yi, L. Sentiment Analysis to Measure Quality and Build Sustainability in Tourism Destinations. Sustainability 2021, 13, 6015. [CrossRef]
Hu, Q.; Zillig, L.M.P.; Lynne, G.D.; Tomkins, A.J.; Waltman, W.J.; Hayes, M.J.; Wilhite, D.A. Understanding farmers’ forecast use from their beliefs, values, social norms, and perceived obstacles. J. Appl. Meteorol. Climatol. 2006, 45, 1190–1201. [CrossRef]
Sung, J.H.; Baek, D.; Ryu, Y.; Seo, S.B.; Seong, K.-W. Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin. Sustainability 2021, 13, 4969. [CrossRef]
Sharifzadeh, M.; Zamani, G.H.; Khalili, D.; Karami, E. Agricultural climate information use: An application of the planned behaviour theory. J. Agric. Sci. Technol. 2012, 14, 479–492.
FAO. The Impact of Disasters and Crises on Agriculture and Food Security. 2017. Available online: http://www.fao.org (accessed on 10 June 2020).
Hazran, Z.; Norsida, M.; Nitty Hirawaty, K.; Nolila, M.N. The post-flood impacts on farmers, agricultural sector and food security in Kelantan. Int. J. Acad. Res. Bus. Soc. Sci. 2017, 7, 175–184.
Mertz, O.; D’haen, S.; Maiga, A.; Moussa, I.B.; Barbier, B.; Diouf, A.; Dabi, D. Climate variability and environmental stress in the Sudan-Sahel zone of West Africa. Ambio 2012, 41, 380–392. [CrossRef]
Doblas-Reyes, F.; Garcia, A.; Hansen, J.; Mariani, L.; Nain, A.; Ramesh, K.; Venkataraman, R.; Weather and Climate Forecasts for Agriculture. Guide to Agricultural, Meteorological Practices. 2003. Available online: http://citeseerx.ist.psu.edu (accessed on 15 November 2019).
Wamalwa, I.W.; Mburu, B.K.; Mang’uriu, D.G. Agro climate and weather information dissemination and its influence on adoption of climate smart practices among small scale farmers of Kisii country, Kenya. J. Biol. Agric. Healthc. 2016, 6, 14–23.
Mu, L.; Fang, L.; Liu, Y.; Wang, C. Identifying Barriers and Enablers for Climate Change Adaptation of Farmers in Semi-Arid North-Western China. Sustainability 2020, 12, 7494. [CrossRef]
Maleki, T.; Kouhestani, E.; Zarifian, S.; Zarafshani, K. Adaptation of eastern regions of Urmia Lake Basin to water crisis. Iran J. Agric. Educ. Ext. J. 2018, 14, 37–53. (In Persian)
Jones, L.; Boyd, E. Exploring social barriers to adaptation: Insights from Western Nepal. Glob. Environ. Chang. 2011, 21, 1262–1274. [CrossRef]
Mase, A.S.; Prokopy, L.S. Unrealized potential: A review of perceptions and use of weather and climate information in agricultural decision making. Weather Clim. Soc. 2014, 6, 47–61. [CrossRef]
World Bank. World Development Report 2008: Agriculture for Development; The World Bank: Washington, DC, USA, 2008.
Bijani, M.; Ghazani, E.; Valizadeh, N.; Fallah Haghighi, N. Predicting and understanding of farmers’ soil conservation behavior in Mazandaran province, Iran. J. Agric. Sci. Technol. 2019, 21, 1705–1719.
Vincent, K.; Dougill, A.J.; Dixon, J.L.; Stringer, L.C.; Cull, T. Identifying climate services needs for national planning: Insights from Malawi. Clim. Policy 2017, 17, 189–202. [CrossRef]
Stone, R.C.; Meinke, H. Weather, climate, and farmers: An overview. Meteorol. Appl. 2006, 13 (Suppl. S1), 7–20. [CrossRef]
Clements, J.; Ray, A.; Anderson, G. The Value of Climate Services across Economic and Public Sectors: A Review of Relevant Literature; United States Agency for International Development (USAID): Washington, DC, USA, 2013.
Lemos, M.C.; Kirchhoff, C.J.; Ramprasad, V. Narrowing the climate information usability gap. Nat. Clim. Chang. 2012, 2, 789–794. [CrossRef]
Singh, C.; Daron, J.; Bazaz, A.; Ziervogel, G.; Spear, D.; Krishnaswamy, J.; Kituyi, E. The utility of weather and climate information for adaptation decision-making: Current uses and future prospects in Africa and India. Clim. Dev. 2018, 10, 389–405. [CrossRef]
Hill, H.S.; Mjelde, J.W.; Love, H.A.; Rubas, D.J.; Fuller, S.W.; Rosenthal, W.; Hammer, G. Implications of seasonal climate forecasts on world wheat trade: A stochastic, dynamic analysis. Can. J. Agric. Econ. 2004, 52, 289–312. [CrossRef]
Broad, K.; Pfaff, A.; Taddei, R.; Sankarasubramanian, A.; Lall, U.; De Souza Filho, F.D.A. Climate, stream flow prediction and water management in northeast Brazil: Societal trends and forecast value. Clim. Chang. 2007, 84, 217–239. [CrossRef]
Muema, E.; Mburu, J.; Coulibaly, J.; Mutune, J. Determinants of access and utilisation of seasonal climate information services among smallholder farmers in Makueni County, Kenya. Heliyon 2018, 4, e00889. [CrossRef]
Le Dang, H.; Li, E.; Bruwer, J.; Nuberg, I. Farmers’ perceptions of climate variability and barriers to adaptation: Lessons learned from an exploratory study in Vietnam. Mitig. Adapt. Strateg. Glob. Chang. 2014, 19, 531–548.
Mudombi, S.; Nhamo, G. Access to weather forecasting and early warning information by communal farmers in Seke and Murewa districts, Zimbabwe. J. Hum. Ecol. 2014, 48, 357–366. [CrossRef]
Afouku, A.; Obiazi, C. Constraints to Access and Utilization of Meteorological Services in Delta State, Nigeria. Yuzuncu Yıl Univ. J. Agric. Sci 2020, 31, 150–161.
Krell, N.T.; Giroux, S.A.; Guido, Z.; Hannah, C.; Lopus, S.E.; Caylor, K.K.; Evans, T.P. Smallholder farmers’ use of mobile phone services in central Kenya. Clim. Dev. 2021, 13, 215–227. [CrossRef]
Lin, H.I.; Liou, J.L.; Hsu, S.H. Economic Valuation of Public Meteorological Information Services—A Case Study of Agricultural Producers in Taiwan. Atmosphere 2019, 10, 753. [CrossRef]
Hein, Y.; Vijitsrikamol, K.; Attavanich, W.; Janekarnkij, P. Do farmers perceive the trends of local climate variability accurately? An analysis of farmers’ perceptions and meteorological data in Myanmar. Climate 2019, 7, 64. [CrossRef]
Frisvold, G.B.; Murugesan, A. Use of weather information for agricultural decision making. Weather Clim. Soc. 2013, 5, 55–69. [CrossRef]
Statistical Center of Iran. Statistical YearBook of Fars Province for the Year 2017. 2019. Available online: http://nashriatamari. fars.ir (accessed on 10 January 2020).
Etikan, I.; Alkassim, R.; Abubakar, S. Comparision of snowball sampling and sequential sampling technique. Biom. Biostat. Int. J. 2016, 3, 55. [CrossRef]
Mansoorfar, K. Advanced Statistical Methods Using SPSS; University of Tehran: Tehran, Iran, 2018. (In Persian)
Johnson, T.P. Snowball sampling: Introduction; Wiley StatsRef, Statistics Reference Online: Hoboken, NJ, USA, 2014; pp. 1–20.
Fatemi, M.; Karami, E. Case study and analysis of the drivers and impacts of drought. Iran J. Agric. Educ. Ext. J. 2010, 6, 77–96. Available online: https://www.climate-services.org (accessed on 20 January 2020).
Krejcie, R.V.; Morgan, D.W. Determining sample size for research activities. Educ. Psychol. Meas. 1970, 30, 607–610. [CrossRef]
Kalantari, K. Data Processing and Analysis in Socio-Economic Research; Design and Landscape Publishing: Tehran, Iran, 2018. (In Persian)
Nunnally, J.; Bernstein, I. Psychometric Theory; McGraw-Hill: New York, NY, USA, 1994.
Habibpour Gatabi, K.; Safari Shali, R. Comprehensive Manual for Using SPSS in Survey Researches; Luyeh Publication: Tehran, Iran, 2018. (In Persian)
Howard, M.C. A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve? Int. J. Hum. Comput. Interact. 2016, 32, 51–62. [CrossRef]
Kaiser, H.F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 1960, 20, 141–151 . [CrossRef]
Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [CrossRef]
Huang, C.C.; Wang, Y.M.; Wu, T.W.; Wang, P.A. An empirical analysis of the antecedents and performance consequences of using the moodle platform. Int. J. Inf. Educ. Technol. 2013, 3, 217. [CrossRef]
Shyu, C.S.; Li, Y.L.; Tang, Y. Applying confirmatory factor analysis on the measure for restaurant over-service. J. Int. Manag. Stud. 2013, 8, 10–16.
Cheung, G.W.; Wang, C. Current approaches for assessing convergent and discriminant validity with SEM: Issues and solutions. Acad. Manag. Ann. 2017, 2017, 12706. [CrossRef]
Dumenu, W.K.; Tiamgne, X.T. Social vulnerability of smallholder farmers to climate change in Zambia: The applicability of social vulnerability index. SN Appl. Sci. 2020, 2, 1–16. [CrossRef]
Behailu, G.; Ayal, D.Y.; Zeleke, T.T.; Ture, K.; Bantider, A. Comparative Analysis of Meteorological Records of Climate Variability and Farmers’ Perceptions in Sekota Woreda, Ethiopia. Clim. Serv. 2021, 23, 100239. [CrossRef]
Salite, D. Traditional prediction of drought under weather and climate uncertainty: Analyzing the challenges and opportunities for small-scale farmers in Gaza province, southern region of Mozambique. Nat. Hazards 2019, 96, 1289–1309. [CrossRef]
Neisi, M.; Bijani, M.; Abbasi, E.; Mahmoudi, H.; Azadi, H. Analyzing farmers’ drought risk management behavior: Evidence from Iran. J. Hydrol. 2020, 590, 125243. [CrossRef]
