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Factors affecting farmers’ decisions to participate in direct marketing: A case study of cherry growers in the Kemalpasa District of Izmir, Turkey

Published online by Cambridge University Press:  15 June 2016

Hakan Adanacioglu
Hakan Adanacioglu*
Affiliation:
Department of Agricultural Economics, Faculty of Agriculture, Ege University, Bornova, Izmir, Turkey.
*
*Corresponding author: hakan.adanacioglu@ege.edu.tr
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Abstract

The objective of this study is to explore the main factors that drive decisions of farmers to sell their products directly to consumers through farm direct marketing channels. A case study on cherry growers on the subject of direct marketing, which is one of the alternative marketing options in agricultural products marketing for farmers, is examined in this study. In addition, further suggestions are put forward on how to improve the use of direct marketing strategies by farmers in Turkey. An ordinal logistic regression analysis model was used to analyze the effects of agricultural businesses and demographic features on the tendency of growers to choose direct marketing channels in cherry selling. According to these model results, the cherry farming experience of the growers, the size of the cherry orchard, the level of specialization in cherry production, and the gross margin per hectare for cherry production have a statistically significant impact on the tendency of the growers to choose direct marketing channels in cherry selling. In particular, the growers whose experience is more than 20 yr, the farms that are semi-specialized, the farms providing a gross margin of more than US$5506 ha−1, and the farms having a cherry orchard between 1 and 2 ha in size were determined to have more of a tendency for direct marketing. These results show that owners of medium-sized farms are more interested in direct marketing. According to the interviewed cherry growers, the most important factor limiting their participation in direct marketing is that there are no organizations that will help them meet their direct marketing goals and build direct marketing arrangements between themselves and their consumers.

Keywords

direct marketingfarmers’ decisionsfarmers’ participationcherry growers
Type
Research Papers
Information
Renewable Agriculture and Food Systems , Volume 32 , Issue 4 , August 2017 , pp. 291 - 305
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Copyright
Copyright © Cambridge University Press 2016 

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References

Adanacioglu, H. 2013. The factors affecting farmers’ direct marketing decisions: A research on the cherry producers in Izmir province. Ege University Scientific Research Project Report, Project Number: 2011-ZRF-051, Bornova, Izmir, Turkey. 144 p.Google Scholar
Aguglia, L., Francesco, D.S., and Salvioni, C. 2009. Direct selling: A marketing strategy to shorten distances between production and consumption. In Paper Prepared for Presentation at the 113th EAAE Seminar ‘A Resilient European Food Industry and Food Chain in a Challenging World’, Chania, Crete, Greece, 3–6 September 2009. p. 13. Available at Web site http://ageconsearch.umn.edu/bitstream/57657/2/Aguglia.pdf Google Scholar
Alberta Ag-Info Centre 2013. Farm Direct Marketing for rural Producers. Available at Web site http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/agdex3482/$file/845-6_jun15.pdf?OpenElement Google Scholar
Auld, G.W., Thilmany, D., and Jones, K. 2009. Factors affecting small Colorado producers’ local food sales. Journal of Hunger and Environmental Nutrition 4(2):129–146. Available at Web site http://www.tandfonline.com/doi/pdf/10.1080/19320240902915284 CrossRefGoogle Scholar
Benedek, Z., Fertő, I., Baráth, L., and Tóth, J. 2014. Factors influencing the decision of small-scale farmers on marketing channel choice: A Hungarian case study. In Paper Prepared for Presentation at the EAAE 2014 Congress ‘Agri-Food and Rural Innovations for Healthier Societies’, Ljubljana, Slovenia, 26–29 August 2014. p. 12. Available at Web site http://econ.core.hu/file/download/kti60/smallscale.pdf Google Scholar
Bewick, V., Cheek, L., and Ball, J. 2005. Statistics review 14: Logistic regression. Critical Care 9(1):112–118. Available at Web site http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1065119/pdf/cc3045.pdf CrossRefGoogle ScholarPubMed
Bruch, M.L. and Ernst, M.D. 2010. Choosing Direct Marketing Channels for Agricultural Products. The University of Tennessee; Institute of Agriculture. Available at Web site https://extension.tennessee.edu/publications/Documents/PB1796.pdf Google Scholar
Burt, L. and Wolfley, B. 2009. Farmer-to-consumer Marketing: An Overview. Oregon State University, Extension Catalog, PNW 201-E. Available at Web site http://ir.library.oregonstate.edu/xmlui/bitstream/handle/1957/20662/pnw201-e.pdf Google Scholar
CBRT. 2012. Exchange Rates. Central Bank of the Republic of Turkey. Available at Web site http://www.tcmb.gov.tr/wps/wcm/connect/TCMB+EN/TCMB+EN/Main+Menu/STATISTICS/Exchange+Rates Google Scholar
Chen, X., Ender, P., Mitchell, M., and Wells, C. (eds). 2003. Regression with SAS. SAS Web Books. Available at Web site http://www.ats.ucla.edu/stat/sas/webbooks/reg/default_short.htm Google Scholar
Corsi, A., Borsotto, P., Borri, I., and Strøm, S. 2009. Diversification of the marketing chains among organic producers. In Contributed Paper Prepared for Presentation at the International Association of Agricultural Economists Conference, Beijing, China, 16–22 August 2009. p. 13. Available at Web site http://ageconsearch.umn.edu/bitstream/51422/2/Diversification%20IAAE.pdf Google Scholar
Corsi, A., Novelli, S., and Pettenati, G. 2014. Alternative food networks in Piedmont: Determinants of on-farm and off-farm direct sales by farmers. In Paper Prepared for Presentation at the 3rd AIEAA Conference ‘Feeding the Planet and Greening Agriculture: Challenges and Opportunities for the Bio-Economy’, 25–27 June 2014, Alghero, Italy. p. 9. Available at Web site http://ageconsearch.umn.edu//handle/173035 Google Scholar
Cottingham, J., Hofland, J., Lenon, J., Roper, T., and Techtmann, C. 1994. Direct Marketing of Farm Produce and Home Goods. Available at Web site http://foodsecurecanada.org/sites/default/files/DirectMarketing.PDF Google Scholar
Detre, J.D., Mark, T.B., Mishra, A.K., and Adhikari, A. 2011. Linkage between direct marketing and farm income: A double-hurdle approach. Agribusiness 27:19–33.CrossRefGoogle Scholar
Diamond, A. and Soto, R. 2009. Facts on direct-to-consumer food marketing. USDA, Agricultural Marketing Service. Available at Web site https://web.extension.illinois.edu/smallfarm/downloads/48143.pdf Google Scholar
Fehér, I. 2012. Direct food marketing at farm level and its impacts on rural development. In Adisa, R.S. (ed.). Rural Development—Contemporary Issues and Practices. InTech, Croatia. p. 337–354. ISBN: 978-953-51-0461-2, doi: 10.5772/29019. Available at Web site http://cdn.intechopen.com/pdfs-wm/34427.pdf Google Scholar
Fujisawa, M., Kobayashi, K., Johnston, P., and New, M. 2015. What drives farmers to make top-down or bottom-up adaptation to climate change and fluctuations? A comparative study on 3 cases of apple farming in Japan and South Africa. PLoS ONE 10(3):e0120563. doi: 10.1371/journal.pone.0120563. Available at Web site http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0120563&representation=PDF CrossRefGoogle ScholarPubMed
Gilg, A.W. and Battershill, M. 1999. The role of household factors in direct selling of farm produce in France. Tijdschrift voor Economische en Sociale Geografie 90:312–319.CrossRefGoogle Scholar
Gorton, M., Salvioni, C., and Hubbard, C. 2014. Semi-subsistence farms and alternative food supply chains. EuroChoices 13:15–19.CrossRefGoogle Scholar
Greene, W.H. 2008. Two advances in ordered choice modeling. In International Choice Modeling Conference, ITS, University of Leeds, UK, 30 March–1 April 2009. p. 34. Available at Web site http://www.icmconference.org.uk/index.php/icmc/icmc2009/paper/viewFile/152/22 Google Scholar
Hardesty, S.D. and Leff, P. (2010). Determining marketing costs and returns in alternative marketing channels. Renewable Agriculture and Food Systems 25(1):24–34.CrossRefGoogle Scholar
Heck, R.H., Thomas, S., and Tabata, L. (eds). 2013. Multilevel Modeling of Categorical Outcomes Using IBM SPSS. Routledge, Taylor & Francis Group, New York, USA.CrossRefGoogle Scholar
Heeringa, S.G., West, B.T., and Berglund, P.A. (eds). 2010. Applied Survey Data Analysis. Chapman and Hall/CRC, Boca Raton, FL, USA.CrossRefGoogle Scholar
Hird, V., Holder, H., Chandrasekaran, K., Benning, R., Schimpf, M., and Becheva, S. 2010. A New Food and Agriculture Policy for the European Union. Position Paper on the 2013 Reform of the Common Agricultural Policy. Available at Web site https://www.foeeurope.org/sites/default/files/CAP_PP_full_final%5B1%5D.pdf Google Scholar
IATP. 1998. Marketing Sustainable Agriculture: Case Studies and Analysis from Europe. Institute for Agriculture and Trade Policy, USA. Available at Web site http://www.iatp.org/files/Marketing_Sustainable_Agriculture_-_Case_Studi.pdf Google Scholar
Kay, R.D., Edwards, W.M., and Duffy, P.A. 2004. Farm Management. 5th ed. The McGraw-Hill Companies, New York, USA, ISBN: 0-07-242868-6.Google Scholar
MFAL. 2012. Crop Production Statistics. Izmir Provincial Directorate of Ministry of Food, Agriculture and Livestock. Available at Web site http://izmir.tarim.gov.tr/Sayfalar/EN/AnaSayfa.aspx Google Scholar
Minetos, D. and Polyzos, S. 2007. Forest land use changes in Greece: An ordinal regression analysis at the regional level. Discussion Paper Series 13(7):165–192. Available at Web site http://www.prd.uth.gr/uploads/discussion_papers/2007/uth-prd-dp-2007-07_en.pdf Google Scholar
Monson, J., Mainville, D.Y., and Kuminoff, N.V. 2008. The decision to direct market: An analysis of small fruit and specialty-product markets in Virginia. Journal of Food Distribution Research 39(2):1–11. Available at Web site http://ageconsearch.umn.edu/bitstream/55971/2/Monson.pdf Google Scholar
Muthini, D.N. 2015. An assessment of mango farmers’ choice of marketing channels in Makueni, Kenya. MSc thesis, Agricultural and Applied Economics of the University of Nairobi. p. 75. Available at Web site http://ageconsearch.umn.edu/bitstream/204866/2/MuthiniFinal%20thesis.pdf Google Scholar
Newbold, P. (ed.). 1995. Statistics for Business and Economics. Prentice-Hall, New Jersey, USA.Google Scholar
Nyaupane, N.P. and Gillespie, J.M. 2010. Factors influencing producers’ marketing decisions in the Louisiana crawfish industry. Journal of Food Distribution Research 42(2):1–11. Available at Web site http://ageconsearch.umn.edu/bitstream/139420/2/Nyaupane_42_2.pdf Google Scholar
Pampel, F.C. (ed.). 2000. Logistic Regression: A Primer. Sage Publications, Thousand Oaks, CA, USA.CrossRefGoogle Scholar
Park, T. 2015. Direct marketing and the structure of farm sales: An unconditional quantile regression approach. Journal of Agricultural and Resource Economics 40(2):266–284. Available at Web site http://www.waeaonline.org/UserFiles/file/JAREMay20155Parkpp266-284.pdf Google Scholar
Park, T., Mishra, A.K., and Wozniak, S.J. 2014. Do farm operators benefit from direct to consumer marketing strategies? Agricultural Economics 45(2):213–224.CrossRefGoogle Scholar
Park, T.A., Mishra, A.K., and Wozniak, S.J. 2011. Farm operator benefits from direct marketing strategies: How does local food impact farm financial performance? In Presentation Prepared for the 2011 Agricultural and Applied Economics Association Annual Meeting, Pittsburgh, Pennsylvania, 24–26 July 2011. p. 25. Available at Web site http://ageconsearch.umn.edu/handle/103756 Google Scholar
Sage, J.L. and Goldberger, J.R. 2012. Decisions to direct market: Geographic influences on conventions in organic production. Applied Geography 34:57–65. Available at Web site http://www.sciencedirect.com/science/article/pii/S0143622811001913 CrossRefGoogle Scholar
Thilmany, D., Tegegne, E. and Hines, B. 2012. Farmers markets and direct marketing in the Western US: Market trends and linkages with food system issues. Western Economics Forum 11(2):9. Available at Web site http://ageconsearch.umn.edu/bitstream/176583/2/WEFFall2012v11n2_Thilmany.pdf Google Scholar
Timmons, D. and Wang, Q. 2010. Direct food sales in the United States: Evidence from state and county-level data. Journal of Sustainable Agriculture 34(2):229–240. Available at Web site http://www.tandfonline.com/doi/pdf/10.1080/10440040903482605 CrossRefGoogle Scholar
Uematsu, H. and Mishra, A.K. 2011. Use of direct marketing strategies by farmers and their impact on farm business income. Agricultural and Resource Economics Review 40(1):1–19. Available at Web site http://ageconsearch.umn.edu/bitstream/105457/2/mishra%20workshop%20paper%20-%20current.pdf CrossRefGoogle Scholar
USDA. 2014. Farmers Marketing. USDA, National Agricultural Statistics Service, ACH12-7. Available at Web site http://www.agcensus.usda.gov/Publications/2012/Online_Resources/Highlights/Farmers_Marketing/Highlights_Farmers_Marketing.pdf Google Scholar
Uva, W.-F.L. 2002. An Analysis of Vegetable Farms’ Direct Marketing Activities in New York State. Department of Applied Economics and Management, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY. p. 56. Available at Web site http://ageconsearch.umn.edu/bitstream/122632/2/Cornell_Dyson_rb0203.pdf Google Scholar
Williams, R. 2015. Logistic Regression, Part II: The Logistic Regression Model (LRM)—Interpreting Parameters. Available at Web site https://www3.nd.edu/~rwilliam/stats2/l82.pdf Google Scholar
Wolanin, C.A. 2013. Intensity of adoption of direct marketing strategies among fruit and vegetable farmers in Tennessee. Master's thesis, University of Tennessee. p. 45. Available at Web site http://trace.tennessee.edu/utk_gradthes/1698/ Google Scholar
Yay, M. and Akinci, E. 2009. Application of ordinal logistic regression and artificial neural networks in a study of student satisfaction. Cypriot Journal of Educational Sciences 4:58–69. Available at Web site http://www.world-education-center.org/index.php/cjes/article/viewFile/%2072/35 Google Scholar
Young, D. 1995. Characteristics of Direct Marketing Alternatives. Direct Farm Marketing and Tourism Handbook, The University of Arizona, Cooperative Extension Service, Tucson, AZ. Available at Web site https://ag.arizona.edu/arec/sites/cals.arizona.edu.arec/files/publications/directfarmmarket%26tourbook.pdf Google Scholar