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A latent class modelling approach to evaluating farmers’ preferences for pona seed yam certification systems and their willingness to pay in Ghana

A latent class modelling approach to evaluating farmers’ preferences for pona seed yam certification systems and their willingness to pay in Ghana

Boadu, Paul, Aidoo, Robert, Ohene-Yankyera, Kwasi, Kleih, Ulrich, Abdoulaye, Tahirou, Orchard, John and Maroya, Norbert (2019) A latent class modelling approach to evaluating farmers’ preferences for pona seed yam certification systems and their willingness to pay in Ghana. International Journal of Agricultural Extension and Rural Development Studies, 6 (1). pp. 1-25. ISSN 2058-9093 (Print), 2058-9107 (Online)

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Abstract

The study employed choice experiment and latent class model to assess farmers’ preferences for seed yam certification system and their willingness to pay for certified seed yam in selected yam producing Districts in Ghana. A total of 9120 choice experiments were conducted to elicit data from 380 yam farmers. The study identified three classes/ market segments of farmers regarding preferences for Pona seed yam. The results show that farmers have more utility towards fully certified seed yam and are willing to pay GH¢719.60 (US$189.4) for a bunch (100 tubers weighing about 45kg) of fully certified seed yam. However, farmers were found to have high utility towards medium-sized Pona seed yam and are willing to pay a premium of GHC¢12.5 (US$3.3) for this attribute. The study has demonstrated high potential for the commercialization of seed yam production in Ghana through a formal seed yam certification system.

Item Type: Article
Uncontrolled Keywords: Choice Experiment, Latent Class Modelling, Market Segmentation, Seed Yam, Willingness to pay
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department
Related URLs:
Last Modified: 11 Jan 2019 17:05
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/22727

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