An Analysis of Factors Influencing African Indigenous Vegetable Farmers’ Bargaining Power: A Case Study from Zambia


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Yu Z., Xu H., Govindsamy R., Van Wyk E., Özkan B., Simon J. E.

TARIM BILIMLERI DERGISI, cilt.30, sa.1, ss.193-204, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.15832/ankutbd.1239590
  • Dergi Adı: TARIM BILIMLERI DERGISI
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.193-204
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Growing African Indigenous Vegetables (AIVs) is an innovative way to address poverty and malnutrition problems in Zambia. Farmers’ bargaining power plays an important role in increasing AIV production and farmers’ income. Based on 300 responses from Zambian AIV farmers, we defined AIV farmers’ bargaining power and analyzed its benefits to farmers and the AIV industry. We used the ordered logistic regression model (OLRM) to analyze the influence of several factors that contribute to farmers’ bargaining power, and then use the interpretative structural modeling (ISM) to analyze the relationship and hierarchical structure between the effects. Four key results and innovations arose from the analysis of the data. Growing African Indigenous Vegetables (AIVs) is an innovative way to address poverty and malnutrition problems in Zambia. Farmers’ bargaining power plays an important role in increasing AIV production and farmers’ income. Based on 300 responses from Zambian AIV farmers, we defined AIV farmers’ bargaining power and analyzed its benefits to farmers and the AIV industry. We used the ordered logistic regression model (OLRM) to analyze the influence of several factors that contribute to farmers’ bargaining power, and then use the interpretative structural modeling (ISM) to analyze the relationship and hierarchical structure between the effects. Four key results and innovations arose from the analysis of the data. First, we defined farmers’ bargaining power through their self-reported bargaining power. Second, we found that the respondents’ bargaining power was significantly influenced by seven variables: age, gender, education, main trading partners, awareness of AIV prices, and distance to the market from the farm. Second, the main trading partners and awareness of AIV prices are surface direct factors, gender, education and distance to the market from the farm are middle indirect relationships, and age, belong to any community are deep root factors. Last, farmers’ bargaining power can be improved through education, especially women’s education level, strengthening farmers’ organization construction, altering some of the farmers’ trading methods, and developing infrastructure.

Keywords: AIVs, Negotiating prices, Selling prices, Influencing factor analysis, Profitability