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Research Article Open access CC BY 4.0

Willingness to Pay for Centralized Modern Locust Bean Processing Equipment in Ekiti State, Nigeria

K. A. Abdu-Raheem, F. M. Oluwatusin, A. O. Kolawole, A. O. Adekunmi, O. C. Ajewole, F. O. Osundare

European Journal of Nutrition & Food Safety · pp. 42–49 · Published 27 Jun 2023

10.9734/ejnfs/2023/v15i71321

Abstract

This study investigated the propensity to pay for modern apparatus processing locust beans in Ekiti State, Nigeria. Utilizing primary data and a multistage sampling strategy, 240 processors were chosen for the study. The collected data were analyzed utilizing descriptive statistics and an inferential statistical instrument, such as Logit and ordinary least square regression analysis. About 72 percent of processors were married and had an average of eight years of experience processing locust legumes. The majority of respondents belonged to a cooperative group (66.7%) and had no access to credit (100%). The average monthly income from processing was N18,000. The majority of respondents (65%) were unwilling to pay for the service of centralized, advanced or contemporary processing equipment. According to the results of the Logit regression analysis, the variables that significantly influence willingness to pay for the service of centralized modern locust bean processing equipment were household income and processing experience (1%), level of education and age (5%), and household size (10%). The Ordinary Least Square (OLS) analysis reveals that household size and education level were significant at the 1% level, while age and marital status were significant at the 5% level and processing experience was significant at the 1% level. The study suggests that processors ready to pay should form a formidable cooperative to secure grants or loans to establish a centralized, contemporary locust processing cottage. The government should educate processors on modern food processing and safety at all levels.

Willingness-to-pay locust bean modern processing logit & OLS regression analysis

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