Value Chain Analysis of Maize in Bhaktapur District, Nepal: Structure, Participants, and Constraints

Abstract

Background: Value chain participants play a vital role in ensuring the smooth flow of agri-food products to meet consumer demand without compromising safety and quality. Understanding the structure, relationships, and challenges within the maize value chain is essential for improving productivity and market efficiency.

Purpose: This study aims to analyze the maize value chain in Bhaktapur District, identifying its structure, major participants, key relationships, and the constraints faced by stakeholders.

Design/Methodology/Approach: The study follows an exploratory research paradigm using value chain analysis theory. Quantitative data were collected from 350 participants in Bhaktapur District, Nepal, using a structured questionnaire via KOBO Toolbox. Purposive sampling was applied, and data were analyzed descriptively and inferentially using ordered logistic regression with STATA and Excel.

Findings: Results indicate a conventional arrangement of the maize value chain with limited innovative connections. Farmers’ knowledge of value addition is positively and significantly associated with their education level, the size of maize cultivation land, and their autonomy in setting selling prices. Key challenges include limited market knowledge and the unavailability of quality inputs.

Conclusion: Enhancing value addition requires interventions to improve market information flows among traders, strengthen input supply systems through governmental and non-governmental support, and promote sales via organized channels. Supportive strategies include farmer education and expanding maize cultivation areas. The study offers insights into policy and practice to improve the maize value chain through refined extension services, capacity-building programs, and better information dissemination. This is a novel study with no potential conflict of interest.

Key words: Value Chain Analysis, Farmer, Traders, Value addition, Ordered logistic Model