Khairani Mardhiah
Indonesian Ministry of National Development Planning, South Jakarta, Jakarta, Indonesia

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Factors Influencing Fishermen to Process Their Catches Into Value-Added Products - A Case Study of Banyuwangi Regency Khoirina Fajriani; Khairani Mardhiah
Jurnal Perikanan UGM (Journal of Fisheries Sciences) Vol 28, No 1 (2026)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfs.110894

Abstract

The fisheries sector plays a crucial role in supporting global food security and national economic growth, including in Indonesia. However, despite increased production and exports, the welfare of traditional fishermen remains lagging due to the suboptimal downstream processing and the unequal distribution of value within the fisheries supply chain. Fishermen often receive the smallest share of the economic value from their catch. This study aims to identify the factors influencing fishermen’s decisions to process their catch into value-added products to increase their income in Banyuwangi Regency. Banyuwangi Regency was selected because it has the third-highest fishery production in East Java Province, possesses several other prominent sectors-particularly tourism-and is located near the ferry port connecting Java to Bali. This research employs a quantitative approach using logistic regression analysis. Data were collected through questionnaires distributed to 120 fishermen in Banyuwangi Regency. The analysis results show that, to some extent, product processing training has a significant and positive impact on encouraging fishermen to add value to their catch. The logistic regression results show that processing training has a significant positive effect on fishermen’s decision to process their catch (B = 5.709; p = 0.019; Exp(B) = 301.670). Other variables, including marketing training, gadget ownership, education level, fishermen’s income, and social media usage, were not statistically significant individually (p > 0.05), but simultaneously contributed to the model (χ² = 28.043; p < 0.001). The model explains 63.6% of the variation in fishermen’s processing decisions (Nagelkerke R² = 0.636).