Determinants of the mortality of patin fish in Temerloh, Pahang, Malaysia

The aquaculture industry plays a crucial role in meeting the increasing global demand for fish and seafood. In response, the Malaysian government has prioritized investment in this sector. Effective water quality management is essential for successful aquaculture operations, as suboptimal conditions can negatively affect fish health and raise production expenses. This study aimed to examine the links between various factors, including fish species, number of partners, fish sales duration, fish quantity per cage, and daily feeding frequency, and the mortality of Patin fish, the dependent variable. Data were gathered from 135 fish farmers through questionnaires and analyzed using SPSS Software. Correlation analysis was employed to evaluate the relationships between independent and dependent variables, while descriptive analysis techniques, such as frequency analysis, cross-tabulation, and descriptive statistics, were utilized to provide additional insights. Among the five factors investigated, fish species, duration before fish sales, and fish quantity per cage were significantly related to fish mortality. Conversely, the number of partners and daily feeding frequency demonstrated no significant association. In the context of this study, when the study refers to "number of partners", the study is alluding to the count of individuals or entities collaborating in the fish farming venture. Moreover, the study is intertwined with the concept of an effective water quality management system as it emphasizes the significance of optimal water conditions and their direct influence on fish mortality, which is a crucial component of successful aquaculture. These findings hold vital implications for cost management in Malaysia's aquaculture industry and can inform strategies to enhance fish farming practices and water quality control. The results underscore the necessity of considering multiple factors when examining fish mortality and its consequences on water quality. As the global demand for fish and seafood continues to grow, understanding and addressing these factors can contribute to sustainable aquaculture practices. Future research should investigate other factors that contribute to fish mortality and examine the causal relationships between these factors and water quality degradation. In doing so, a more in-depth understanding of these relationships can further improve fish farming practices, encourage sustainable aquaculture, and ensure the industry's long-term success. From a cost management perspective, this study's findings can inform decision-making processes for fish farm operators, allowing them to minimize fish mortality and reduce expenses associated with poor water quality. By relating the findings to the Interactive Governance Theory, Stress Response Theory, and Disease Ecology Theory, the research contributes to a broader understanding of the complex factors impacting aquaculture costs and fish health. The findings from the regression model provide critical insights into the determinants of Patin Fish Deaths. The statistical significance of variables such as Fish Species, Duration before sale, and Quantity Per Cage underscores their influence on mortality rates. Specifically, less popular fish species, extended holding periods before sales, and higher quantities Per Cage appear to increase Patin Fish Deaths. Meanwhile, variables like Daily Eating Frequency, despite its insignificance, should not be discarded without further exploration in broader datasets or varying conditions. The overall model, being statistically significant, serves as an important tool for aquaculture stakeholders. However, the relatively low adjusted R-squared value indicates that there are other unaccounted factors affecting Patin Fish Deaths. Thus, aquaculture practitioners and policymakers need to be cautious and consider that only a fraction of the variability is captured by the current model. In practical terms, while adjusting their strategies based on these findings, stakeholders should also be proactive in exploring other potential factors affecting mortality. The absence of autocorrelation further ensures the reliability of predictions from this model, but a holistic approach in the industry should involve continuous data collection and model refinement.

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