Nori (Pyropia yezoensis) is one of the most important aquaculture products in Japan and is widely consumed in foods such as sushi and kimbap. It accounts for approximately 19% of the total marine aquaculture production value in Japan, with a market size of about 80 billion yen (approximately 0.5 billion USD). In recent years, color fading and decreases in harvest yield have been reported, resulting in reduced income for farmers. One major cause is the decline in nutrient concentrations in coastal waters. Similar problems have been observed in nori farms around Saijo City, Ehime Prefecture, located in the southwestern Hiuchi-Nada in the Seto Inland Sea. As a countermeasure, nutrient management operation has been conducted since 2011 at a nearby wastewater treatment plant during the cultivation period (October–March), where nutrient concentrations in treated water are increased before discharge. However, large variability in nutrient concentrations has been observed in the surrounding waters, making it difficult to quantitatively evaluate its effectiveness. In addition, clear improvements in nori production have not been confirmed. Increasing nutrient discharge from treated water is currently being considered. To support this approach, it is necessary to clarify the factors controlling long-term variation in nori production and to quantitatively understand material cycling in the aquaculture area, including nutrient competition with phytoplankton and exchanges with rivers and offshore waters. Therefore, this study aims to (1) identify the factors controlling interannual variation in nori production and (2) evaluate nutrient cycling and management effects using observational data analysis and a numerical ecosystem model. This study is the first to integrate observational analysis and ecosystem modeling to quantitatively evaluate nutrient management effects on nori production, linking production variability with material cycling and the role of nori within the ecosystem.
The cumulative nori harvest from January to February for each year (corresponding to the peak production period) was compared with environmental variables including water temperature, salinity, nutrients (DIN, DIP, NO₃, NO₂, NH₄), and river discharge. Average values during the same period were calculated, and linear regression analysis was conducted. In addition, analysis of covariance (ANCOVA) was applied to evaluate differences before and after the implementation of nutrient management. For numerical analysis, an ecosystem model based on eNEMURO (Yoshie et al., 2011) with a nori growth module was developed. External forcings included solar radiation, offshore nutrient concentrations, nutrient inputs from rivers and the wastewater treatment plant, and bottom nutrient fluxes. Simulations were conducted for April 2023 to March 2025. Model performance was evaluated by comparing simulated and observed chlorophyll-a, nutrient concentrations, and nori harvest. Material cycling structure was analyzed, and sensitivity experiments were conducted by varying nutrient input from the wastewater treatment plant.
A significant positive correlation was found between nori production and river discharge (r = 0.65, P < 0.01), indicating that river-derived nutrients are a primary factor controlling interannual variation in production. In contrast, ANCOVA showed no statistically significant effect before and after the implementation of nutrient management, suggesting that wastewater-derived nutrients have not had a clear effect to date. However, nutrient loads from treated water were approximately 50–100% of those from rivers, indicating that they are not negligible. A key difference lies in nitrogen form: approximately 90% of riverine DIN is nitrate, whereas approximately 90% of treated water DIN is ammonium. This difference in nitrogen form may influence nori production. In addition, the relationship between river discharge and nori production has weakened in recent years, possibly due to reduced fishery scale, increased grazing pressure associated with warming, and land-use changes in the watershed. The model reproduced phosphate and phytoplankton variations reasonably well, although nitrate was overestimated and ammonium was underestimated. These discrepancies indicate limitations in representing nitrogen processes in the model; however, the model performance was sufficient for analyzing overall material cycling. The results showed that nutrient consumption by phytoplankton greatly exceeded that by nori, indicating that nutrient dynamics are primarily controlled by phytoplankton. Nitrate supply was mainly derived from rivers, whereas ammonium was largely generated through biological processes. Sensitivity experiments suggested that increasing nutrient discharge from treated water up to five times the current level could restore nori production to levels observed before the recent decline. However, this result remains preliminary and requires further validation.
This study presents an integrated framework for evaluating nutrient management based on nori production rather than nutrient concentrations alone. This framework enables simultaneous evaluation of nori production variability and material cycling, including the influence of nori on the ecosystem. By combining observational analysis and numerical modeling, it clarifies the key factors controlling nori production and the underlying material cycling structure in the aquaculture area. The results highlight the importance of nutrient sources and forms, and suggest that nutrient management should consider nitrogen form as well as nutrient quantity, and that nitrate-rich discharge may be worth considering. The results demonstrate the potential of this approach for evaluating management strategies and predicting future scenarios.