Abstract The high levels of phosphorus (P) occurring through human activities contribute to the eutrophication. Hence, for mitigating the eutrophication, it is important to understand the quantity of P flows of the different components of local socioeconomic ecosystems, and how the components interlink. In this study, the static substance flow analysis (SFA) model was used to determine the P flows in the socioeconomic ecosystem of Feixi County in 2008, which has typical features of central China. Based on the model, the P flows of six subsystems were identified and quantified: product manufacturing, crop farming, large-scale breeding, domestic breeding, rural consumption and urban consumption. The data and parameters, required for the calculation of P flows in the study, were obtained from official statistical database, questionnaires, face to face interviews and published literature. The study showed that the three largest P flows were imports to the large-scale breeding subsystem, P loss into water of crop farming subsystem, and excrement of large-scale livestock applied to farmland. Moreover, the total amount of P drained from Feixi to the surrounding water environment was mostly originated from the crop farming and large-scale breeding subsystems. Furthermore, due to the low efficiency in P recycling and reuse recorded in this study and the high levels of eutrophication caused by P flows in Feixi, corresponding environmental management strategies to reduce P loss to surrounding water environment have been suggested, which include reducing the application of chemical fertilizers, applying the livestock excrement effectively for planting, substituting the crop feed for the chemical feed, improving the infrastructure for wastes disposal of breeding enterprises, developing livestock manure for other use, purchasing P-chemical materials and products with lower P-containing rate, constructing waste treatment facilities, investing and researching for developing technology on eutrophication reduction, etc. Finally, the quantifiable science-based methods used in this study may be applied toward improving the ecological management efficiency of cities/counties and water quality of the surrounding area.