Process information
| Key Data Set Information | |
| Location | ND-FJ-CN |
| Geographical representativeness description | The prefecture-level city of Ningde comprises a group of districts and counties, of which Fuding city (A), Xiapu County (B), Fu’an city (C), and Jiaocheng district (D) are next to the sea and were included in our assessment. A total of 321 households were randomly sampled in the area in August 2017 and January 2018, and we analyzed the data from 292 households that had mariculture production during 2016. |
| Reference year | 2017 |
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| Use advice for data set | When using this data set, attention should be paid to the geographic specificity of the data, extracted from the prefecture-level city of Ningde. Users should consider the seasonal and regional variability in aquaculture practices. Care should also be taken to include or exclude energy and feeds production-related impacts according to the aim of the study. Inclusion of appropriate life cycle stages (e.g., breeding, feeding) depending on the scope of the assessment is essential to maintain accuracy and relevance of the findings. |
| Technical purpose of product or process | The data set represents the process of breeding large yellow croaker seedlings in cage culture systems using formulated feed and trash fish. It can be applied to studies and assessments of aquaculture sustainability, especially concerning the production chain of large yellow croakers, and provides insight for improvement in similar mariculture operations. Appropriate applications include environmental impact assessments, resource use analysis, and evaluation of production efficiency in aquacultural practices. |
| Classification |
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Hierarchy level
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| General comment on data set | The system includes energy production (red), feed production (green), the main chain of aquaculture production (blue), and the production of other inputs (purple). |
| Copyright | No |
| Owner of data set | |
| Quantitative reference | |
| Reference flow(s) | |
| Functional Unit | 1 live-weight ton of large yellow croaker |
| Time representativeness | |
| Data set valid until | 2018 |
| Time representativeness description | 文献未具体说明采样时间，研究者于 2017 年 8月份和2018年1月份在宁德市沿海进行入户访谈The literature did not specify the sampling time. The researchers conducted household interviews in August 2017 and January 2018 in the coastal areas of Ningde City |
| Technological representativeness | |
| Technology description including background system | The cage seedling cultivation technology of large yellow croaker, the sea area is selected as small and medium wave area, the water depth at low tide is ≥8 m, the flow rate is smooth, the flow rate is less than 1.5 m/s, and the flow direction is smooth and stable. The sea area with high flow rate which can be controlled by technical measures can also be used as aquaculture area. Water quality should comply with the provisions of GB 11607, water temperature: 8 ℃ ~ 31 ℃; Salinity: 13 ~ 35. The area of a single cage is 16 m 2 ~ 100 m 2, and the net depth is 4.0m ~ 8.0m. Net mesh 3 mm ~ 15 mm, adjust the size according to the specifications of the fish, in order not to escape the fish as the principle.Breeding feed includes large yellow croaker compound feed and fresh and mixed fish ingredient bait. |
| Flow diagram(s) or picture(s) | |
| Mathematical model | |
| Model description | In order to account for the variability of the studied system, Monte Carlo simulations were performed in R software (v.3.3.2) to model the correlation between unit process variables in LCA using copula function, which solved the correlation between process variables.Monte Carlo simulations were used in R software, with each cell process represented by a set of input parameters, a correlation matrix of geographical location and growth stages, 2000 iterations were used in the simulation, and the variability of the cell process due to inherent uncertainty, dispersion and unrepresentativeness was considered. Inherent uncertainty is included by assuming a coefficient of variation of 5%, spreads are calculated from primary and secondary data, and a pedigree approach is used to address unrepresentativeness. Dependencies between process chains are included by using dependency sampling in each iteration, as well as dependencies between processes |
Modelling and validation
Administrative information
Inputs and Outputs