Annual Financial Performance, Farming Sample Clauses
Annual Financial Performance, Farming. The seabream farming model is based on data from a combination of data sources and methods. These include data from the AquaVlan model8, as well as STECF data for seabream cage system farming in Spain and trout RAS farming in Finland11. The latter was selected because it was the closest proxy for RAS farming that STECF reported financial information for. That is, there was no information for other RAS farming systems (e.g. seabream, salmon, other marine aquatic animals), and nor did any other country report data for trout RAS farming besides Finland. Therefore, Finnish trout RAS farming was selected to provide some insight into the costs of RAS operations. Where possible, the cost structure of RAS farming in Finland was adapted to Spanish economic conditions. For example, while labour productivity (tonnes per employee) is derived from the Finnish data, annual salary per employee is assumed to reflect the Spanish seabream cage farming case. The reason is twofold: 1) the economic conditions for the Space@Sea case study is more likely to resemble seabream cage farming in Spain, and 2) the Finnish data for RAS farming reports considerable losses (more than 50% of revenue), primarily attributable to high labour cost and other operational costs. This seems unrealistic for farms to continue operation. The AquaVlan model, on the other hand, is a bioeconomic model from an Interreg project about aquaculture in Flanders and the Netherlands developed by LEI (now named Wageningen Economic Research) in collaboration with ▇▇▇▇▇▇ (now named Wageningen Marine Research)8. The model was built from a set of aquaculture models applied in previous projects and utilises cost components collected from literature review and expert consultations12. The final Space@Sea model is the average of RAS farming based on STECF data, adapting the Finnish trout RAS cost structure to economic conditions of Spanish seabream cage farming, and the AquaVlan model for RAS farming. The only exception is capital investment, which is purely based on the AquaVlan model because it contains detailed information breakdown of capital investment (see Table 9). For STECF data, average earnings and cost for seabream cage farming in Spain over the 3 years from 2014 to 2016, inclusive, are used to establish the economic conditions applicable to Space@Sea. As data for trout RAS farming in Finland is only available for 2015 and 2016, the average cost structure of those years is considered in the model. More speci...
Annual Financial Performance, Farming. The calculation of annual revenue and expenditures for mussel farming in Table 3 is based on data extracted from the Scientific, Technical and Economic Committee for Fisheries (STECF) of the European Union1. Average farm data for the years from 2014 to 2016 for Denmark longline mussel culture, and from 2014 to 2015 for bottom culture in the Netherlands are reported in the first two columns of Table 3, standardised to a production output of 1.000 tonnes. The reason for excluding the year 2016 from the Dutch farm data is due to the exceptional poor performance of the industry in that year, primarily attributed to poor mussel quality. Inclusion of the Dutch financial performance for 2016 would cause significant negative distortion to the data, and as such was omitted from the analysis. Annual revenue for longline culture in the Netherlands is calculated by applying the prices from Table 1 in the Scenario Design to the estimated production volume, net of tare. In this case study, no additional revenue from subsidies or work for third parties are assumed. Cost of mussel farming is broken down by operating cost (OPEX) and capital cost (CAPEX). OPEX includes expenditures directly attributable to the day to day farming of mussels on the Space@Sea platform while CAPEX relates to the depreciation, or wear and tear, of machinery and equipment as well as the cost of financing capital (i.e. interest payments). The calculation of these costs is based on the selection of the most appropriate per tonne cost from the Danish longline and Dutch bottom culture data, or a combination of both. For example, the category ‘wages and salary’ uses the annual salary per worker from the Netherlands but the productivity (i.e. tonnes per worker) from the Danish data. This is because while the annual salary is likely to reflect the country of operation, labour productivity is expected to be influenced by the method of farming. For energy use, while it is also likely for the method of farming to be of considerable influence, Dutch cost per tonne is applied owing to the fact that the Space@Sea platform is offshore and would therefore incur additional fuel use compared to the average Danish longline farm. The energy cost per tonne of mussels production is only applied to the seed and juvenile mussels as the energy or transport cost for consumption mussels will be borne by the processing plant. The remaining OPEX items are taken at the average cost per tonne of the Danish longline and Dutch ...