This article examines the Japanese market for salmon. This market is of interest, since it is the largest and most diversified salmon market in the world with wild and farmed species, from Europe and South and North America, competing in the same market. In contrast to the European Union (EU)- and U.S.-markets, there have been neither trade conflicts nor trade restrictions. The Japanese market can hence provide information about the impact of bringing substantial quantities of a new product into a market, and the effect of large-scale aquaculture on traditional fisheries. In this article, market integration between wild and farmed salmon on the Japanese market is examined, using both bivariate and multivariate cointegration analysis. Tests for the Law of One Price are also conducted. The results indicate that the species are close substitutes on the market, and that the expansion of farmed salmon has resulted in price decreases for all salmon species. Copyright 2005 International Association of Agricultural Economics.

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

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