Consumers are becoming increasingly interested in reducing the consumption of animal-based foods for health, sustainability, and ethical reasons. The food industry is developing products from plant-based ingredients that mimic animal-based foods’ nutritional and sensory characteristics. In this study, the focus is on plant-based milk alternatives (PBMAs). A potential problem with plant-based diets is the deficiency of important micronutrients, such as vitamin B12, B2, and calcium. Therefore, an analysis of micronutrients in PBMAs was conducted to assess their nutritional value. The second main focus was on the sensory description of the PBMAs, done by a trained panel, and instrumental assessment to characterize the sensory attributes. Almond drinks met the daily micronutrient requirements the least, while soy drinks came closest to cow’s milk in macro- and micronutrients. The experimentally determined electronic tongue and volatile compound results confirmed the sensory panel’s evaluations and could therefore be used as a method for easy and effective assessments of PBMAs. The PBMAs evaluated in this study could not completely replace cow’s milk’s nutritional and sensory properties. They are products in their own product group and must be evaluated accordingly. Given the variety of products, consumers should experiment and make their decisions regarding the substitution of cow’s milk.

Human food production is the largest cause of global environmental changes. Environmental benefits could be achieved by replacing diets with a high amount of animal-sourced foods with more plant-based foods, due to their smaller environmental impacts. The objective of this study was to assess the environmental impacts of the three most common plant-based milk alternatives (PBMAs)—oat, soy, and almond drink—in comparison with conventional and organic cow milk. Life cycle assessments (LCA) were calculated by the ReCiPe 2016 midpoint method, in addition to the single issue methods “Ecosystem damage potential” and “Water scarcity index”. PBMAs achieved lower impact values in almost all 12 of the calculated impact categories, with oat drink and the organic soy drink being the most environmentally friendly. However, when LCA results were expressed per energy and by the protein content of the beverages, the ranking of the beverages, in terms of their environmental impacts, changed greatly, and the results of PBMAs approached those of milk, particularly with regard to the protein index. The study highlights the importance of considering a broader range of impact categories when comparing the impacts of PBMAs and milk.

The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head–head and head–tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a $MOTA$ score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems.

Centrality parameters in animal trade networks typically have right-skewed distributions, implying that these networks are highly resistant against the random removal of holdings, but vulnerable to the targeted removal of the most central holdings. In the present study, we analysed the structural changes of an animal trade network topology based on the targeted removal of holdings using specific centrality parameters in comparison to the random removal of holdings. Three different time periods were analysed: the three-year network, the yearly and the monthly networks. The aim of this study was to identify appropriate measures for the targeted removal, which lead to a rapid fragmentation of the network. Furthermore, the optimal combination of the removal of three holdings regardless of their centrality was identified. The results showed that centrality parameters based on ingoing trade contacts, e.g. in-degree, ingoing infection chain and ingoing closeness, were not suitable for a rapid fragmentation in all three time periods. More efficient was the removal based on parameters considering the outgoing trade contacts. In all networks, a maximum percentage of 7.0% (on average 5.2%) of the holdings had to be removed to reduce the size of the largest component by more than 75%. The smallest difference from the optimal combination for all three time periods was obtained by the removal based on out-degree with on average 1.4% removed holdings, followed by outgoing infection chain and outgoing closeness. The targeted removal using the betweenness centrality differed the most from the optimal combination in comparison to the other parameters which consider the outgoing trade contacts. Due to the pyramidal structure and the directed nature of the pork supply chain the most efficient interruption of the infection chain for all three time periods was obtained by using the targeted removal based on out-degree.