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The term ”Agriculture 4.0” emerged from the term “Industry 4.0” like amany other “4.0” terms. However, are Industry 4.0 technologies and concepts really applicable to agriculture? Are the benefits that Industry 4.0 brings to industrial use cases transferable to livestock farming? This paper tries to answer this question for the three dominant sectors of livestock farming in Central Europe and Germany: Poultry, pig fattening, and dairy farming. These sectors are analyzed along with the eight most relevant Industry 4.0 benefits. The results show that only part of the Industry 4.0 benefits are relevant for livestock farming in a similar manner as in industrial production. Due to basic differences between industrial and livestock farming use cases, some of the benefits must be adapted. The presence of individual living animals and the strong environmental impact of livestock farming affect the role of digital individualization and demand orientation. The position of livestock farming within the value chain minimizes the need for flexibilization. The introduction and adoption of Industry 4.0 concepts and technologies may contribute significantly to transforming agriculture into something that may be called Agriculture 4.0. Technologies are indispensable for this development step, but vocational education and open-mindedness of farmers towards Industry 4.0 is essential as well.

The organizational aspects of digitalization in the context of agriculture are embedded in a complex interplay of a market-based economy, sustainability, and information security. In the medium term, digital solutions’ success in increasing data transparency and security will depend on organizational adaptation due to value chain-specific data flows. A research project in Lower-Saxony, Germany, is currently applying inter-and transdisciplinary research methods to investigate data flows along agricultural value chains. In the following, the organizational aspects of digitalization of farm businesses exemplified in the value chain of dairy production will be outlined—aligned with current limitations in data processing and sharing based on quantitative and qualitative data available so far.

Digitalisierung und Nachhaltigkeit befinden sich im Kontext der Landwirtschaft in einem komplexen Wechselspiel miteinander. Der Erfolg von digitalen Lösungen, die auf eine Steigerung der Nachhaltigkeit der landwirtschaftlichen Produktion abzielen, wird mittelfristig auch von der Identifikation und Innovationspotentialen entlang wertschöpfungskettenspezifischer Betriebsdatenflüsse abhängen. Die Hebung solcher Potentiale wird aktuell im Rahmen eines niedersächsischen Verbundprojektes durch inter- und transdisziplinäre Forschungsmethoden forciert. Nachfolgend sollen auf der Grundlage bisher vorliegender, quantitativer und qualitativer Daten Innovationspotentiale exemplarisch für die Wertschöpfungskette der Milchviehhaltung skizziert werden. Aufbauend darauf wird anhand von zwei Kreativtechniken aufgezeigt, wie die zuvor identifizierten Innovationspotentiale mittels partizipativer Prozesse in Lösungsansätze kanalisiert werden können.

Plumage condition (PC) is a well investigated welfare-related indicator in laying hens. Different manual scoring systems are well-established to monitor the PC, however manual systems are labour-intensive, subject to an observer bias and its results may not represent the whole flock. Therefore, the objective of this study was to develop and test an automated, image-based system to detect feather damage in laying hen flocks.

Footpad dermatitis (FPD) is an indicator of animal welfare in turkeys, giving evidence of the animals' physical integrity and providing information on husbandry management. Automated systems for assessing FPD at slaughter can present a useful tool for objective data collection. However, using automated systems requires that they reliably assess the incidence. In this study, the feet of turkeys were scored for FPD by both an automated camera system and a human observer, using a five-scale score. The observer reliability between both was calculated (Krippendorff's alpha). The results were not acceptable, with an agreement coefficient of 0.44 in the initial situation. Therefore, pictures of 3,000 feet scored by the automated system were evaluated systematically to detect deficiencies. The reference area (metatarsal footpad) was not detected correctly in 55.0% of the feet, and false detections of the alteration on the footpad (FPD) were found in 32.9% of the feet. In 41.3% of the feet, the foot was not presented straight to the camera. According to these results, the algorithm of the automated system was modified, aiming to improve color detection and the distinction of the metatarsal footpad from the background. Pictures of the feet, now scored by the modified algorithm, were evaluated again. Observer reliability could be improved (Krippendorff's alpha = 0.61). However, detection of the metatarsal footpad (50.9% incorrect detections) and alterations (27.0% incorrect detections) remained a problem. We found that the performance of the camera system was affected by the angle at which the foot was presented to the camera (skew/straight; p < 0.05). Furthermore, the laterality of the foot (left/right) was found to have a significant effect (p < 0.001). We propose that the latter depends on the slaughter process. This study also highlights a high variability in observer reliability of human observers. Depending on the respective target parameter, the reliability coefficient (Krippendorff's alpha) ranged from 0.21 to 0.82. This stresses the importance of finding an objective alternative. Therefore, it was concluded that the automated detection system could be appropriate to reliably assess FPD at the slaughterhouse. However, there is still room to improve the existing method, especially when using FPD as a welfare indicator.