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In recent years, ISO, IFS, BRC and FSSC 22000 standards in the areas of quality, environment and occupational health and safety have been increas-ingly implemented in companies in various industries. The main focus of these developed standards are the processes. But the past shows that the factor human is another very important factor, which should be much more in the middle in organization. The new developed guideline has the human factor in the foreground. In particular, the attitude and awareness of occupational safe-ty and health protection in the behaviour of employees at all levels of the or-ganisation are at the centre of consideration.
Inspired by this approach, a group of experts from the fields of quality scienc-es, standardisation and certification as well as consulting for system-relevant companies in the agricultural and food industry came together to form a committee during the Corona crisis in spring 2020. The common goal is to develop a new standard. The first step is to establish criteria for a guideline.
and establish an evaluation system for several pillars of a House of Total Safety Culture (HSC) tailored to the entire value chains of the agri-food sec-tor. In addition, the essential building blocks of the guideline must be defined. The attitude of managers and employees, their behaviour and their compe-tence form the roof of the standard. The integrated management system with a continuous improvement process forms the foundation of the House of To-tal Safety Culture across the company in value chains. Qualification and communication are the main pillars and thus further elements of the HSC. Particular attention is paid to the fields of action of occupational safety and health protection, food safety, animal and environmental protection as well as sustainability and digitisation. They form the four inner pillars of the House of Total Safety Culture. The design of the respective certification levels is con-sidered as a “construction phase”. As part of the standardisation process, the coordination of the respective requirements for safety culture has not yet been completed. This article first provides an overview of the state of knowledge in relation to the established standards and norms of the agri-food industry with special consideration of the Safety Culture Ladder model. The procedure for developing and establishing the new guideline is then ex-plained. For this purpose, the composition of the expert forum is presented before the concrete steps to establish the guideline are presented. The model of the House of Total Culture is presented below. Building on this, the proce-dure for assessing the degree of maturity and possible concepts of continuing training are explained before the conclusion of this article.
In Deutschland werden jährlich ca. 11 Mio. Tonnen Lebensmittel entlang der Wertschöpfungskette entsorgt.
Die Tafeln verteilen ca. 265 000 Tonnen dieser Lebensmittel
und spielen eine bedeutende Rolle in der Reduzierung von
vermeidbaren Lebensmittelabfällen. Ein Teilziel des Projekts LeMiFair ist es, Einblicke in die Arbeit und die Herausforderungen der Tafeln in Niedersachsen zu gewinnen.
Die Digitalisierung des Bodenbeprobungsverfahrens mit einer automatisierten Generierung einer Düngeempfehlung auf Grundlage der analysierten Bodennährstoffgehalte – direkt nach Beendigung der Bodenbeprobung auf dem Acker – ist ein übergeordnetes Ziel bei der Nutzung des mobilen Feldlabors „soil2data“. Neben den Bodennährstoffanalyse-Ergebnissen sind für die Umsetzung einer automatisierten generierten Düngeempfehlung weitere Informationen notwendig.
Die Quellen dieser Informationen haben einen unterschiedlichen Ursprung. Es sind Daten aus verschiedenen Quellen vom Bewirtschafter, von Dienstleistern und vom mobilen Feldlabor, welche miteinander verknüpft und synchronisiert werden müssen. Für einen automatisierten Prozessablauf zur Generierung einer Düngeempfehlung ist die Datenorganisation eine essenzielle Voraussetzung. Die Grundlage der Empfehlung sind die Tabellenwerke der offiziellen Düngeempfehlung, die bei den für die Düngung zuständigen Behörden der Bundesländer vorliegen. In dieser Publikation werden die notwendigen Daten und der Prozessdatenfluss für die Bodenbeprobung und Düngeempfehlung-Generierung beschrieben und grafisch dargestellt.
Knowledge of the maximum friction coefficient µmax between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µmax estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µmax estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.
Computer-image processing becomes more and more important in the analysis of data in biological and agricultural research and practice. However, robust image processing is highly de pendent on the histogram analysis algorithms used and the quality of the data being processed. The algorithm presented here aims to improve the accuracy of the classification of image data generated under complex boundary situations and inconsistent lighting conditions. Using the example of the determination of nitrogen content of tomato leaves and the qualitative determination of starch con tent of apples on the basis of color image processing, we showed that the developed algorithm is able to perform a robust classification and represents an improvement to simple histogram analysis.
Within the consortium “Experimentation Field Agro-Nordwest”, a practical concept for knowledge and technology transfer of digital competence in agriculture was created. For this purpose, the web-based e-learning system “SensX” was set up, consisting of videos, presentations and instructions. In addition, the classical e-learning concept was extended by data sets, student experiments and sensor data of plants acquired by a remote phenotyping robot. This resulted in a massive open online course (MOOC), which was tested with agricultural and biotechnology students in higher education at the University of Applied Sciences Osnabrück over two years. The evaluation process of “SensX” included an empirical survey, qualitative interviews of the participating students by an external institution and an evaluation of the concept by the lecturers.