This paper investigates factors influencing public transport passengers’ pre-travel information-seeking behaviours in a British urban environment. Public transport traveller surveys were conducted to better understand the journey stages at which information was sought and the information sources used. A multivariate explanatory model of pre-travel information-seeking behaviour was developed using binomial logistic regression. Explanatory factors considered include socio-demographics, trip context, frequency of public transport use, information sources used, and smartphone ownership and use. Findings suggest that travel behaviour (5 + trips weekly, and < 1 trip weekly), socio-demographics (unemployment/unknown employment), trip context (journey planning stages, mode of transport), and preferred information sources (Internet site, word-of-mouth, visits to travel shop/centre/library) were significant predictors of pre-travel information-seeking behaviours among surveyed travellers. While the final model found that bus users are significantly associated with the use of Internet sites as a source of pre-travel information, rail users rely significantly on a multiplicity of sources comprising Internet sites, word-of-mouth, and visits to a travel shop/centre/library. The final model suggests that metro (light rail) users tend not to seek pre-travel information. The odds of seeking pre-travel public transport information are 2.512 times greater for respondents who reported < 1 trip per week as opposed to those who reported 5 + trips per week. These findings are relevant for passenger information strategies deployed by operators and authorities and can be used to caution against a “one size fits all” strategy for travel information service provision. Implications and suggestions for future research are discussed.

In this article, we discuss our experience of realizing a prototype IoT-based food safety monitoring solution which integrates inexpensive off-the-shelf open source IoT technology for monitoring food deliveries, semantic services for managing and reasoning about food safety provenance records, and private blockchain networks for persistent and secure storage of semantic provenance graphs. We describe how observation of real-world contexts was used to develop a prototype device, and the results of field trials deploying these prototypes as part of the food delivery process. Results indicate that continuous, context sensitive, trustworthy temperature measurement could provide benefits to multiple stakeholders across the delivery pathway. However, close attention has to be paid to the technology used—as cheap multi-functional IoT devices may produce low quality sensor observations which adversely affect the utility of the overall solution. Our experience also suggests that future food safety management systems may need to include machine-processable guidelines to support analysis of raw sensor data for food safety compliance.

The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. In this paper, we explore the opportunities and challenges of deploying AI-based data infrastructures for sustainability in the agri-food sector by focusing on two case studies: soft-fruit production and brewery operations. We investigate the potential benefits of incorporating Internet of Things (IoT) sensors and AI technologies for improving the use of resources, reducing carbon footprints, and enhancing decision-making. We identify user engagement with new technologies as a key challenge, together with issues in data quality arising from environmental volatility, difficulties in generalising models, including those designed for carbon calculators, and socio-technical barriers to adoption. We highlight and advocate for user engagement, more granular availability of sensor, production, and emissions data, and more transparent carbon footprint calculations. Our proposed future directions include semantic data integration to enhance interoperability, the generation of synthetic data to overcome the lack of real-world farm data, and multi-objective optimisation systems to model the competing interests between yield and sustainability goals. In general, we argue that AI is not a silver bullet for net zero challenges in the agri-food industry, but at the same time, AI solutions, when appropriately designed and deployed, can be a useful tool when operating in synergy with other approaches.

The fatty acid content is a very important feature of the milk that affects the health of consumers. The aim of this study was to estimate cow’s milk fatty acid composition in early vegetative, late vegetative and reproductive phase, with the simultaneous determination of the pasture biomass chemical and botanical composition. The research was conducted on Agrostis castellana semi-natural dry grassland in the vicinity of Podgorica, Montenegro. Cows were put to grazing during the whole experimental period. This pasture was assessed as moderate to good quality and botanically diversified. The chemical composition of the grassland biomass changed with the maturity phase. A significant increase in the content of DM, crude fiber, fat and a decrease in crude protein content were determined throughout three phenological phases (p < 0.05). The content of total milk fat did not show significant variability during first two phases, but it did in the third phase. The content of milk fat and protein was high in all phases, although it is a dry pasture with a poor quality of biomass in the third phase. The content of majority saturated fatty acids (SFAs) increased with grass maturity, while total content of monounsaturated fatty acids (MUFAs) decreased. The polyunsaturated fatty acids (PUFAs) content kept the same level up to the reproductive phase. The phenological phase significantly influenced the content of fatty acids in the third phase (p < 0.05). A notable number of SFAs, MUFAs end PUFAs had the highest content in the second phase