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Purpose The purpose of this paper is twofold: to present gamified mobile experiences as valid tools for DMOs to enrich the experience of tourists, and to present the benefits provided to DMOs by analytics tools integrated on gamified mobile experiences. Design/methodology/approach Staff from three DMOs have generated a gamified mobile experience using a custom authoring tool designed and developed to fulfil their requirements. This gamified experience has targeted families with children visiting Basque Country during off-peak season. The experience has been validated over a period of seven weeks within a pilot project promoted by the local tourist information offices of the DMOs. Data directly provided by tourists and data gathered from analytic tools integrated on the gamified mobile experience have been analysed to fulfil the research objectives presented on the paper. Findings Both DMOs and tourists can benefit from gamified mobile experiences. The integration of analytics tools to gain insights into the behaviour of tourists can be a relevant information source for DMOs. Research limitations/implications The pilot project has targeted a niche tourism market, families with children visiting Basque Country, and has been running during off-peak season. Further studies focusing on other tourist types and different tourism season and destination types will be required to strengthen the validation of the research objectives presented on this paper. Practical implications The paper promotes both the development of gamified mobile experiences and the inclusion of analytics tools for DMOs to obtain relevant information about tourists and the mobile experiences. Originality/value A gamified mobile experience is generated by DMOs, validated on the basis of experience of real tourists. The analytics tools inside the gamified mobile experience provide DMOs with relevant information.

One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the already achieved results, the current investigations and the still open technical challenges. As an overall conclusion, it can be mentioned that even though in their primary stages in some cases, AI technologies improve decision support at farm level, monitoring conditions and optimizing production to allow farmers to apply the optimal number of inputs for each crop, thereby boosting yields and reducing water use and greenhouse gas emissions. Future extensions of this work will include new concepts based on autonomous and intelligent robots for plant and soil sample retrieval, and effective livestock management.

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