Jonker, Luca (2026) AI-Based Decision-Support in Dutch Dairy Farming: Drivers and Barriers to Adoption. Master thesis, Sustainable Entrepreneurship (SE).
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Abstract
Artificial intelligence (AI) is increasingly discussed as a promising tool for improving decision- making in agriculture. In dairy farming, AI-based decision-support technologies may help farmers optimise feeding strategies, improve efficiency and potentially contribute to more sustainable farm management. However, the adoption of such technologies is not self-evident. This study therefore examines the drivers and barriers in the adoption of AI-based decision- support technologies among Dutch dairy farmers. The study uses a qualitative research design based on semi-structured interviews with eight Dutch dairy farmers. Respondents were presented with a scenario of an AI-based feeding optimisation tool that analyses farm data and provides recommendations on ration and feeding strategy. The interview data were analysed through a deductive–inductive thematic approach. The findings show that dairy farmers are generally open to AI-based decision support, but only under clear conditions. Adoption intention is shaped by the combined evaluation of perceived usefulness, practical feasibility, trustworthiness and economic value. Farmers saw potential value in improved decision support, faster insights, greater objectivity and possible cost savings. At the same time, they expressed concerns about whether such a tool would fit daily farm routines, adequately reflect biological and environmental variability and generate sufficient economic return. Trust emerged as a central condition for adoption and depended on transparency, proof in practice and the retention of farmer judgement. The study concludes that the adoption of AI-based decision-support technologies in Dutch dairy farming is best understood as a conditional and context-dependent process rather than as a straightforward response to technological potential. The findings contribute to the literature on agricultural technology adoption, technology acceptance and trust in AI by showing how these dimensions are intertwined in a dairy farming context. Practically, the study suggests that successful implementation of AI in dairy farming requires tools that are not only technically advanced, but also explainable, feasible and clearly valuable under real farm conditions.
| Item Type: | Thesis (Master) |
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| Name supervisor: | Jong, G. de |
| Date Deposited: | 01 Jul 2026 12:40 |
| Last Modified: | 01 Jul 2026 12:40 |
| URI: | https://campus-fryslan.studenttheses.ub.rug.nl/id/eprint/872 |
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