Kano Model Analysis of Digital On-Farm Technologies for Climate Adaptation and Mitigation in Livestock Farming

Abstract

In the EU, agriculture contributes significantly to greenhouse gas (GHG) emissions. In Germany, over half of the GHG emissions from agriculture can be directly attributed to livestock farming. To combat the progressing climate change, GHG emissions must be significantly reduced. Digital solutions, particularly decision support systems (DSS), are promising tools to assist livestock farmers in achieving the globally agreed GHG reduction goals. However, there is a lack of studies addressing DSS requirements for reducing GHG emissions in livestock on the farm level. Users’ feedback on technologies can support identifying areas for enhancement and refinement. This study identifies, categorizes, and ranks fourteen DSS features aimed at supporting GHG reduction based on their impact on customer satisfaction. A quantitative online questionnaire using the Kano model surveyed livestock farmers’ satisfaction or dissatisfaction levels with these features. Results gathered from 98 responses across German federal states highlighted the significance of data authority and integrability, with their absence causing dissatisfaction. Multi-target optimization emerged as an attractive feature, positively impacting satisfaction. Connectivity and market perspective, however, appeared indifferent. The findings guide DSS developers in prioritizing attributes crucial for customer satisfaction. It also helps to focus on must-have attributes to preserve customer satisfaction and ensure successful GHG reduction implementation.