From digital production data to decision support: Insights from participant observations in fruit-growing farm enterprises

• Participatory observations in three German farming enterprises during apple harvest and winter controlling phase • Process from digital data collection to controlling-based decision-making • Requirements for future ICSs derived from FMIS use • Lack of field-specific databases inhibits linking production and monetary data • Main barrier: limited business management expertise, not digital competence • ICS framework combining planning tools, KPIs, a feedback mechanism and a management control loop Apple harvesting is a labor-intensive process and a key lever for farm business optimization. This field study examines the under-researched process from digital production data collection to controlling-based decision-making in the context of apple harvesting. The aim is to analyze the decision-making processes regarding requirements for future information and controlling systems (ICSs). To this end, participatory observations were carried out in three German fruit-growing farm enterprises during harvest and subsequent winter controlling phase as part of a comparative multiple-case design. Interactions with a farm management and information system (FMIS) were examined. The empirical results show that farm managers, motivated by reporting obligations, collect extensive production data using FMIS, but do not create field-specific databases linking with monetary information. Decisions remain predominantly experience-based. Based on the empirical results, an ICS framework was developed for the apple harvesting context. First, the concept of agricultural decision support systems (aDSSs) was expanded to include a business management perspective through the integration of planning tools and key performance indicators. Second, a feedback mechanism within the aDSS enables learning effects that further develop both the data architecture and tool functionality. Third, in decision-making cases, the aDSS is embedded in a higher-level management control loop that coordinates the functions of planning, control, monitoring, and information supply. Overall, the study provides a contextual specification of integrated management control for small and medium-sized enterprises (SME) fruit farms. It combines organizational ethnographic methods with a smart farming perspective and provides guidelines for smart agricultural technologies that integrate business management and production data.