Status quo of adoption of precision agriculture enabling technologies in Swiss plant production

TLDR

Swiss agriculture is undergoing mechanisation and automation, and this study examines the adoption of driver assistance systems and electronic measuring systems. The study aims to describe the current state of precision agriculture technology adoption in Swiss farms, particularly driver assistance and electronic measuring systems. The authors analysed adoption rates and correlated them with farm and farmer characteristics, using descriptive statistics and a binary logistic regression. DAS adoption far exceeds EMS across Swiss farms, is highest among high‑value vegetable growers, lowest among grape growers, and is negatively associated with mountain location and small farm size, indicating that high‑value producers are more likely to adopt PAT, which currently mainly reduces workload but not yet supports performance decisions, while automated data collection is essential for future smart farming.

Abstract

Abstract This paper presents the state of application of Precision Agricultural enabling Technology (PAT) in Swiss farms as an example for small-scale, highly mechanised Central European agriculture. Furthermore, correlations between farm and farmers’ characteristics and technology adoption were evaluated. Being part of a comprehensive and representative study assessing the state of mechanisation and automation in Swiss agriculture, this paper focuses on the adoption of Driver Assistance Systems (DAS) and activities in which Electronic Measuring Systems (EMS) are used. The adoption rate of DAS was markedly higher compared to EMS in all agricultural enterprises. The adoption rate was highest for high-value enterprise vegetables and surprisingly low for the high-value enterprise grapes. The results of a binary logistic regression showed that farmers located in the mountain zone were less likely to adopt PAT compared to farmers in the valley. Small farm size correlated with low adoption rates and vice versa showing adoption happens country-specific in the upper farm size distribution. The results show the potential for novel technologies to be adopted by farmers of high-value products. Furthermore, technologies have been partially used to reduce physical workload but not yet to evaluate crop or management performance to support decisions. However, automatic collection and forwarding of data is a fundamental step towards Smart Farming realizing its full potential in the future.