Ari Ronkainen

Decision support systems (DSS) in crop protection provide valuable support for pest risk prognosis and recommendations for pest control, enabling farmers to make better-informed decisions. As a part of the European Union’s strategy for the sustainable use of plant protection products, the “IPM Decisions” project developed an online platform that gives farmers and advisors access to a wide range of DSS for major pests, weeds, and diseases in a variety of crops across Europe. Multiple DSS models relevant for different crops and geographical regions of Europe were selected for integration in the platform. Information on the models is compiled into a model catalogue, which serves as a core component of the IPM Decisions platform. To facilitate the use of these models, two application programming interfaces (APIs) were developed. In line with the FAIR (findable, accessible, interoperable, and reusable) principles, the DSS API provides access to models and their metadata, including descriptions of input and output parameters. The weather API enables access to European online weather data sources and adapts this data to meet the requirements of DSS models. While these APIs are integrated into the IPM decisions platform, they are also open source, allowing other crop protection and farm management software to inspect, download, modify, install, run, and use them. In this article, we describe the development of the DSS and weather APIs, outline their structure and definitions, and present the services that DSS API and weather API provide. Finally, we demonstrate their application through three practical use cases.

This study examined agricultural machine accidents in Finland. In Finland, the relative share of the number of machine accidents of all accidents in agriculture has grown. In addition, agricultural machinery causes more than half of farmers' deaths at work. Most of the machine accidents occur when using field machinery. The two main objectives of the study were 1) to reduce farmers' machine accidents and their costs by publishing machine safety information cards in a magazine for farmers, and 2) to evaluate the impact of this safety information on the occurrence of machine accidents by following up accident statistics. In 2017, ten information cards on agricultural machinery safety were published in the Koneviesti farm machinery magazine. The topics of the information cards were the most dangerous farm machinery and how to avoid accidents with these: tractors, seeding and tilling machines, automatic functions in field machinery, trailers, ATVs, combines, front loaders, machinery used in livestock production, personal protective equipment, and machine maintenance work. One year after the intervention, the statistics of accidents caused by these machines were analysed, from ten years before to one year after the intervention. A statistically significant decrease in accident development after the intervention was observed among Koneviesti's farmer subscribers. By contrast, no corresponding decrease in accident development was observed among other farmers who were not subscribers to the Koneviesti magazine. The study produced practical training material on agricultural machinery safety.