Agricultural decision support systems (DSSs) are mostly focused on increasing the supply of individual soil functions such as, e.g., primary productivity or nutrient cycling, while neglecting other important soil functions, such as, e.g., water purification and regulation, climate regulation and carbon sequestration, soil biodiversity, and habitat provision. Making right management decisions for long-term sustainability is therefore challenging, and farmers and farm advisors would greatly benefit from an evidence-based DSS targeted for assessing and improving the supply of several soil functions simultaneously. To address this need, we designed the Soil Navigator DSS by applying a qualitative approach to multi-criteria decision modeling using Decision Expert (DEX) integrative methodology. Multi-criteria decision models for the five main soil functions were developed, calibrated, and validated using knowledge of involved domain experts and knowledge extracted from existing datasets by data mining. Subsequently, the five DEX models were integrated into a DSS to assess the soil functions simultaneously and to provide management advices for improving the performance of prioritized soil functions. To enable communication between the users and the DSS, we developed a user-friendly computer-based graphical user interface, which enables users to provide the required data regarding their field to the DSS and to get textual and graphical results about the performance of each of the five soil functions in a qualitative way. The final output from the DSS is a list of soil mitigation measures that the end-users could easily apply in the field in order to achieve the desired soil function performance. The Soil Navigator DSS has a great potential to complement the Farm Sustainability Tools for Nutrients included in the Common Agricultural Policy 2021–2027 proposal adopted by the European Commission. The Soil Navigator has also a potential to be spatially upgraded to assist decisions on which soil functions to prioritize in a specific region or member state. Furthermore, the Soil Navigator DSS could be used as an educational tool for farmers, farm advisors, and students, and its potential should be further exploited for the benefit of farmers and the society as a whole.

Decision support systems (DSSs) enable the optimisation of pesticide application timing to increase pesticide efficacy and thus reduce pesticide use without compromising yield quality and quantity. Limited access to information about available DSSs for use in integrated pest management (IPM) is a major barrier to the uptake of DSSs for IPM across Europe. To overcome this barrier, a typology for DSSs for IPM in Europe was developed, introducing a systematic approach to describe the ever-growing number of DSSs for IPM. The developed IPM-DSS typology was implemented in the free web tool “IPM Adviser”, where currently 79 IPM DSSs are described with over 50 attributes describing their structural and performance characteristics. The information about IPM DSSs, which was previously scattered on different websites and difficult to compare, is now standardised and presented in a uniform way, so that it is possible to compare different IPM DSSs on the basis of all the attributes described. The presented IPM-DSS typology implemented in the web tool IPM Adviser facilitates the dissemination and uptake of DSSs for IPM and thus contributes to the achievement of the EU targets for the sustainable use of pesticides.

Primary productivity is in the foundation of farming communities. Therefore, much effort is invested in understanding the factors that influence the primary productivity potential of different soils. The International Long-Term Ecological Research (ILTER) is a network that enables valuable comparisons of data in understanding environmental change. In this study, we investigate three ILTER cropland sites and one long-term field experiment (LTE) outside of the ILTER network. The focus is on the influence of different management practices (tillage, crop residue incorporation, and compost amendments) on primary productivity. Data mining analyses of the experimental data were carried out in order to investigate trends in the productivity data. We generated predictive models that identify the influential factors that govern primary productivity. The data mining models achieved very high predictive performance (r > 0.80) for each of the sites. Preceding crop and crop of the current year were crucial for primary productivity in the tillage LTE and compost LTE, respectively. For both crop residue incorporation LTEs, plant-available Mg affected productivity the most, followed by properties such as soil pH, SOM, and the crop residue management. The results obtained by data mining are in line with previous studies and enhance our knowledge about the driving forces of primary productivity in arable systems. Hence, the models are considered very suitable and reliable for predicting the primary productivity at these ILTER sites in the future. They may also encourage researcher-farmer-advisor-stakeholder interaction, and thus create enabling environment for cooperation for further research around these ILTER sites.