Timo A. Räsänen

Soil hydraulic properties are essential information for sustainable agriculture and environmental management, yet their measurement is time-consuming and costly. Pedotransfer functions (PTFs) are widely used to estimate these properties from basic soil data, but few have been developed or validated for boreal conditions. This study evaluated two sets of European PTFs for boreal arable soils using a Finnish dataset of 800 water retention curves (5530 measurements) and 293 saturated hydraulic conductivity (Ksat) measurements. To assess PTF performance in modelling context, a functional evaluation with HYDRUS-1D was conducted for clayey fields, comparing simulations using measured and PTF-predicted water retention data. Performance of PTFs was reasonable in terms of water retention (RMSE 0.07–0.09 cm³ cm⁻³ and R2 0.63–0.79) and functional evaluation (R2 0.56–0.97). These results were comparable to previous studies, indicating suitability for boreal arable soils. A hierarchy of PTF performance is provided to guide their selection, while highlighting the importance of considering soil texture in their application. Ksat PTFs performed poorly and systematically underestimated measured values (R2 -2.17 – -0.86), which highlights the demand for further development before reliable application to boreal soils.

Soil conditioning materials, gypsum, structure lime and pulp and paper mill sludges, have emerged as promising means to combat soil losses for protecting waterways. The mechanisms of impact vary between these materials, whereby the selection of the most efficient product depends on the soil type. In this paper, a soil‐properties based selection method was developed for these three soil conditioners focusing on water quality impacts, specifically on particle associated phosphorus (P) losses that make the major share of the P transfer to the eutrophied Archipelago Sea. For targeting soil conditioner applications, a risk grading protocol identifying field parcels with the highest emission potential was formulated. The soil features considered in the risk grading were soil P status, aggregate stability, erosion potential, and typical soil management. In assessing the suitability of the individual soil conditioners, the soil properties considered were electrical conductivity (EC), pH, concentrations of exchangeable calcium (Ca) and magnesium (Mg), soil test P, clay percentage, organic carbon (C) content, and the ratio of clay/C. The method was applied in the Savijoki catchment in Southwest Finland mainly consisting of relatively flat, non‐calcareous clay soils low in soil test P and high in clay/C ratios. Phosphorus emission risk in the area was estimated to be moderate though sporadic high‐risk areas were identified. The applied selection criteria targeted fibres to 61%, structure lime to 29% and gypsum to 11% of the case study area. High clay/C ratio favoured fibres, whereas gypsum and structure lime were limited to soils with low EC. The proposed method provides means for farmers and advisors to select the most suitable soil conditioner for a given soil. For decision‐makers, the method offers an instrument for policy recommendations serving, for example, in directing environmental subsidies. The method is applicable beyond the Archipelago Sea region though the suggested limit values need to be adjusted site‐specifically.

Soil health and climate change are increasingly impacting agriculture across Europe. Decision support tools (DSTs) have emerged as essential tools to help make accurate, evidence‐based agricultural decisions aimed at enhancing productivity, profitability, and effective soil health management. Nevertheless, the adoption of these tools remains limited among farmers and varies across different regions. The aim of this study was to investigate, through a participatory approach, the challenges and drivers influencing the uptake and adoption of DSTs for sustainable soil management in Europe and Turkey. Our goal was to engage various stakeholders to identify the challenges and drivers associated with this process and collaboratively develop actionable recommendations to expand the application of DSTs. Multi‐stakeholder workshops were conducted in Italy, Latvia, Sweden and Turkey, bringing together end‐users, farmers, advisors, tool developers, researchers and policymakers from diverse agricultural contexts. The discussions during these workshops addressed the current use of DSTs, barriers to adoption, and potential solutions for scaling their use. The findings revealed that the primary barriers to adoption and scaling included costs, tool viability, data complexity, limited access to technical support, compatibility issues and insufficient marketing. To overcome these barriers and enhance the appeal of DSTs to users, stakeholders highlighted the necessity for user‐friendly, affordable tools that offer enhanced transparency, real‐time information, and adaptability to local farming conditions. Furthermore, stakeholders emphasised the importance of user‐driven designs that could stimulate the innovation process and consider the interactions between human and technology. This study emphasises the complexity of adopting and scaling DSTs and the need for considering the local agricultural context. Stakeholder insights were categorised into a set of recommendations such as defining the scope of DSTs application, enhancing capacity building, creating a road map for stakeholders, and considering regional disparities in the participatory implementation process. A systematic, participatory approach is essential for addressing the different dimensions of the DST adoption and scaling process while taking into account regional differences in conditions and user needs.

Decision support tools (DSTs) are crucial in aiding agricultural decision‐making, particularly in improving soil health by enhancing nutrient management, soil organic matter (SOM) and water retention. Despite the availability of numerous DSTs in Europe, their adoption, effectiveness and development needs are not well understood, as most research is based on literature reviews rather than direct feedback from stakeholders. This study aims at filling this gap by conducting an expert survey of the most widely used digital DSTs across Europe on SOM, water retention and nutrient use efficiency in agriculture. We aimed at evaluating the current use, limitations and development needs of DSTs and offering recommendations to improve the effectiveness and adoption of DSTs in the context of soil health. A questionnaire was distributed to experts in 24 countries. Answers were received from 18 countries, including 14 European Union (EU) nations, Norway, the UK, Switzerland and Turkey. A total of 115 DSTs were identified aligning with our definition of DST, with agronomists, consultants and farmers being the primary users. Adoption of DSTs was rated moderate (score: 3.1/5), with tools featuring user‐friendly interfaces and alignment with farmer goals achieving higher adoption rates. DSTs were rated better suited to achieve farm‐level goals (score: 4.1/5) than regional (score: 3.6/5) or national objectives (score: 3.5/5). Major barriers to adoption included limited end‐user involvement in DST development, which may hinder alignment with practical needs. Considering all the received questionnaires, the most frequently cited areas for improvement were nutrient use efficiency (45%), SOM (24%) and water retention (18%). Respondents emphasised the need for better integration of new farming systems (e.g., organic farming, agroforestry), more detailed process descriptions, integration of multiple processes, inclusion of economic modules and improved user interfaces. This study presents the first comprehensive evaluation of DSTs in Europe, revealing a diverse yet moderately adopted landscape. Increasing user engagement, enhancing technical integration and improving accessibility are essential for promoting a wider use of DSTs to improve soil health. By adopting these recommendations, DSTs can play a key role in achieving the EU's sustainability goals, fostering resilient agricultural systems and addressing environmental challenges such as soil degradation and climate change.

Spatial information on the distribution of erosion areas and sediment transport pathways within agricultural landscapes is limited. Thus, we assess structural sediment connectivity via surface runoff by using a digital elevation model (2 × 2 m²) and RUSLE-based erosion estimates to compute index of connectivity (IC) and sediment delivery estimates. The variables were analyzed within and between two topographically contrasting subcatchments. We found greater spatial variability of IC within a subcatchment than between the subcatchments. The majority of field parcel areas (65%–97%) were structurally connected to adjacent open ditches and streams. Areas with high erosion estimates also tended to be structurally well-connected, both at the pixel (Pearson r = 0.58–0.63) and parcel scale (r = 0.49–0.67). The IC model was not highly sensitive to parameter variations. In contrast, the magnitude of sediment delivery estimates was highly sensitive to parameter variations. However, based on the high rank correlation (Spearman r_s > 0.95) between computed sediment delivery estimates, the tool provided consistent information on potentially high sediment delivery areas. More empirical data and dynamic model applications could be applied to improve the accuracy of the estimates. The method provides a feasible tool to generate open data on connectivity.

Soil erosion reduces the sustainability of agricultural sector by loss of productive soil and through negative impacts on surface waters. In Finland, considerable efforts have been made to reduce soil erosion, but the suspended sediment loads to surface waters have not markedly reduced. A major limitation has been the lack of high-resolution data on erosion risk for efficient targeting of the erosion management efforts. In this study, by using the Revised Universal Soil Loss Equation (RUSLE) a two-meter resolution erosion risk data was developed and consequently the spatial distribution of the erosion risk of Finnish agricultural land was analysed. With agricultural management practices of 2019, the average erosion of agricultural land was estimated to be 430 kg ha⁻¹ yr⁻¹, and it varied at the municipality scale from 100 to 1290 kg ha⁻¹ yr⁻¹. At more local scales the erosion risk had even greater variability, and areas with high erosion risk were differently located in terms distances to water bodies. The results also suggest that the past erosion management efforts have not been well-targeted according to the actual erosion risk. Altogether, the results indicate that erosion mitigation measures can be improved by inclusion of high-resolution data in the planning and implementation of the measures, by considering the spatial variability of the erosion risk over multiple spatial scales, and by implementation of location specific erosion reduction measures.

Soil erosion reduces the sustainability of agricultural sector by loss of productive soil and through negative impacts on surface waters. In Finland, considerable efforts have been made to reduce soil erosion, but the suspended sediment loads to surface waters have not markedly reduced. A major limitation has been the lack of high-resolution data on erosion risk for efficient targeting of the erosion management efforts. In this study, by using the Revised Universal Soil Loss Equation (RUSLE) a two-meter resolution erosion risk data was developed and consequently the spatial distribution of the erosion risk of Finnish agricultural land was analysed. With agricultural management practices of 2019, the average erosion of agricultural land was estimated to be 430 kg ha⁻¹ yr⁻¹, and it varied at the municipality scale from 100 to 1290 kg ha⁻¹ yr⁻¹. At more local scales the erosion risk had even greater variability, and areas with high erosion risk were differently located in terms distances to water bodies. The results also suggest that the past erosion management efforts have not been well-targeted according to the actual erosion risk. Altogether, the results indicate that erosion mitigation measures can be improved by inclusion of high-resolution data in the planning and implementation of the measures, by considering the spatial variability of the erosion risk over multiple spatial scales, and by implementation of location specific erosion reduction measures.