The greatest environmental problem facing the world today is climate change, with a rise in sea level being one of the most important consequences, especially in low-lying coastal areas, such as river deltas where changes are exacerbated by human impacts, leading to increased seawater intrusion into coastal aquifers and the degradation of water quality. Water quality monitoring systems are being developed and deployed to monitor changes in the aquatic environment. With technological progress, traditional sampling-based water monitoring has been supplemented with sensors and automated data acquisition and transmission devices, resulting in the automation of water quality monitoring systems. This paper reviews the recent development and application of automated continuous water quality monitoring systems. It also draws on the results of our own experience in implementing such a system in the Neretva River Delta on the Croatian Adriatic coast. The installed system provides (near) real-time data on parameters such as temperature, pH, EC, TDS, and DO in the water, as well as a number of soil and weather variables, with data available at a high frequency through a developed database and web portal for various stakeholders. Continuous monitoring enables the collection of big data that can be used to develop models for predictions of water quality parameters and to develop guidelines for future management.

Organic agriculture (OA) is a continuously growing global concept that emphasizes the use of sustainable and environmentally-friendly practices. By adopting OA, it is possible to improve ecosystems services, increase biodiversity, decrease environmental pollution, reduce carbon footprints and mitigate greenhouse gas emissions, generating food that is free from harmful residues of agrochemicals, thereby enhancing food safety and security. This study provides a comprehensive review of the latest insights on the global utilization of land resources in OA, focusing particularly on some EU countries that experienced a notable and rapid progress in organic farming during the past two decades. With negligible 1.6% (75 Mha) of global cropland is currently dedicated to OA, there is ample opportunity to expand the adoption of OA and realize its multi-beneficial potential for farmers (by premium prices), and consumers (by healthier and nutritious food). The importance of OA has been recognized by the most recent EU agro-environmental policies and green strategies, with an ambitious goal to have at least 25% of agroecosystems under organic management by 2030. Despite numerous financial supports and a multifold increase in OA land area, many member states are unlikely to achieve this goal, including Croatia, which currently has a share of only 8% (~109,000 ha) of lands in OA. Furthermore, converting conventional land to organic farming has not always led to an increase in value-added final OA products. EU policies related to OA have been focused on the area of land cultivated organically, rather than overall production performance, and financial subsidies have been essential to achieve this policy. Therefore, some of critical obstacles and challenges for OA under rising pressures due to global climate change, public health and geopolitical crises need to be managed by specifically designed policies and regulations, which would contribute to more sustainable OA, i.e., food safety and security.

In polder-type land, water dynamics are heavily influenced by the artificial maintenance of water levels. Polders are low-lying areas of land that have been reclaimed from the sea or from freshwater bodies and are protected from flooding by dikes or other types of flood-protection structures. The water regime in polders is typically managed using a system of canals, pumps, and sluices to control the flow of water in and out of the area. In this study, the temporal changes in water salinity in the polder-type agricultural floodplain within the Neretva River Delta (NRD), Croatia, were analyzed by applying multivariate statistics and forecast modelling. The main aim of the study was to test the model that can be used in practice to forecast, primarily, water suitability for irrigation in a coastal low-lying agricultural catchment. The specific aim of this study was to use hydrochemistry data series to explain processes in water salinity dynamics and to test the model which may provide accurate salinity prediction, or finally select the conditions in which the model can be applied. We considered the accuracy of the model, and it was validated using independent data sets. To describe different patterns of chemical changes in different water classes due to their complex hydrological connectivity, multivariate statistics (PCA) were coupled with time-series analysis and Vector Autoregression (VAR) model forecasting. The multivariate statistics applied here did not indicate a clear connection between water salinity of the surface-water bodies and groundwater. The lack of correlation lies in the complex hydrological dynamics and interconnectivity of the water bodies highly affected by the artificial maintenance of the groundwater level within the polder area, as well as interventions in the temporal release of freshwater into the drainage canal network. Not all individual water classes contributed equally to the dominant patterns of ionic species identified by PCA. Apparently, land use and agricultural management practices in the different polders lead to uneven water chemistry and the predominant contributions of specific ions, especially nutrients. After applying the Granger causality test to reveal the causal information and explain hidden relationships among the variables, only two surface-water and two groundwater monitoring locations displayed a strong causal relationship between water electrical conductivity (EC_w) as an effect and sea level as a possible cause. The developed models can be used to evaluate and emphasize the unique characteristics and phenomena of low-lying land and to communicate their importance and influence to management authorities and agricultural producers in managing and planning irrigation management in the wider Mediterranean area.

Hazelnut orchards are popular for cropping on sloped sites, which are often highly erodible. This study aimed to assess the impact of soil management and season in a hazelnut orchard on soil properties and hydrological response. Three treatments (Tilled, Straw, and Grass) were established in Munije (Croatia) on Stagnosol. In Spring, Summer, and Fall, a rainfall simulation was performed (intensity of 58 mm h⁻¹ for 30 min). Results reveal higher water stable aggregate values were observed for the Straw treatment in all seasons. Higher soil organic matter (SOM) content was noticed for the Grass treatment in all seasons, while lower values were recorded for the Tilled treatment. Sediment loss in Summer was up to 650% and 1300% higher for the Tilled treatment compared with the Straw and Grass treatments. This study strengthens the comprehension of utilizing a permanent ground cover in hazelnut orchards as a sustainable practice, contributing to the mitigation of soil erosion processes and the improvement of soil properties. The Straw treatment is a viable option since it increases soil stability and SOM, consequently preventing high soil erosion.

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha⁻¹. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions.

Soil organic matter (SOM) is a key indicator of soil health in cities. However, its monitoring in heterogeneous urban landscapes is still constrained by th