Increased mobility of people around the globe has facilitated transferring species to new environments, where some have found suitable conditions and even become invasive. False indigo‐bush (Amorpha fruticosaL.) is a plant native to North America but has intentionally or unintentionally spread over the Northern Hemisphere, where it often becomes invasive. The plant is especially easily dispersed within the watersheds of large rivers, where seasonal flooding is regular. Seeds and other propagules are buoyant, and when the water recedes, new plants emerge, forming dense thickets where only a few other species can co‐exist. In order to sustain native biodiversity, spread control is needed. However, mechanical control and eradication measures currently in use are labor demanding and costly, while application of herbicides is limited. On the other hand, the plant possesses a number of beneficial properties, such as phytochemical applications (medical and insecticidal effects), biocoenotic uses (honey plant, ornamental features), and ecosystem services (soil stabilization, provision of food for animals, and fiber and biomass for industry, e.g., nanocellulose). For the reasons above mentioned, the plant is considered quite controversial, and the paper discusses both aspects: potential detrimental effects when introduced to new habitats and its beneficial uses for human society. In addition, the paper presents alternative measures of spreading control (e.g., grazing) and argues that exploiting it for beneficial purposes might help spread control, thus covering the expenses of controlling its distribution.

Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone.

The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r² = 0.65, y = 4.01 tha⁻¹; r² = 0.70, y = 4.28 tha⁻¹) than of III-S (r² = 0.012, y = 3.54 tha⁻¹; r² = 0.020, y = 3.7 tha⁻¹). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period.

The study evaluated nine empirical methods for estimating reference evapotranspiration (ET_o) in Bosnia and Herzegovina (BiH) across different climatic zones. The methods compared were the Hargreaves–Samani method (HS), the modified Hargreaves–Samani method (HM), the calibrated Hargreaves–Samani method (HC), the Priestley–Taylor method (PT), the Copais method (COP), the Makkink method (MAK), the Penman–Monteith method based on air temperature and overall average windspeed (PMT₂), the Penman–Monteith method based on air temperature and regional average windspeed (PMT_1.3), and the Penman–Monteith method based on air temperature and site-specific windspeed (PMT_lok). These methods were tested against the “Food Agricultural Organization-Penman Monteith approach” (FAO-PM). The evaluation was performed using data from 20 meteorological stations in BiH, considering a common irrigation season (April–October) for two periods (2000–2005 and 2018–2022). The stations represented three climatic zones: semi-arid (SA), dry sub-humid (DSH), and moist sub-humid (MSH). The performance and ranking of the ET_o methods were analyzed using the TOPSIS method. The trend of ET_o during the common irrigation season for the period from 2018 to 2022 was determined using the Mann–Kendall test. The results of the study indicated that the HC method showed the best performance across all three climatic zones. The average root mean square error (RMSE) was 0.67 mm day⁻¹, 0.49 mm day⁻¹, and 0.50 mm day⁻¹ for the SA, DSH, and MSH zones, respectively. As an alternative to the HC method, the PT method is recommended for its favorable results in both periods and in all zones. On the other hand, the HS method exhibited the highest average overestimation, particularly in the MSH zone, where ET_o values were 18% higher compared with those of the FAO-PM method. The COP method also showed high overestimation and was not recommended for use. Regarding the MAK method, it resulted in underestimation during the period from 2000 to 2005, ranging from 17% in the DSH zone to 11% in the MSH zone. However, its performance improved during the period from 2018 to 2022, for which it ranked second place in the MSH zone. Among the PMT methods, the PMTl_ok, which utilized local average windspeed, yielded the best results. Despite performing well in the neighboring country of Serbia, the HM method showed poor overall performance in BiH. The findings of this study can serve as a foundation for further research in BiH to enhance irrigation practices in response to climate changes.

Floodplains provide an extraordinary quantity and quality of ecosystem services (ES) but are among the most threatened ecosystems worldwide. The uses and transformations of floodplains differ widely within and between regions. In recent decades, the diverse pressures and requirements for flood protection, drinking water resource protection, biodiversity, and adaptation to climate change have shown that multi-functional floodplain management is necessary. Such an integrative approach has been hampered by the various interests of different sectors of society, as represented by multiple stakeholders and legal principles. We present an innovative framework for integrated floodplain management building up on ES multi-functionality and stakeholder involvement, forming a scientifically based decision-support to prioritize adaptive management measures responding at the basin and local scales. To demonstrate its potential and limitations, we applied this cross-scaled approach in the world's most international and culturally diverse basin, the Danube River Basin in Europe. We conducted large-scale evaluations of anthropogenic pressures and ES capacities on the one hand and participatory modelling of the local socio-ecohydrological systems on the other hand. Based on our assessments of 14 ES and 8 pressures, we recommend conservation measures along the lower and middle Danube, restoration measures along the upper-middle Danube and Sava, and mitigation measures in wide parts of the Yantra, Tisza and upper Danube rivers. In three case study areas across the basin, stakeholder perceptions were generally in line with the large-scale evaluations on ES and pressures. The positive outcomes of jointly modelled local measures and large-scale synergistic ES relationships suggest that multi-functionality can be enhanced across scales. Trade-offs were mainly present with terrestrial provisioning ES at the basin scale and locally with recreational activities. Utilizing the commonalities between top-down prioritizations and bottom-up participatory approaches and learning from their discrepancies could make ecosystem-based management more effective and inclusive.