The demand for food vegetable oil is rising and this trend is reflected in the agricultural sector of the Czech Republic. The traditional oil crops of the Czech Republic are winter rapeseed and sunflower. These oil crops have high demands on energy inputs, for example, in the form of land preparation and chemical protection. At the same time, they are characterized by high food oil production and oiliness. Moreover, marginal oils crops, such as hemp, are also gaining prominence. This work aimed to evaluate the environmental impacts associated with the cultivation of winter rapeseed and sunflowers based on standard cultivation practices typical of the conditions of the Czech Republic. For comparison, an intensive cultivation strategy for hemp was modelled, also corresponding to the conditions of the Czech Republic. This study assessed the environmental impact of traditional oil crops from the agricultural Life Cycle Assessment (LCA) perspective. The system boundaries included all the processes from the cradle to the farm gate. Mass-based (volume of food oil) and area-based (land demand for generating the same volume of food oil) functional units were employed. The results cover nine impact categories related to the agricultural LCA. ReCiPe Midpoint (H) characterization and normalization models were used for the data expression. Hemp is a plant with generally low demands on the inputs of the growing cycle but generally has a low oil production, which affects the character of the results relating to the goal and scope definition of the study. Hemp food oil thus generated a higher environmental impact per unit of production and area compared to sunflower and rapeseed food oil.

Winter cereal:legume intercropping is considered a sustainable arable farming system not only in temperate regions but also in Mediterranean environments. Previous studies have shown that with suitable crop stand composition, high grain yield can be achieved. In this study, a life cycle assessment (LCA) of the influence of sowing ratio and nitrogen (N) fertilization on grain nitrogen yield of oat (Avena sativa L.) and pea (Pisum sativum L.) in intercrops was performed to find the optimal design to achieve low environmental impact. This study compared the environmental impact of oat:pea intercrops using agricultural LCA. Monocrops of oat and pea and substitutive intercrops, which were fertilized with different levels of N, were compared. The system boundaries included all the processes from cradle to farm gate. Mass-based (grain N yield) and area-based (land demand for generating the same grain N yield) functional units were used. The results covered the impact categories related to the agricultural LCAs. The ReCiPe 2016 Midpoint and Endpoint characterization model was used for the data expression. According to the results, an unfertilized combination of oat and pea (50%:50%) had the lowest environmental impact in comparison with the other 14 assessed variants and selected impact categories. In the assessed framework, pea monocrops or intensively fertilized oat monocrops can also be considered as alternatives with relatively low impact on the environment. However, an appropriate grain N yield must be reached to balance the environmental impact resulting from the fertilizer inputs. The production and use of fertilizers had the greatest impact on the environment within the impact categories climate change, eutrophication, and ecotoxicity. The results indicated that high fertilizer inputs did not necessarily cause the highest environmental impact. In this respect, the achieved grain N yield level, the choice of allocation approach, the functional unit, and the data expression approach played dominant roles.

Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity ($K_s$) and field water capacity ($FWC$) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated $K_s$ with good results (R${}^2$ = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of $FWC$ for topsoil (0–0.3 m) ranged from R${}^2$ = 0.38 to R${}^2$ = 0.99. The differences within the study sites of the $FWC$ estimations were higher for the subsoil layer (0.3–0.6 m). R${}^2$ values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture.

The management of landscapes and agricultural activities significantly impacts phosphorus (P) and nitrogen (N) losses, directly influencing eutrophication risk. This study quantifies the eutrophication potential of different land covers through in-situ measurements and analysis of runoff and inorganic substances. The research was conducted in two sub-catchments in the Bedřichovský stream basin, Novohradské hory, Czech Republic: a forest-dominated upper sub-catchment (UFS) and an agricultural lower sub-catchment (LAS). Water flows and surface water samples were measured over a hydrological year (November 2017 to October 2018) to determine runoff and concentrations of nitrate (N-NO₃⁻) and phosphate (P-PO₄³⁻). The ReCiPe 2016 method, as a tool for LCIA, was used to quantify the eutrophication potential, converting N and P concentrations into nitrogen equivalents (N eq ha⁻¹ sub-catchment) for marine eutrophication and phosphorus equivalents (P eq ha⁻¹ sub-catchment) for freshwater eutrophication. The potential loss of species (species·yr ha⁻¹ sub-catchment) was assessed as follows. Results indicate UFS has about 60% lower freshwater and 80% lower marine eutrophication potential compared to LAS, along with about 60% lower potential for biodiversity loss. This highlights the role of forest and grassland covers in mitigating eutrophication and protecting water sources. These findings can guide landscape management practices to reduce eutrophication potential, enhancing environmental quality and biodiversity conservation.