Soil organic carbon (SOC) and mineral nitrogen (N_min), especially nitrates (NO₃⁻) in agroecosystems have attracted much attention over the past few decades due to their crucial roles in soil fertility, crop productivity, environmental quality, and/or climate change mitigation and adaptation. The aim of the study was to evaluate the contents of organic carbon, ammonium, and nitrate in soils under differentiated pH, texture, and fertilization rates. A large-scale environmental study was conducted in Polish arable lands. The spatial distribution of the sampling points reflected agricultural production conditions, variability of soil properties, and representativeness of textures that are characteristic of Poland. Our results indicated that SOC content was significantly affected by the soil pH and texture as well as mineral and organic fertilization. The same factors, except organic amendments, significantly supported mineral nitrogen concentration in the present study. The most important factors controlling SOC in the study were ranked as follows: soil pH > pre-crop N fertilization > crop N fertilization > N applied with manure > soil texture. In the case of N-NH₄ and N-NO₃, mineral fertilization was the most critical variable. The carbon and nitrogen governance in agroecosystems should consider the ranks of factors controlling their contents.

The aim of the study was to analyse spatial variability of selected parameters of subsurface waters in the area of approximately 10 ha, located in the valley of the Ciemięga River in the village of Snopków, near Lublin, Poland. For the purpose of this study, nine sections were delimited, each with four points of collecting groundwater. In the groundwater samples, there were measured \({\text{NH}}_{4}^{ + }\), \({\text{NO}}_{3}^{-} ,\) and \({\text{NO}}_{2}^{-}\). Due to the small number of samples, the analysis was limited to deterministic interpolation methods. The following methods were compared using leave-one-out cross-validation procedure: triangulation, inverse distance weighting, radial base function, and modified Shepard’s method. The methods which proved to be optimal were used to create spatial variability maps of the analysed parameters. Spatial interpolation and visualization of the results were performed in Surfer ver.16, and other calculations were conducted using R software.

The selection of appropriate locations for the reintroduction of endangered plant species is an important process, because it usually influences the success of the conservation. The aim of this study was to select the optimal substitute habitats for Aldrovanda vesiculosa, taking into account the influence of physical–chemical factors (light intensity, temperature, pH, concentration of dissolved forms of nitrogen and cyanobacterial toxin microcystin-LR) on the efficiency of plant growth. Water analysis and field observations of the habitats of six lakes in Eastern Poland typified as potential substitute habitats for aldrovanda were carried out. The results of the experiments showed that both the concentration and the form in which nitrogen compounds are present in the environment were the factors limiting the growth rate and condition of plants. The second factor that caused the inhibition of aldrovanda growth was microcystin-LR. It was found that the habitat conditions in Lake Brzeziczno were within the ecological tolerance of the species. Particularly important was the low content of mineral compounds and the available forms of nitrogen and phosphorus in the water. Therefore, the probability of development of toxic cyanobacteria, the metabolites of which may affect the growth of A. vesiculosa, is also minimal.

The aim of this study was to compare different methods for analyzing data from a field breeding experiment with check plots where soil variability was controlled with the use of statistical and geostatistical methods. The analyzed data were obtained from field experiments investigating the yield of camelina Camelina sativa L. Crantz and crambe Crambe hispanica ssp. abyssinica Hochst. ex R.E. Fr. Prina seeds in the temperate climate of north-eastern Poland. Analysis of variance models with a completely randomized design, a randomized block design, and analysis of covariance were compared. In the vast majority of cases, the values of statistical information criteria demonstrated that the best model was the analysis of covariance where the theoretical seed yield from the check plot, interpolated with a geostatistical method, was the covariate. The results of this study can be useful for testing new cultivars in large-scale commercial farms with variable soil conditions to identify the optimal genotype for the local environmental and climatic conditions.

Missing data is a pervasive challenge in environmental monitoring, severely hindering reliable analysis and effective water resource management. This study addresses this issue in the context of the EU Nitrates Directive (91/676/EEC), focusing on the proportion of groundwater monitoring sites expected to exceed the regulatory limit of 50 mg NO₃⁻/L. Overall, 57% of observations were missing, with 99% of monitoring stations experiencing data gaps over the 2000–2024 study period. Six reconstruction techniques were evaluated: k-nearest neighbours, multiple imputation by chained equations, missForest, natural neighbour, inverse distance weighting, and indicator kriging. This work presents a novel approach to comparing various imputation techniques for reconstructing threshold overruns and modelling trends in exceedance rates. Moving beyond conventional error metrics, the evaluation is grounded in distributional similarity and monotonic dependence using Hellinger and Kullback-Leibler distances and generalised Lorenz curves. Based on the comparative analysis, indicator kriging was identified as the most robust approach and was subsequently adopted to parameterise the log-linear trend in the proportion of monitoring stations expected to exceed the concentration threshold. This study provides a methodological framework for addressing extreme missingness in environmental monitoring networks, offering actionable insights for EU Member States reporting under the Nitrates Directive.