Landfill leachates are potentially harmful to the environment and to human health. The objective of this study was to characterize leachates in order to analyze whether a relationship exists between the stored waste and the composition of leachates, and to detect possible leakages of pollutants into the environment. To achieve these objectives, field data, Global Positioning System data and physico-chemical data were used. Biological tests are becoming increasingly popular in determining leachate toxicity; therefore, two toxicity tests were performed with the seeds of white mustard (Sinapis alba L.) and duckweed (Lemna minor L.). Leachates were sampled from the leachate pond. Groundwater quality was monitored by using drill holes. The research and analysis carried out are important to determine their potential impact on agricultural areas located near the landfill. Demonstrably increased (P < 0.05) concentrations of heavy metals were detected only in the leachate pond which closes the landfill body, where it links up with the landfill insulation layer. Water sampled from drill holes reaching into groundwater was not contaminated. The results showed that the leachates did not leak outside the landfill. Nevertheless, they were found to be phytotoxic. Both toxicity tests showed that the increasing amount of leachates resulted in the increasing growth inhibition of the tested plants. The proper handling of leachates should have been ensured.

Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and usability of these vegetation indices in the identification of anomalies in the condition of the flowering growth were verified based on the regression analysis. Correlation analysis was performed to find the degree of yield dependence on the values of NDVI, BNDVI, and NDYI indices, which revealed a strong, significant linear positive dependence of seed yield on BNDVI (R = 0.98) and NDYI (R = 0.95). The level of correlation between the NDVI index and the seed yield was weaker (R = 0.70) than the others. Regression analysis was performed for a closer determination of the functional dependence of NDVI, BNDVI, and NDYI indices and the yield of seeds. Coefficients of determination in the linear regression model of NDVI, BNDVI, and NDYI indices reached the following values: R² = 0.48 (NDVI), R² = 0.95 (BNDVI), and R² = 0.90 (NDYI). Thus, it was shown that increased density of yellow flowers decreased the relationship between NDVI and crop yield. The NDVI index is not appropriate for assessing growth conditions and prediction of yields at the flowering stage of oil seed rape. High accuracy of yield prediction was achieved with the use of BNDVI and NDYI. The performed analysis of NDVI, BNDVI, and NDYI demonstrated that particularly the BNDVI and NDYI indices can be used to identify problems in the development of oil seed rape growth at the stage of flowering, for their precise localization, and hence to targeted and effective remedial measures in line with the principles of precision agriculture.

The presented paper deals with the analysis of potential differences between organic waste compost (C_BD), vermicompost (C_VER) and mineral fertilizer (MF; 27% of N) applications affecting the quality of arable soil by influencing microbial activity therein. The selected types of compost represent alternatives to conventional organic fertilizers, which are, however, not available to Czech and Slovak farmers in sufficient amounts. Their mutual comparison and the comparison with organic fertilizers aim to provide farmers further information about their influence on arable land and thus to give them the possibility of deciding on the most suitable amendments. To demonstrate the effect of these amendments, six variants were prepared: one without the addition of fertilizers; two variants with the addition of 40 Mg/ha of C_VER and C_BD; one variant with the addition of double dosed C_VER (80 Mg/ha), and the remaining two variants were fertilized only with MF (0.22 Mg/ha) and with the combination of C_VER (0.20 Mg/ha) and MF (0.11 Mg/ha). Substrate induced respiration (SIR), basal respiration (BS), microbial carbon (C_mic) and enzymatic activities (hydrolysis of fluorescein diacetate—FDA, dehydrogenase activity—DHA, and phosphatase activity—PA) were used to evaluate the effect of C_BD, C_VER and MF application on the soil quality. Both organic and mineral amendments affected BS and SIR. The highest BS and SIR rates were found in variants with compost application (C_VER and C_BD). All variants treated with the mineral fertilizer showed the lowest level of enzyme activities; lower by about 30% in comparison with variants where C_VER, C_BD and the combination of MF and C_VER were applied. We found insignificant differences between the individual types of compost. More importantly, we compared the situation at the beginning of the experiment and after its end. It was found that the application of mineral fertilizers automatically led to the deterioration of all enzymatic parameters, on average by more than 25%, as compared with the situation at the beginning of the experiment. However, when the mineral fertilizer dose was supplemented with organic amendments (C_VER), this negative effect was eliminated or significantly reduced. Furthermore, both composts (C_VER and C_BD) positively affected plant biomass production, which reached a level of production enhanced by the MF. Results clearly showed that the application of both compost types could be used to improve soil quality in agriculture.

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

Intensive farming on land represents an increased burden on the environment due to, among other reasons, the usage of agrochemicals. Precision farming can reduce the environmental burden by employing site specific crop management practices which implement advanced geospatial technologies for respecting soil heterogeneity. The objectives of this paper are to present the frontier approaches of geospatial (Big) data processing based on satellite and sensor data which both aim at the prevention and mitigation phases of disaster risk reduction in agriculture. Three techniques are presented in order to demonstrate the possibilities of geospatial (Big) data collection in agriculture: (1) farm machinery telemetry for providing data about machinery operations on fields through the developed MapLogAgri application; (2) agrometeorological observation in the form of a wireless sensor network together with the SensLog solution for storing, analysing, and publishing sensor data; and (3) remote sensing for monitoring field spatial variability and crop status by means of freely-available high resolution satellite imagery. The benefits of re-using the techniques in disaster risk reduction processes are discussed. The conducted tests demonstrated the transferability of agricultural techniques to crisis/emergency management domains.