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To investigate the different responses of crops to drought stress under field conditions of Central European Climate for selected crop rotations, a field experiment was conducted at a test site in the Czech Republic from 2014 to 2021. Depending on the crop, rainout shelters were placed in late spring and early summer to study the effects of drought in the final stages of crop development. Due to these rainout shelters and the associated lower water availability for the crops during the summer, a reduction in leaf area index, biomass and yield was observed. For example, a yield decrease of more than 30% was observed for spring barley, winter rape and winter wheat compared to conditions without rainout shelters. The reduction was 25% and 18% for winter rye and silage maize, respectively, under rainout shelters. Soil moisture played a significant role in yield, where a predictive model based on monthly soil moisture explained up to 79% (winter rape) of the yield variance.

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km2) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018.

This work presents results from a field experiment which was focused on the impact of the drought period on microbial activities in rhizosphere and non-rhizosphere soil. To demonstrate the effect of drought, the pot experiment lasted from 2012 to 2015. Fifteen lysimeters (plastic containers) were prepared in our area of interest. These lysimeters were filled with the subsoil and topsoil from this area and divided into two groups. The first group consisted of two variants: V1 (control) and V2 (84 kg N/ha), which were not stressed by drought. The second group consisted of three variants, V3 (control), V4 (84 kg N/ha), and V5 (84 kg N/ha + 1.25 L lignohumate/ha), which were stressed by drought every year of the experiment for 30 days. Changes in the soil moisture content caused by drought significantly affect the growth of Deschampsia caespitosa L., the microbial activity, and the soil’s capacity to retain nutrients. The measured basal respiration and dehydrogenase activity values confirm the significant effect of drought on microbial activity. These values were demonstrably higher in the period before drought simulation by more than 60%. On the other hand, significant differences between microbial activities in the rhizosphere and non-rhizosphere soil were not found. We did not find a clear effect of drought on the formation of soil water repellency.

In this paper, a possible use of white sweet clover (Melilotus albus Med.) for phytoremediation was assessed. The plants were grown on soils with naturally occurring concentrations of potentially toxic elements (PTEs). First, the content of PTEs was determined in plant biomass and in soil samples using: (a) Optical emission spectrometry with inductively coupled plasma to determine Sb, As, Cd, Cu, Ni, Pb, and Se, and (b) thermal decomposition, amalgamation, and atomic absorption spectrometry to determine Hg. The effectiveness of Melilotus albus Med. (M. albus) for phytoremediation was evaluated using the bioconcentration factor (BCF). The phytoextraction potential of M. albus was determined using bioaccumulation factor (BAC) and translocation factor (TF) values. The highest concentration of PTEs in roots was detected for zinc (10.56 mg/kg of dry weight, DW) and copper (5.128 mg/kg of DW). Similarly, the highest concentration in above-ground parts of the plant was detected for zinc and copper (12.638 and 4.0 mg/kg of DW, respectively). Although the values were relatively high, the effectiveness of the absorption of these PTEs by plant biomass from the soil was relatively very low. BAC and BCF were always lower than 1. On the other hand, the results suggested that M. albus effectively transports PTEs (only for Zn, Pb and Hg) from roots to shoots, because TF was always higher than 1. However, the accumulation of PTEs from soils with a natural abundance of PTEs was not excessive in comparison to conventional maize silage. Therefore, there is no potential risk of biomethane production in biogas plants when biomass from M. albus is used.

Controlled tile drainage (CTD) practices are a promising tool for improving water balance, water quality and increasing crop yield by raising shallow groundwater level and capillary rise due to drainage flow retardation. We tested the effect of CTD on growth and grain yield of spring barley, at a study site in central Bohemia using vegetation indices from unmanned aerial vehicle (UAV) imagery and Sentinel-2 satellite imagery. Tile drainage flow was slowed by fixed water level control structures that increased soil moisture in the surrounding area according to the terrain slope. Vegetation indices based on red-edge spectral bands in combination with near-infrared and red bands were selected, of which the Normalized Red Edge-Red Index (NRERI) showed the closest relationships with shoot biomass parameters (dry biomass, nitrogen concentration and uptake, nitrogen nutrition index) from point sampling at the tillering stage. The CTD sites showed significantly more biomass using NRERI compared to free tile drainage (FTD) sites. In contrast, in the period prior to the implementation of CTD practices, Sentinel-2 satellite imagery did not demonstrate higher biomass based on NRERI at CTD sites compared to FTD sites. The grain yields of spring barley as determined from the yield map also increased due to CTD (by 0.3 t/ha, i.e., by 4%). The positive impact of CTD on biomass development and grain yield of spring barley was confirmed by the increase in soil moisture at depths of 20, 40 and 60 cm compared to FTD. The largest increase in soil water content of 3.5 vol% due to CTD occurred at the depth of 40 cm, which also had a higher degree of saturation of available water capacity and the occurrence of crop water stress was delayed by 14 days compared to FTD.

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.

Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.

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).

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

Spatial variability of crop growth and yields is the result of many interacting factors. The contribution of the factors to variable yields is often difficult to separate. This work studied the relationships between the 13C discrimination (Δ13C) of plants and the spatial variability of field soil conditions related to impacts of water shortage on crop yield. The 13C discrimination, the indicator of water shortage in plants, 15N (δ15N) discrimination, and nitrogen (N) content were determined in grains of winter wheat, spring barley, and pea. The traits were observed at several dozens of grid spots in seven fields situated in two regions with different soil and climate conditions between the years 2017 and 2019. The principles of precision agriculture were implemented in some of the studied fields and years by variable rate nitrogen fertilization. The Δ13C significantly correlated with grain yields (correlation coefficient from 0.66 to 0.94), with the exception of data from the wetter year 2019 at the site with higher soil water capacity. The effect of drought was demonstrated by statistically significant relationships between Δ13C in dry years and soil water capacity (r from 0.46 to 0.97). The significant correlations between Δ13C and N content of seeds and soil water capacity agreed with the expected impact of water shortage on plants. The 13C discrimination of crop seeds was confirmed as a reliable indicator of soil spatial variability related to water shortage. Stronger relationships were found in variably fertilized areas.

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.

Yield mapping is a subject of research in (precision) agriculture and one of the primary concerns for farmers as it forms the basis of their income and has implications for subsidies and taxes. The presented approach involves deployment of field harvesters equipped with sensors that provide more detailed and spatially localized values than merely a sum of yields for the whole plot. The measurements from such sensors need to be filtered and subject to further processing, including interpolation, to facilitate follow-up interpretation. This paper aims to identify the relative differences between interpolations from (1) (field) measured data, (2) measured data that were globally filtered, and (3) measured data that were globally and locally filtered. All the measured data were obtained at a fully operational farm and are considered to represent a natural experiment. The revealed spatial patterns and recommendations regarding global and local filtering methods are presented at the end of the paper. Time investments into filtering techniques are also taken into account.

The presented paper deals with the analysis of potential differences between organic waste compost (CBD), vermicompost (CVER) 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 CVER and CBD; one variant with the addition of double dosed CVER (80 Mg/ha), and the remaining two variants were fertilized only with MF (0.22 Mg/ha) and with the combination of CVER (0.20 Mg/ha) and MF (0.11 Mg/ha). Substrate induced respiration (SIR), basal respiration (BS), microbial carbon (Cmic) and enzymatic activities (hydrolysis of fluorescein diacetate—FDA, dehydrogenase activity—DHA, and phosphatase activity—PA) were used to evaluate the effect of CBD, CVER 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 (CVER and CBD). All variants treated with the mineral fertilizer showed the lowest level of enzyme activities; lower by about 30% in comparison with variants where CVER, CBD and the combination of MF and CVER 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 (CVER), this negative effect was eliminated or significantly reduced. Furthermore, both composts (CVER and CBD) 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.

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.