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The presented research deals with the effect of plot size changes on the crop yield. Three plots were chosen in a company engaged in conventional agriculture, on which yields were monitored from 2019 using yield maps. In 2020, the plots (initial size > 30 ha) were divided into different parts sized < 30 ha. In 2021, these newly arisen parts of the plots were harvested. Changes in the yield of grown crops were analysed using yield maps acquired by the harvesting machines. Relative yields (%) and absolute yields (t/ha) were determined on all experimental land parts arising from the initial plots' division. The values were then compared with yields recorded before the division of individual plots using zonal statistics. Measured relative yield values clearly show (P < 0.05) that the division of plots resulted in the increased heterogeneity of crop yields. On the initial plots as well as on the newly arisen plots, the relative yield was divided into the following categories: < 70, 70-85, 85-95, 95-105, 105-115, 115-130 and > 130%, with the value of 100% representing average yield. The analysis of measured yield data showed that the division of plots into smaller parts resulted in an uneven yield distribution because if a divided plot was heterogeneous in terms of yield levels, a cumulation of "higher yield levels (> 100%)" could have occurred in one specific newly arisen plot at the expense of another one. Moreover, new marginal parts of lands came into being during the division of larger soil complexes, and hence zones with potentially reduced yields.

The presented research deals with the variable application of N fertilizers (VRA NF) in the stand of winter wheat and with possibilities for evaluating the effect of such application on grain yield. The effect of VRA NF was assessed in close cooperation between Spearhead Czech Ltd. and Mendel University in Brno within an operational experiment. In 2018 and 2019, three experimental plots were always chosen on which VRA NF was implemented by conventional technique according to application maps. Each application map included control strips with the uniform NF application. The application maps were prepared based on the spectral analysis of satellite images. The individual plots were divided into three zones: Zone 1 with the lowest yield potential, Zone 2 with the medium yield potential, Zone 3 with the highest yield potential. The highest dose of N was at all times applied in Zone 3, and conversely the lowest dose was applied in Zone 1. In 2018 and 2019, the experimental fields were harvested by harvester New Holland CX 8080 which was equipped with the technology for the monitoring of grain yields. The main goal of data processing was to remove error data at first and then to re-calibrate them using the information about the weight of harvested grain. The expected benefit, i.e. a yield increase by min. 3% was found only in 2018, when the benefit was about 5%. In the following year, the difference between the conventional application of N and VRA technologies was minimal. However, this condition was probably caused by drought which negatively affected all stands. In particular, measured values of grain yield do not indicate the negative effect of VRA NF on grain yield. This research further showed the applicability of yield data acquired by harvest technology; however, the elimination of error data is necessary as well as their re-calibration according to total yield ascertained by weighing the total production of grain from a specific plot.

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 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).