Odvození topografických charakteristik pro účely precizního zemědělství

Quantitative knowledge of the factors and interactions affecting agricultural yield is essential for site-specific yield management. Topography of the terrain certainly remains on these yield affecting factors. For this reason, this paper deals with the prospects of modelling topographic features - digital elevation models and slope models for an experimental plot with an area of 11.5 hectares. The basis for the creation of these models is formed by data from various sources (combine yield monitor, RTK-GPS and data from airborne laser scanning). These data sets have been then modified via ArcGIS software in order to most accurately describe the topography of the analysed landscape. The resulting models of topographical characteristics were compared with crop yields during the observed period of 2004-2012, in order to determine which data source is best for the evaluation of the influence topography holds over yield values. Data from airborne laser scanning turned out to be the most suitable dataset for the tasks, because of their sufficient accuracy and frequency.16117

Yield variability prediction by remote sensing sensors with different spatial resolution

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 – BBCH 59 – average correlation coefficient 0.856, and in 2011 – BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method

Spectral Indices as a Tool for Hop Growth Evaluation

The use of unmanned aerial vehicles (UAV) to monitor crop growth is nowadays a common non-invasive way how to obtain information on the current state of crops. Spectral indices derived from multispectral images obtained in the right growth stage can then serve as a good data source for agro-technical interventions and yield estimation. Hop belongs among the crops where it is possible to scan the individual growth parameters very exactly. In the year 2021, significant precipitation amounts were recorded during the growing season, when it turned out that UAVs are a very powerful tool for determining the quality of production or quantification of vegetation damage compared to the previous year (2020). It was found that the common spectral indices were possible to use for calculation leaf area, structure, vigor and chlorophyll content of hop gardens

Effect of soil tillage technologies on soil properties in long term evaluation

Soil compaction is a major problem of modern agriculture. Soil compaction increases due to growth of the weight of agricultural machines and the number of passes over land. Soil compaction may be significantly reduced by using suitable soil tillage. One indicator of compaction can be cone index. It was established field trial with six variants of tillage. On 3 variants were used ploughing systems and other 3 used reduced systems of tillage. Experiment was conceived as a multi-annual and was run from 2010 to 2014. The soil on the land was shallow sandy loam cambisol. Registration penetrometer was used for measurement. Cone index was measured at the depth of 0.04 m to 0.32. Each plot had size 6 x 50 m. The results were evaluated after 5 years of the experiment. The results showed a difference of the cone index values between variants. The variants with ploughing are apparent initial favourable effect of loosening with a strong transition of not processed layers. There are no visible transitions in variants with reduced tillage. But the values of cone index in the surface layers have higher values than the variants with ploughingVytauto Didžiojo universitetasŽemės ūkio akademij

Monitoring of Khorasan (Triticum turgidum ssp. Turanicum) and Modern Kabot Spring Wheat (Triticum aestivum) Varieties by UAV and Sensor Technologies under Different Soil Tillage

Khorasan wheat (Triticum turgidum ssp. turanicum (Jakubz.)) is an ancient tetraploid spring wheat variety originating from northeast parts of Central Asia. This variety can serve as a full-fledged alternative to modern wheat but has a lower yield than modern varieties. It is commonly known that wheat growth is influenced by soil tillage technology (among other things). However, it is not known how soil tillage technology affects ancient varieties. Therefore, the main objective of this study was to evaluate the influence of different soil tillage technologies on the growth of the ancient Khorasan wheat variety in comparison to the modern Kabot spring wheat (Triticum aestivum) variety. The trial was arranged in six small plots, one half of which was sown by the Khorasan wheat variety and the other half of which was sown by the Kabot wheat variety. Three soil tillage methods were used for each cultivar: conventional tillage (CT) (20–25 cm), minimum tillage (MTC) with a coulter cultivator (15 cm), and minimization tillage (MTD) with a disc cultivator (12 cm). The soil surface of all of the variants were leveled after tillage (harrows & levelling bars). An unmanned aerial vehicle with multispectral and thermal cameras was used to monitor growth during the vegetation season. The flight missions were supplemented by measurements using the GreenSeeker hand-held sensor and plant and soil analysis. The results showed that the Khorasan ancient wheat was better suited the conditions of conventional tillage, with low values of bulk density and highvalues of total soil porosity, which generally increased the nutritional value of the yield in this experimental plot. At the same time, it was found that this ancient wheat does not deplete the soil. The results also showed that the trend of developmental growing curves derived from different sensors was very similar regardless of measurement method. The sensors used in this study can be good indicators of micronutrient content in the plant as well as in the grains. A low-cost RGB camera can provide relevant results, especially in cases where equipment that is more accurate is not available

Long-Term Monitoring of Different Field Traffic Management Practices in Cereals Production with Support of Satellite Images and Yield Data in Context of Climate Change

Cereals in Europe are mainly grown with intensive management. This often leads to the deterioration of the physical properties of the soil, especially increasing bulk density due to heavy machinery traffic, which causes excessive soil compaction. Controlled traffic farming (CTF) technology has the potential to address these issues, as it should be advantageous technology for growing cereals during climate change. The aim of this study was to compare the yield potential of CTF and standardly used random traffic farming (RTF) technology using yield maps obtained from combine harvester and satellite imagery as a remote sensing method. The experiment was performed on a 16-hectare experimental field with a CTF system established in 2009 (with conversion from a conventional (ploughing) to conservation tillage system). Yield was compared in years when small cereals were grown, a total of 7 years within a 13-year period (2009–2021). The results show that CTF technology was advantageous in dry years. Cereals grown in the years 2016, 2017 and 2019 had significantly higher yields under CTF technology. On the contrary, in years with higher precipitation, RTF technology had slightly better results—up to 4%. This confirms higher productivity when using CTF technology in times of climate change