Quantitative knowledge of the factors and interactions affecting yield is essential for site-specific crop management. One of the factors that frequently affects yield is topography. The aims of this study were to compare elevation data obtained from a combine harvester yield monitor and a hand RTK-GPS, and to evaluate the relationships between the spatial variation of cereal yield, selected crop nutrient concentration and topographic attributes derived from the two sources of elevation data. Simple models of elevation, slope and flow accumulation were created from the data of an experimental field in the Czech Republic, and the relations between yield and soil nitrogen and organic carbon contents and topography were determined over a four-year period. The models of elevation, slope and flow accumulation were compared with the yield, and soil nitrogen and organic carbon contents during the growing seasons of 2004, 2005, 2006 and 2007 in relation to total precipitation and temperature. The relationship between yield and topographic attributes was evaluated with the help of geostatistical methods. The results of correlation analysis among the variables were evaluated statistically by forward stepwise linear regression. No significant differences between elevation data from the combine harvester yield monitor and RTK-GPS were found. There was a significant relation between yield and crop nutrient concentration with topography. The correlation coefficients between flow accumulation and yield were weak for the wetter years and strong for the drier years.
The main aim of this study was to determine the intensity of hydrophobic/hydrophilic components of the soil's organic matter as well as its hydrophobicity. Non-destructive Fourier Transform Infrared (FTIR) spectroscopy was used for the diagnosis and characterization of the basic classes of the chemical groups (hydrophilic and hydrophobic components) from which the organic matter in the soils is formed. Soil samples (depth 0-30 cm) were taken from the topsoil of the 70 sampling sites from the experimental field at Prague-Ruzyne (Czech Republic) during [2007][2008][2009], where a conventional soil tillage technology was used. It was found that the variability of the intensity of the hydrophobic components is greater (27.6%) than that of the intensity of the hydrophilic components (6.2%), which correlated significantly with the C org (r = 0.58; P < 0.05) and N t (r = 0.65; P < 0.05) in the soil. It was proven that the soil samples with a higher proportion of coarse grains are more hydrophobic than those with higher proportions of clay. Data about soil hydrophobicity can help to evaluate the soil quality parameters as well as the soil fertility.
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.
The main aim of this study was to determine the dependence of yield and selected soil properties on topography of the experimental field by using topographical data (elevation, slope and flow accumulation). The topography and yield data were obtained from a yield monitor for combine harvester, and soil properties data were taken from sampling points of our experimental field. Initially, the topographical parameters of elevation and slope were estimated and then the Digital Elevation Model (DEM) grid was created. On the basis of field slope the flow direction model and the flow accumulation model were created. The flow accumulation model, elevation and slope were then compared with the yield and content of nitrogen and organic carbon in soil in the years 2004, 2005 and 2006 in relation to the sum of precipitation and temperatures in crop growing seasons of these years. The correlation analysis of all previously mentioned elements was calculated and statistical evaluation proved a significant dependence of yield and soil nutrition content on flow accumulation. For the wettest evaluated year the correlation coefficient 0.25 was calculated, for the driest year it was 0.62.
Yields of winter wheat, winter rape and oats were evaluated in the field; the field was divided into the site-specific zones and treated with variable doses of nitrogen fertilizer in years [2004][2005][2006]. Measurements of the yields were carried out with a yield monitor placed in a combine harvester. The measured data were processed into the yield maps by means of ArcGIS 9.2 software. Variable application of fertilizer should balance yield potential of the field. Generally, total yield variability on the field after the application of various doses of experimental fertilizer was similar in the years 2004 (11.3%), 2005 (14.7%) and 2006 (11.7%) in comparison with the year 2003 (25.02%). Variable application of nitrogen in the site-specific zones, created on the basis of the yield levels, decreased the yield variability in comparison with the uniform dose. Different doses of nitrogen fertilizer also enabled to increase utilization of production potential of the experimental field.
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