Timely and reliable prediction of grain yield and quality of spring barley represents a key prerequisite for effective crop management. Within this study we evaluated the relationships between yield components, grain quality, biomass production and the number of tillers in different growth stages. For this purpose, in three years (2011)(2012)(2013) multifactorial field trials focused on the combined effects of cultivar, sowing density and nitrogen nutrition were conducted. Based on ANOVA it was found that the formation of grain yield was affected by individual factors in the following order of importance: year, nitrogen, cultivar and sowing rate. The final grain yield significantly correlated both with the number of tillers and dry weight of above-ground biomass per unit area. The best estimation of yield provided both parameters at early growth stage (R = 0.83** and 0.81** for number of tillers and the above-ground biomass at BBCH 25). The grain protein content was inversely related to early growth parameters (R = -0.64** and -0.41** for number of tillers and above-ground biomass at BBCH 25). Based on the comparison of relationships between the years, it can be concluded that the early growth of barley and tiller differentiation is a key parameter for the formation of yield and grain quality.

Our study offers:A review of basic indicators important for sustainability assessment of arable farming (balance of nutrients, organic matter and energy, pesticide use, biodiversity and soil protection); A review and way of expression of the most frequent indicators for assessment of the above characters and impact of farming on the environment including the methods for a complex assessment of agricultural enterprises in which individual indicators have been used;A knowledge and practical experience of using of the indicators for assessment in agricultural enterprises.

The main objective of this study was to evaluate the spectral reflectance in the vertical profile of spring barley canopy at the booting growth stage and to determine how the reflectance gradient changes in relation to crop density and nitrogen (N) nutrition. Vertical gradients of spectral reflectance were studied in field trials with three sowing densities (2, 4 and 6 million of germinating seeds/ha) and two levels of N nutrition (0 and 90 kg/ha). It was found that differences in vegetation indices caused by N nutrition are most pronounced in the second and third leaf from the top, and these increase with increasing sowing density. The vertical gradient of reflectance, specifically the ratio between the leaves F-3/F-1 for vegetation indices based on red-edge reflectance, represents a reliable indicator of number of ears per area unit (R = –0.87 for Normalised Red Edge-Red Index (NRERI) and –0.93 for Zarco-Teja-da and Miller Simple Ratio Index (ZM)). A close relationship to ear productivity was found almost for all observed vegetation indices and any leaf in vertical profile (R = 0.79–0.97). In contrast, the prediction of protein content in barley grain was the most reliable when the red-edge reflectance indices (ZM and NRERI) particularly from upper three leaves were used (R = 0.81–0.88). The results show that the knowledge of reflectance heterogeneity in the vertical profile of canopy can significantly contribute to the interpretation of the measured data, to the differentiation of the N nutrition effect from the response to canopy density, and finally to a more accurate estimation of yield parameters and protein content in grain.

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ABSTRACT:Mapping of the with-in field variability of crop vigor has a long tradition with a success rate ranging from medium to high depending on the local conditions of the study. Information about the development of agronomical relevant crop parameters, such as aboveground biomass and crop nutritional status, provides high reliability for yield estimation and recommendation for variable rate application of fertilizers. The aim of this study was to utilize unmanned and satellite multispectral imaging for estimation of basic crop parameters during the growing season. The experimental part of work was carried out in 2014 at the winter wheat field with an area of 69 ha located in the South Moravia region of the Czech Republic. An UAV imaging was done in April 2014 using Sensefly eBee, which was equipped by visible and near infrared (red edge) multispectral cameras. For ground truth calibration the spectral signatures were measured on 20 sites using portable spectroradiometer ASD Handheld 2 and simultaneously plant samples were taken at BBCH 32 (April 2014) and BBCH 59 (Mai 2014) for estimation of above-ground biomass and nitrogen content. The UAV survey was later extended by selected cloud-free Landsat 8 OLI satellite imagery, downloaded from USGS web application Earth Explorer. After standard pre-processing procedures, a set of vegetation indices was calculated from remotely and ground sensed data. As the next step, a correlation analysis was computed among crop vigor parameters and vegetation indices. Both, amount of aboveground biomass and nitrogen content were highly correlated (r > 0.85) with ground spectrometric measurement by ASD Handheld 2 in BBCH 32, especially for narrow band vegetation indices (e.g. Red Edge Inflection Point). UAV and Landsat broadband vegetation indices varied in range of r = 0.5 -0.7, highest values of the correlation coefficients were obtained for crop biomass by using GNDVI. In all cases results from BBCH 59 vegetation stage showed lower relationship to vegetation indices. Total amount of aboveground biomass was identified as the most important factor influencing the values of vegetation indices. Based on the results can be assumed that UAV and satellite monitoring provide reliable information about crop parameters for site specific crop management. The main difference of their utilization is coming from their specification and technical limits. Satellite survey can be used for periodic monitoring of crops as the indicator of their spatial heterogeneity within fields, but with low resolution (30 m per pixel for OLI). On the other hand UAV represents a special campaign aimed on the mapping of high-detailed spatial inputs for site specific crop management and variable rate application of fertilizers.