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16 results on '"Jakšík, Ondřej"'

13. USING VNIR-DRS TO ASSESS SOIL DEGRADATION DUE TO EROSION.

Author

Klement, Aleš, Kodešová, Radka, Fér, Miroslav, and Jakšík, Ondřej

Subjects
SOIL degradation, ELECTROMAGNETIC spectrum, SOIL composition, SOIL physics, DIGITAL soil mapping
Abstract

Large numbers of soil samples must be collected and analyzed when mapping soil degradation due to erosion. The reflective spectroscopy in the visible and near infrared part of electromagnetic spectrum (VNIR DRS spectroscopy) is a low-cost indirect method, which may be used to gain information about soil properties more effectively. The method for assessing soil degradation using soil spectra proposed and tested in this study was based on estimating oxidable organic carbon content, carbonate content, and contents of different forms of iron and manganese (Mehlich III extract, ammonium oxalate extract and dithionite-citrate extract). Study was performed in Brumovice. Tested soil types were Haplic Chernozem, Colluvic Chernozem, Colluvial soils and Regosol. Samples were taken from the topsoil within the regular grid covering the studied area. The soil spectra curves (of air dry soil and sieved using 0.2 mm sieve) were measured in the laboratory using the spectrometer FieldSpec®3 (350 - 2 500 nm). Partial least squares regression (PLSR) was used for modeling of the relationships between spectra and measured soil properties. The results showed the best prediction for carbonate content (R² = 0.87). The worse predictions were obtained for oxidable organic carbon content (R² = 0.78), and contents of Mn in ammonium oxalate extract (R² = 0.78), Mn in dithionite--citrate extract (R² = 0.72), Fe in ammonium oxalate extract (R² = 0.70), Fe in dithionite-itrate extract (R² = 0.57), Mn in Mehlich III extract (R² = 0.31) and content of Fe in Mehlich III extract (R² = 0.20). In general, the results confirmed that the measurement of soil spectral characteristics is a promising technology for digital soil mapping and predicting soil degradation due to erosion processes. [ABSTRACT FROM AUTHOR]

Published
2014

15. Soil sampling with increased accuracy

Author

Mesić, Milan, Vuković, Ivana, Kisić, Ivica, Zgorelec, Željka, Jurišić, Aleksandra, Jakšík, Ondřej, Klement, Aleš, and Borůvka, Luboš

Subjects
soil pH, automatic circular soil probe, precision farming, kriging, spatial variability
Abstract

Conventional soil sampling usually implemented in Croatia considers sample weight of 2 kg per 4-5 ha area, which means that representative sample in relation to soil mass up to 30 cm depth is presented through the ratio 1:10 000 000. New sampling method changes the ratio to 1:625 000, thus increasing amount of sampled soil 16 times with assumption that such sample better describes investigated area. Moreover, new soil sampling probe can be used for precision farming purposes where the central point of the probe ring is positioned with precision of ±1 cm and represented with 4, 8 or 16 samples taken in 50 cm radius from the center. Soil probe prototype was tested on agricultural land of 4 ha area with total number of 200 samples and on the 12 ha area with total of 12 samples. To justify application of new constructed probe, this study gives results of geostatistical analysis of spatial variability in soil pH values up to 30 cm depth. Ordinary kriging was used as interpolation method. Spatial structure of soil parameter was analyzed by calculating semivariograms and approximated by exponential model. Root-mean-square error (RMSE) of prediction was used as measure for the best model evaluation. Interpolation analysis of reduced number of samples per investigated area of 4 ha resulted in deviation in RMSE of 13, 2% when 50% less samples were randomly introduced, and 41% with 60 samples, compared to total of 200 samples. Using new soil sampling method for soil survey and applied geostatistical tools provides a solution for quantifying spatial variability of soil properties, possibilities for commercial activities and a way to introduce variable rate technology in agricultural input application which can optimize farm profitability through improving yield, reducing input costs and minimizing input losses to the environment.

Published
2011

16. Absorption Features in Soil Spectra Assessment.

Author

Vašát R, Kodešová R, Borůvka L, Jakšík O, Klement A, and Drábek O

Subjects
Absorption, Physicochemical, Calibration, Least-Squares Analysis, Linear Models, Support Vector Machine, Soil chemistry, Spectroscopy, Near-Infrared methods
Abstract

From a wide range of techniques appropriate to relate spectra measurements with soil properties, partial least squares (PLS) regression and support vector machines (SVM) are most commonly used. This is due to their predictive power and the availability of software tools. Both represent exclusively statistically based approaches and, as such, benefit from multiple responses of soil material in the spectrum. However, physical-based approaches that focus only on a single spectral feature, such as simple linear regression using selected continuum-removed spectra values as a predictor variable, often provide accurate estimates. Furthermore, if this approach extends to multiple cases by taking into account three basic absorption feature parameters (area, width, and depth) of all occurring features as predictors and subjecting them to best subset selection, one can achieve even higher prediction accuracy compared with PLS regression. Here, we attempt to further extend this approach by adding two additional absorption feature parameters (left and right side area), as they can be important diagnostic markers, too. As a result, we achieved higher prediction accuracy compared with PLS regression and SVM for exchangeable soil pH, slightly higher or comparable for dithionite-citrate and ammonium oxalate extractable Fe and Mn forms, but slightly worse for oxidizable carbon content. Therefore, we suggest incorporating the multiple linear regression approach based on absorption feature parameters into existing working practices.

Published
2015
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