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Soil Fauna Diversity Relationship with NO3 Content in Grass Filter Strips within Intensive Agriculture Land

100%

Hlava J.

tom Vol. 63, nr 2

273--279

The biodiversity is a core value in all ecosystems. The nitrogen (N) addition in the form of N fertilizers has effect to a wide variety of fauna living in soils. N losses from arable land should tend not only to cause water eutrophication, but also lower soil nutrient stocks and decreasing soil fertility. This study determined the effect of N soil content in conventional tillage agro-ecosystem on the soil fauna diversity in grass stripes under the sloped arable land. My objective here is to observe soil faunal taxa living in grass filter strips. The goal of this work is to describe the relationship of soil fauna to the nitrate (NO3-) content runoff from fields and captured in grass filter strips. The field work was carried out during period from May until June 2013, for soil fauna investigation the extraction in Berlese — Tullgren funnels were used. Nitratenitrogen (NO3-N) was extracted from air-dried sieved soil using a 2 M KCl solution. Altogether 2,020 specimens representing 19 arthropod groups were found in twenty study sites. Most abundant taxa found practically in each of sampling site were Collembola, Acari and the suborder of Oribatid mites. Statistical evaluations revealed that the effect of NO3- concentration in the soil on the average value of the total edaphic individual numbers was statistically significant; the same applies for the abundance of Acari mites. No significant results were find for the Oribatid mites and the Collembola family; however, there was a clear trend of increasing abundance with increasing concentrations of NO3-.

Soil fauna diversity relationship with NO3 content in grass filter strips within intensive agriculture land

100%

Hlava J.

2015

tom 63

nr 2

The biodiversity is a core value in all ecosystems. The nitrogen (N) addition in the form of N fertilizers has effect to a wide variety of fauna living in soils. N losses from arable land should tend not only to cause water eutrophication, but also lower soil nutrient stocks and decreasing soil fertility. This study determined the effect of N soil content in conventional tillage agro-ecosystem on the soil fauna diversity in grass stripes under the sloped arable land. My objective here is to observe soil faunal taxa living in grass filter strips. The goal of this work is to describe the relationship of soil fauna to the nitrate (NO₃⁻) content runoff from fields and captured in grass filter strips. The field work was carried out during period from May until June 2013, for soil fauna investigation the extraction in Berlese — Tullgren funnels were used. Nitratenitrogen (NO₃⁻N) was extracted from air-dried sieved soil using a 2 M KCl solution. Altogether 2,020 specimens representing 19 arthropod groups were found in twenty study sites. Most abundant taxa found practically in each of sampling site were Collembola, Acari and the suborder of Oribatid mites. Statistical evaluations revealed that the effect of NO₃⁻ concentration in the soil on the average value of the total edaphic individual numbers was statistically significant; the same applies for the abundance of Acari mites. No significant results were find for the Oribatid mites and the Collembola family; however, there was a clear trend of increasing abundance with increasing concentrations of NO₃⁻.

Neural Network Evaluation of Model-Based Residuals in Fault Detection of Time Delay Systems

51%

Zitek P. , Mankova R. , Hlava J.

tom Vol. 9, no 3

599-617

Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose. Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs). This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.