Activity of Exoenzymes in Treated Wastewater Irrigated Soils

The reuse of reclaimed wastewater for irrigation of agricultural fields greatly influences the activity of soil microorganisms through the input of organic compounds. Due to the production of exoenzymes by microorganisms for the decomposition of substrates it can be assumed that the irrigation with treated wastewater (TWW) has a strong influence on the soil enzyme pool. In this study the activity of ten exoenzymes, which catalyses processes in C, N and P nutrient cycles, were determined in 3 different soils in 0-10, 10-20, 20-30, 30-50, 50-70 and 70-100 cm soil depth. The soils were used for agriculture and irrigated with reclaimed wastewater reused after a secondary treatment step. Additionally a control after freshwater irrigation was studied. Due to the influence of TWW on the soil biology of these soils, also clear effects on soil exoenzymes in freshwater and TWW irrigated soils could be seen. According to Sinsabaugh et al. (2008) we calculated indices which describe the enzymatic resources for acquisition of organic P and organic N relative to C and therefore give insides into the functional convergence of extracellular enzyme activities in soils and the relative nutrient demand. The distribution pattern of these functional enzyme activities varied between freshwater and TWW irrigated soils and shows therefore a strong influence of the TWW irrigation on the activity of exoenzymes. (Sinsabaugh et al. (2008): Stoichiometry of soil enzyme activity at global scale. Ecology Letters 11 (11), 1252-1264.

Vergleich dreier Ringversuche zur radioimmunologischen Thyrotropin-Bestimmung nach dem "Münchner Modell"

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Challenges in using mid-infrared spectroscopy for the determination of soil physical, chemical, and biochemical properties on undisturbed soil samples

Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in the mid-infrared range (MIR) has become an established analytical tool for quantitative and qualitative analysis of soil samples. The heterogeneity of soil requires sample preparation procedures to optimize the reproducibility and accuracy of the spectroscopic measurement. These procedures have not been standardized. Generally, soil is dried and ground before measurement to avoid reflections of surface water films and minimize the intra- and inter-particle variability, respectively. Additionally, the sample surface is levelled to a plain surface for an ideal reflection. These sample preparation techniques are limited to disturbed samples only. Thus, a potential DRIFT mapping of undisturbed soil samples requires an adjusted calibration to allow for an accurate prediction of soil properties. In this study, we developed a method for calibrating the prediction of DRIFT spectra collected from undisturbed soil samples. In a first step, differences of spectral information measured from undisturbed and ground soil samples have been evaluated. Therefore, we record the DRIFT spectra of 120 German and 120 West-African chemically well characterized soils. DRIFT spectra of both, ground and sieved only soil samples are recorded and both calibrated against different physio-chemical soil properties, such as texture, CEC, organic carbon, pH, or iron oxides. In preliminary experiments, we found that spectra of sieved and ground samples significantly differed in specific spectral regions representing clay minerals, as well as organic matter. It can be assumed that the prediction of surface related soil parameters could be superior using sieved soil spectra, as grounding alters the surface structure of the soil. In a further step, microtopgraphy effects on spectra quality from disturbed and undisturbed soil samples have been evaluated. Therefore, spectral information has been taken from two dimensional disturbed and undisturbed soil samples at a high spatial resolution. The spectra quality was significantly higher in the disturbed soils since microtopography was absent in these samples. Thus, a digital elevation model (DEM) will be constructed using close-range digital photogrammetry to correct these topography effects. With this new method, there is a potential of imaging soil parameters on a microscale that can help considerably in locating and understanding soil processes on a small scale

Classification of West African (peri)-urban and rural agricultural soils based on mid-infrared diffuse reflectance spectroscopy (DRIFT) and multivariate statistics and data mining

Urban and peri-urban agriculture has long been part of West African society. In Sub-Sahara Africa with its low soil fertility and high vulnerability for droughts, food security not only depends on rural food production but also on this (peri)-urban agriculture. The interdisciplinary GlobE – UrbanFoodPlus project aims to enhance the resource use efficiency of such agricultural sites in West African cities to improve the economic situation and food security for the people in this area. To assess soil productivity inside this project, several randomized surveys were conducted to characterize urban and peri-urban agriculture in Tamale (Ghana), Ouagadougou (Burkina Faso), and in rural Northern Ghana. All sample sites were situated in the West African Savannah zone. These surveys systematically described the status of urban agriculture by collecting soil samples, as well as additional socioeconomic and land use data. For our study, the spectra of more than 1000 soil samples were analyzed using diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in the mid-infrared range (MIR 4000–400 cm–1) at a resolution of 4 cm-1. Based on the large data set of spectra, we exploratory analyzed the data for clustering and grouping based on latest improvements in multivariate statistics and data mining. Statistically, we were able to find classes inside the spectral data. This grouping could be explained by sample location using the Random Forest algorithm at a very low error of about 5%. By mathematical pretreatment of the data, the error could further be reduced to <2%. Due to the spectral difference by geography location, potential caused by differences in climate, we continued to determine groups within one location using cluster algorithms. With this technique, we could determine further subgroups in the data. We then used topographic, land use, and socioeconomic data to explain the statistically found clustering in the MIR spectra. We herewith present a novel approach by combing multivariate MIR spectra analysis with socioeconomic data. Although we showed that soil spectra seemed to be largely affected by topography and climate, there were also differences in the spectra that could be explained by differences in land use practices

A modular analyzer system for double antibody radioimmunoassays

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