Carbon and nitrogen mineralization in Vertisol as mediated by type and placement method of residue

Selection of appropriate residue application method is essential for better use of biomass for soil and environmental health improvement. A laboratory incubation experiment was conducted for 75 days to investigate C and N mineralization of residues of soybean (Glycine max L.), chickpea (Cicer arietinum L.), maize (Zea mays L.), and wheat (Triticum aestivum L.) placed on the soil surface and incorporated into the soil. The residue of soybean and chickpea had a greater decomposition rate than that of maize and wheat, despite of their placements. Higher rate of decomposition of the residue of soybean and chickpea was recorded when it was kept on the soil surface while soil incorporation of residue of wheat and maize resulted in faster decomposition. Therefore, these findings could be used as guidelines for management of crop residue application in farmland to improve soil and environmental quality.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

Monitoring of meteorological and hydrological droughts in the Vistula basin (Poland)

The article presents the course of meteorological droughts in Vistula subcatchments in years 1981–2010 and their influence on the occurrence of hydrological droughts. Using the Standardized Precipitation Index (SPI) as an indicator of meteorological drought on the one hand and the Standardized Water-level Index (SWI) and Standardized Runoff Index (SRI) as indicators of hydrological drought on the other, the mutual relationships between precipitation conditions and hydrological conditions were evaluated, as well as the relationships between the two drought types. Studies were conducted for three cumulative periods of these indices, of 12, 24, and 48 months. It was determined that meteorological droughts occurred earliest in the north-western and central part of the basin, and latest in areas lying above 300 m a.s.l. and in the south of Poland. Total duration, depending on the cumulative period, for SPI comprised from 38 to 41% of the analyzed period and for SWI (35–47%) and SRI (24–51%). The strongest relationships were identified in the central part of the Vistula (0.8?<?r?<?0.85), while the weakest relationships were recorded in the foothill region (r?<?0.5). There were also indicated non-climate-related factors influencing those relationships (underground reservoirs, diverse Vistula water resource usage for municipal and industrial intake).     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Water quality index for agricultural systems in Northwest Uruguay

Agricultural systems have experienced rapid expansion and intensification in the last several decades. In Uruguay, since the beginning of 2000, the most common cropping systems have included soybeans. Currently, this crop is expanding towards lowlands traditionally occupied by rice in rotation with pastures. However, the environmental effects of agricultural intensification and diversification are not well known. Thus, some indices have been proposed to quantify the changes in agricultural production systems and assess water quality. The main goal of this study was to develop a water quality index (WQI) to assess the impacts of the diversification of rice production systems in northwest Uruguay. The study was carried out in an agricultural basin where other summer crops have been incorporated in the rice-pasture sequence. Agriculture intensification and crop diversification indices were calculated using information provided by farmers. Water samples were collected downstream of the production area before crop sowing and after crop harvest (2008–2009 to 2010–2011 and 2016–2017 to 2017–2018). Biochemical oxygen demand, nitrates, total phosphorus, fecal coliforms, and total suspended solids were the variables that mainly explained the effects of the agricultural activities on water quality. The proposed water quality index included these unweighted variables, which allowed for the pre-sowing and post-harvest to be differentiated, as well as the degree of diversification. Therefore, the proposed WQI constitutes a tool that can be used to evaluate the water quality in an agricultural basin. Likewise, it can be used to select agricultural sequences that generate the least possible impacts on the associated water resources.     

Variable emissions of microbial volatile organic compounds (MVOCs) from root-associated fungi isolated from Scots pine

Soils emit a large variety of volatile organic compounds. In natural ecosystems, measurements of microbial volatile organic compound (MVOC) exchange rates between soil and atmosphere are difficult due to e.g. the spatial heterogeneity of the belowground organisms, and due to the many potential sources for the same compounds. We measured in laboratory conditions the MVOC emission rates and spectra of eight typical fungi occurring in boreal forest soils. The studied species are decomposers (Gymnopilus penetrans, Ophiostoma abietinum), ectomycorrhizal (Cenococcum geophilum, Piloderma olivaceum, Suillus variegatus, Tomentellopsis submollis) and endophytic fungi (Meliniomyces variabilis, Phialocephala fortinii). The MVOC emissions contained altogether 21 known and 6 unidentified compounds whose emission rates were >0.1 μg g(DW)^?1 h^?1. The most abundant compounds were the short-chain carbonyl compounds (acetone and acetaldehyde). The greatest carbonyl emissions were measured from P. olivaceum (1.9 mg acetone g(DW)^?1 h^?1) and P. fortinii (0.114 mg acetaldehyde g(DW)^?1 h^?1). Terpenoid emissions (isoprene, mono- and sesquiterpenes) were detected from some fungal cultures, but in relatively small amounts. We conclude that soil micro-organisms can potentially be responsible for significant emissions of volatiles, especially short-chain oxygenated compounds, to the below-canopy atmosphere.     

Influence of roads and inhabited areas on metal concentrations in terrestrial mosses

The guidelines regarding sampling terrestrial mosses for biomonitoring the atmospheric contamination in a region specifically recommend that sampling close to focal points of contamination (i.e. roads, populated areas, etc.) should be avoided for satisfactory characterization of general patterns of contamination. However, these recommendations cannot always be followed in many parts of the world with dense, highly dispersed populations. The aim of the present study was to determine how the distances between sampling sites (SS) and contamination foci such as roads, isolated houses and urban nuclei affect regular, large-scale sampling networks. Metal concentrations obtained in biennial sampling surveys carried out between 2000 and 2006 in Galicia (NW Spain) were used, and the relative proportions of the concentrations corresponding to small-scale and large-scale processes were calculated. The possible relationship between the mean concentrations corresponding to small-scale processes in the different sampling surveys and the distances from SS to roads, buildings and urban nuclei was established by a GIS. Outliers in the data were identified at many of the SS and frequency statistics revealed the absence of any relationship between the distance to these foci and the concentrations of metals in the moss. The method also revealed many instances of the influence of one or more small-scale contamination foci such as airports and railways (often not considered in the sampling recommendations) on SS. Relocation of such SS would improve the representativeness of the sampling grid.