REAL-TIME SOIL WATER MONITORING FOR OPTIMUM WATER MANAGEMENT1

Abstract: Efficient water resource management is one of the most important policy issues facing agriculture in Hawaii in the years ahead. Soil water sensors, multisensor capacitance probes (MCP), have been successfully used for different water management projects. These MCPs monitor water content at multiple depths and at various locations in real-time. The objectives of this study were to determine the effect of water content on field soil bulk density of Wahiawa silty clay tropical soil; measure field saturated hydraulic conductivity of this tropical soil: calibrate MCP system for this soil: and monitor and evaluate real-time soil water content variations under a tomato crop using the calibrated MCP system. Sensor calibration was conducted under laboratory conditions. Soil bulk density at different water contents and saturated hydraulic conductivity were measured on the field. Bulk density increased with increasing water content: there was a 30 percent bulk density increase as a result of 0.25 cm³ cm^-3 water content variation. Compared with the manufacturer's calibration, site specific laboratory calibration of MCP gave a more accurate determination of soil water. Field determined saturated hydraulic conductivity was higher than laboratory determined values reported in the literature for the same soil type. Real-time soil water content monitoring within the root zone showed substantial variations due to water input (irrigation and rainfall) and water output (evapotranspiration and deep percolations). However, water content variations were much further reduced in the soil layer below the root zone.     

Associations of variable coloration with niche breadth and conservation status among Australian reptiles

We evaluate predictions concerning the evolutionary and ecological consequences of color polymorphisms. Previous endeavors have aimed at identifying conditions that promote the evolution and maintenance within populations of alternative variants. But the polymorphic condition may also influence important population processes. We consider the prediction that populations that consist of alternative "ecomorphs" with coadapted gene complexes will utilize more diverse resources and display higher rates of colonization success, population persistence, and range expansions, while being less vulnerable to range contractions and extinctions, compared with monomorphic populations. We perform pairwise comparative analyses based on information for 323 species of Australian lizards and snakes. We find that species with variable color patterns have larger ranges, utilize a greater diversity of habitat types, and are underrepresented among species currently listed as threatened. These results are consistent with the proposition that the co-occurrence of multiple color variants may promote the ecological success of populations and species, but there are also alternative interpretations.     

Rove beetles (Coleoptera: Staphylinidae) collected during the long term ecological research in a Hungarian oak forest

Along term ecological research was carried out in a Hungarian oak forest, in "Bükk" National Park starting with 1972. During the faunistical studies 3,602 insect species and more than 200,000 individuals were collected. The dominant orders were Coleoptera (1,051 species), Lepidoptera (803 species), Hymenoptera (470 species) and Diptera (400 species). The relative species abundance (RSA) for all insects collected in all years of sampling period suggests a rather J shape curve than a not clear scaling property. This means that we were able to identify almost three quarters of the insect species from one ha European oak forest during the survey (from 1987 to 2003), and two third of the staphylinides expected. Considering the staphylinid fauna a total number of 160 species and 4,022 individuals were collected. The most widely occurring species in dominance order were: Ocypus biharicus, Pseudocypus mus, Atheta gagatina, Philonthus quisquiliarius, Oxypoda acuminate, Platydracus chalcocephalus, Atheta crassicomis, Latrimaeum atrocephalum, Haploglossa puncticollis, Philonthus succicola and Anotylus mutator. The pooled value of alpha diversity was 1.51. The Shannon-Weiner Index (H') was relatively high (3.29) in comparison with other studies.     

The forecasting of municipal waste generation using artificial neural networks and sustainability indicators

The feasibility of modeling municipal waste generation (MWG) for countries at different levels of development using artificial neural networks (ANN) and selected generic indicators of sustainability was investigated. The main goals of this research were to develop ANN-based models for predicting MWG, to overcome the problem of incomplete MWG data, which is notable in developing countries, and to provide a new method for the planning of municipal solid waste management systems as well as for the simulation of various other scenarios. Data from 26 European countries was used in this study as training, test and validation datasets for the developing of ANN models. Since this kind of modeling is particularly important for developing countries where MWG data is missing or incomplete, emphasis was placed on modeling of MWG for Bulgaria and Serbia. Based on a comparison of actual MWG data with predictions given by the model, we show that ANNs can be applied successfully to modeling and forecasting MWG on a national scale. Moreover, the scope for possible application of the model is broad, since it uses generic indicators of sustainability such as gross domestic product, domestic material consumption and resource productivity, and performs well for countries with highly diversified levels of economic development, industrial structure, productivity and output.     

Eco-efficient recycling of drinking water treatment sludge and glass waste: development of ceramic bricks

Journal of Material Cycles and Waste Management - Current management for drinking water treatment sludge and glass waste is insufficient, and a new method is needed. We sought to develop a new...     

An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level

Journal of Material Cycles and Waste Management - This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual...     

Improved soil carbonate determination by FT-IR and X-ray analysis

In forest soils on calcareous parent material, carbonate is a key component that influences both chemical and physical soil properties and thus fertility and productivity. At low organic carbon contents, it is difficult to distinguish between organic and inorganic carbon, e.g. carbonates, in soils. The common gas-volumetric method to determine carbonate has a number of disadvantages. We hypothesize that a combination of two spectroscopic methods, which account for different forms of carbonate, can be used to model soil carbonate in our region. Fourier transform mid-infrared spectroscopy was combined with X-ray diffraction to develop a model based on partial least squares regression. Results of the gas-volumetric Scheibler method were corrected for the calcite/dolomite ratio. The best model performance was achieved when we combined the two analytical methods using four principal components. The root mean squared error of prediction decreased from 13.07 to 11.57, while full cross-validation explained 94.5 % of the variance of the carbonate content. This is the first time that a combination of the proposed methods has been used to predict carbonate in forest soils, offering a simple method to precisely estimate soil carbonate contents while increasing accuracy in comparison with spectroscopic approaches proposed earlier. This approach has the potential to complement or substitute gas-volumetric methods, specifically in study areas with low soil heterogeneity and similar parent material or in long-term monitoring by consecutive sampling.     

A practical approach to predict the duration of the growing season for European lakes

This work presents the first practically useful models to estimate the duration of the growing season (GS in days) for European lakes. GS is a fundamental parameter in limnology, where it is used, e.g., to calculate lake characteristic annual primary production (in g ww/m²·yr) from measurement data in g C/l·day. We have presented two simple empirical regression equations where GS is estimated from latitude. We have also introduced a more comprehensive model. It is derived from a previously presented well calibrated and validated model predicting lake epilimnetic temperatures from readily available data on latitude, altitude, continentality (distance from the Sea) and lake volume. Operationally, the duration of the growing season (GS in days) is defined from the predicted number of days when epilimentic water temperatures are higher than 9°C. The two modelling approaches are based on different presuppositions and they have been derived and tested from different data-sets. A model comparison has shown that the overall correspondence in predicted GS-values between the methods is very good. Evidently, the more comprehensive model can provide more relevant GS-predictions for many lakes since it also accounts for differences among lakes in altitude, continentality and volume.