Microbial and biochemical soil quality indicators and their potential for differentiating areas under contrasting agricultural management regimes

The aim of this study was to examine interrelationships between functional biochemical and microbial indicators of soil quality, and their suitability to differentiate areas under contrasting agricultural management regimes. The study included five 0.8 ha areas on a sandy-loam soil which had received contrasting fertility and cropping regimes over a 5 year period. These were organically managed vegetable, vegetable -cereal and arable rotations, an organically managed grass clover ley, and a conventional cereal rotation. The organic areas had been converted from conventional cereal production 5 years prior to the start of the study. All of the biochemical analyses, including light fraction organic matter (LFOM) C and N, labile organic N (LON), dissolved organic N and water-soluble carbohydrates showed significant differences between the areas, although the nature of the relationships between the areas varied between the different parameters, and were not related to differences in total soil organic matter content. The clearest differences were seen in LFOM C and N and LON, which were higher in the organic arable area relative to the other areas. In the case of the biological parameters, there were differences between the areas for biomass-N, ATP, chitin content, and the ratios of ATP: biomass and basal respiration: biomass. For these parameters, the precise relationships between the areas varied. However, relative to the conventionally managed area, areas under organic management generally had lower biomass-N and higher ATP contents. Arbuscular mycorrhizal fungus colonization potential was extremely low in the conventional area relative to the organic areas. Further, metabolic diversity and microbial community level physiological profiles, determined by analysis of microbial community metabolism using Biolog GN plates and the activities of eight key nutrient cycling enzymes, grouped the organic areas together, but separated them from the conventional area. We conclude that microbial parameters are more effective and consistent indicators of management induced changes to soil quality than biochemical parameters, and that a variety of biochemical and microbial analyses should be used when considering the impact of management on soil quality. (C) 2004 Elsevier Ltd. All rights reserved.

Exploring the enzymatic landscape: distribution and kinetics of hydrolytic enzymes in soil particle-size fractions

The location of extracellular enzymes within the soil architecture and their association with the various soil components affects their catalytic potential. A soil fractionation study was carried out to investigate: (a) the distribution of a range of hydrolytic enzymes involved in C, N and P transformations, (b) the effect of the location on their respective kinetics, (c) the effect of long-term N fertilizer management on enzyme distribution and kinetic parameters. Soil (silty clay loam) from grassland which had received 0 or 200 kg N ha(-1) yr(-1) was fractionated, and four particle-size fractions (> 200, 200-63, 63-2 and 0. 1-2 mum) were obtained by a combination of wet-sieving and centrifugation, after low-energy ultrasonication. All fractions were assayed for four carbohydrases (beta-cellobiohydrolase, N-acetyl-beta-glucosammidase, beta-glucosidase and beta-xylosidase), acid phosphatase and leucine-aminopeptidase using a microplate fluorimetric assay based on MUB-substrates. Enzyme kinetics (V-max and K-m) were estimated in three particle-size fractions and the unfractionated soil. The results showed that not all particle-size fractions were equally enzymatically active and that the distribution of enzymes between fractions depended on the enzyme. Carbohydrases predominated in the coarser fractions while phosphatase and leucine-aminopeptidase were predominant in the clay-size fraction. The Michaelis constant (K.) varied among fractions, indicating that the association of the same enzyme with different particle-size fractions affected its substrate affinity. The same values of Km were found in the same fractions from the soil under two contrasting fertilizer management regimes, indicating that the Michaelis constant was unaffected by soil changes caused by N fertilizer management. (C) 2004 Elsevier Ltd. All rights reserved.

Shifts in rhizosphere microbial communities and enzyme activity of Poa alpina across an alpine chronosequence

This study quantifies the influence of Poa alpina on the soil microbial community in primary succession of alpine ecosystems, and whether these effects are controlled by the successional stage. Four successional sites representative of four stages of grassland development (initial, 4 years (non-vegetated); pioneer, 20 years; transition, 75 years; mature, 9500 years old) on the Rotmoos glacier foreland, Austria, were sampled. The size, composition and activity of the microbial community in the rhizosphere and bulk soil were characterized using the chloroform-fumigation extraction procedure, phospholipid fatty acid (PLFA) analysis and measurements of the enzymes beta-glucosidase, beta-xylosidase, N-acetyl-beta-glucosaminidase, leucine aminopeptidase, acid phosphatase and sulfatase. The interplay between the host plant and the successional stage was quantified using principal component (PCA) and multidimensional scaling analyses. Correlation analyses were applied to evaluate the relationship between soil factors (C-org, N-t, C/N ratio, pH, ammonium, phosphorus, potassium) and microbial properties in the bulk soil. In the pioneer stage microbial colonization of the rhizosphere of P. alpina was dependent on the reservoir of microbial species in the bulk soil. As a consequence, the rhizosphere and bulk soil were similar in microbial biomass (ninhydrin-reactive nitrogen (NHR-N)), community composition (PLFA), and enzyme activity. In the transition and mature grassland stage, more benign soil conditions stimulated microbial growth (NHR-N, total amount of PLFA, bacterial PLFA, Gram-positive bacteria, Gram-negative bacteria), and microbial diversity (Shannon index H) in the rhizosphere either directly or indirectly through enhanced carbon allocation. In the same period, the rhizosphere microflora shifted from a G(-) to a more G(+), and from a fungal to a more bacteria-dominated community. Rhizosphere beta-xylosidase, N-acetyl-beta-glucosaminidase, and sulfatase activity peaked in the mature grassland soil, whereas rhizosphere leucine aminopeptidase, beta-glucosidase, and phosphatase activity were highest in the transition stage, probably because of enhanced carbon and nutrient allocation into the rhizosphere due to better growth conditions. Soil organic matter appeared to be the most important driver of microbial colonization in the bulk soil. The decrease in soil pH and soil C/N ratio mediated the shifts in the soil microbial community composition (bacPLFA, bacPLFA/fungPLFA, G(-), G(+)/G(-)). The activities of beta-glucosidase, beta-xylosidase and phosphatase were related to soil ammonium and phosphorus, indicating that higher decomposition rates enhanced the nutrient availability in the bulk soil. We conclude that the major determinants of the microllora vary along the successional gradient: in the pioneer stage the rhizosphere microflora was primarily determined by the harsh soil environment; under more favourable environmental conditions, however, the host plant selected for a specific microbial community that was related to the dynamic interplay between soil properties and carbon supply. (C) 2004 Elsevier Ltd. All rights reserved.

Systolic random number generation for genetic algorithms

A parallel hardware random number generator for use with a VLSI genetic algorithm processing device is proposed. The design uses an systolic array of mixed congruential random number generators. The generators are constantly reseeded with the outputs of the proceeding generators to avoid significant biasing of the randomness of the array which would result in longer times for the algorithm to converge to a solution. 1 Introduction In recent years there has been a growing interest in developing hardware genetic algorithm devices [1, 2, 3]. A genetic algorithm (GA) is a stochastic search and optimization technique which attempts to capture the power of natural selection by evolving a population of candidate solutions by a process of selection and reproduction [4]. In keeping with the evolutionary analogy, the solutions are called chromosomes with each chromosome containing a number of genes. Chromosomes are commonly simple binary strings, the bits being the genes.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Building synergies in development research: The application of the mosaic model to animal health research

Development research has responded to a number of charges over the past few decades. For example, when traditional research was accused of being 'top-down', the response was participatory research, linking the 'receptors' to the generators of research. As participatory processes were recognised as producing limited outcomes, the demand-led agenda was born. In response to the alleged failure of research to deliver its products, the 'joined-up' model, which links research with the private sector, has become popular. However, using examples from animal-health research, this article demonstrates that all the aforementioned approaches are seriously limited in their attempts to generate outputs to address the multi-faceted problems facing the poor. The article outlines a new approach to research: the Mosaic Model. By combining different knowledge forms, and focusing on existing gaps, the model aims to bridge basic and applied findings to enhance the efficiency and value of research, past, present, and future.

High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes

Asynchronous Optical Sampling has the potential to improve signal to noise ratio in THz transient sperctrometry. The design of an inexpensive control scheme for synchronising two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is discussed. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing recorded THz transients in the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse shaping using genetic algorithms are mentioned.

3D surface point and wireframe reconstruction from multiview photographic images

This paper describes a new method for reconstructing 3D surface points and a wireframe on the surface of a freeform object using a small number, e.g. 10, of 2D photographic images. The images are taken at different viewing directions by a perspective camera with full prior knowledge of the camera configurations. The reconstructed surface points are frontier points and the wireframe is a network of contour generators. Both of them are reconstructed by pairing apparent contours in the 2D images. Unlike previous works, we empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points automatically cluster closely on a highly curved part of the surface and are widely spread on smooth or flat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not under-sampled or under-represented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method. Given that the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object. The unique pattern of the reconstructed points and contours may be used in 31) object recognition and measurement without computationally intensive full surface reconstruction. The results are obtained from both computer-generated and real objects. (C) 2007 Elsevier B.V. All rights reserved.

Optimal 3D surface reconstruction from a small number of conventional 2D X-ray images

This paper describes a method for reconstructing 3D frontier points, contour generators and surfaces of anatomical objects or smooth surfaces from a small number, e. g. 10, of conventional 2D X-ray images. The X-ray images are taken at different viewing directions with full prior knowledge of the X-ray source and sensor configurations. Unlike previous works, we empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points automatically cluster closely on a highly curved part of the surface and are widely spread on smooth or flat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not under-sampled or under-represented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method. Given that the number of viewing directions is fixed and the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object. The technique may be used not only in medicine but also in industrial applications.

Position criticality in chess endgames

Some 50,000 Win Studies in Chess challenge White to find an effectively unique route to a win. Judging the impact of less than absolute uniqueness requires both technical analysis and artistic judgment. Here, for the first time, an algorithm is defined to help analyse uniqueness in endgame positions objectively. The key idea is to examine how critical certain positions are to White in achieving the win. The algorithm uses sub-n-man endgame tables (EGTs) for both Chess and relevant, adjacent variants of Chess. It challenges authors of EGT generators to generalise them to create EGTs for these chess variants. It has already proved efficient and effective in an implementation for Starchess, itself a variant of chess. The approach also addresses a number of similar questions arising in endgame theory, games and compositions.

Electromagnetic atmosphere-plasma coupling: the global atmospheric electric circuit

A description is given of the global atmospheric electric circuit operating between the Earth’s surface and the ionosphere. Attention is drawn to the huge range of horizontal and vertical spatial scales, ranging from 10−9 m to 1012 m, concerned with the many important processes at work. A similarly enormous range of time scales is involved from 10−6 s to 109 s, in the physical effects and different phenomena that need to be considered. The current flowing in the global circuit is generated by disturbed weather such as thunderstorms and electrified rain/shower clouds, mostly occurring over the Earth’s land surface. The profile of electrical conductivity up through the atmosphere, determined mainly by galactic cosmic ray ionization, is a crucial parameter of the circuit. Model simulation results on the variation of the ionospheric potential, ∼250 kV positive with respect to the Earth’s potential, following lightning discharges and sprites are summarized. Experimental results comparing global circuit variations with the neutron rate recorded at Climax, Colorado, are then discussed. Within the return (load) part of the circuit in the fair weather regions remote from the generators, charge layers exist on the upper and lower edges of extensive layer clouds; new experimental evidence for these charge layers is also reviewed. Finally, some directions for future research in the subject are suggested.

Asparagus: a horticultural ballet

Asparagus: A Horticultural Ballet was a live performance and film narrating the rise of capital in the medium of asparagus. The project stemmed from an obscure reference to an art piece of the same name by Waw Pierogi of the band xex. However, the its re-enactment had little to do with the original exploration of the growth and branching patterns of the asparagus plant. Instead, a rigid choreography inspired by Oskar Schlemmer's Triadic Ballet and based on Karl Marx's Capital dictated the movements of six performers in asparagus costumes. Bringing together the organic and the geometric, the ballet investigated the transition from the Fordist assembly line to immaterial labour through a reanimation of modernist abstraction. Being itself the story of abstraction, Capital shows how human relationships are replaced by those between commodities in the joyless grind of endless accumulation. This process results in the transcendent mythical figure of capital, which frames, transfigures and even produces the natural world. Asparagus: A Horticultural Ballet was produced in collaboration with Montreal based band Les Georges Leningrad and commissioned by The Showroom Gallery, London. It was presented live at Conway Hall in London on 6.3.07 and at the Montreal Biennale at SAT on 12.5.07. The performance was accompanied by a film at the Showroom gallery and preceded by a production residency at the Pump House Gallery. The film has subsequently been shown at The Golden Thread Gallery, Bluecoat Liverpool and as part of A-Lot-Ment in Portsmouth. Props from Asparagus: A Horticultural Ballet, were included in The Eagle Document at the Stephen Lawrence Gallery, London.

Can microgrids make a major contribution to UK energy supply?

Almost all the electricity currently produced in the UK is generated as part of a centralised power system designed around large fossil fuel or nuclear power stations. This power system is robust and reliable but the efficiency of power generation is low, resulting in large quantities of waste heat. The principal aim of this paper is to investigate an alternative concept: the energy production by small scale generators in close proximity to the energy users, integrated into microgrids. Microgrids—de-centralised electricity generation combined with on-site production of heat—bear the promise of substantial environmental benefits, brought about by a higher energy efficiency and by facilitating the integration of renewable sources such as photovoltaic arrays or wind turbines. By virtue of good match between generation and load, microgrids have a low impact on the electricity network, despite a potentially significant level of generation by intermittent energy sources. The paper discusses the technical and economic issues associated with this novel concept, giving an overview of the generator technologies, the current regulatory framework in the UK, and the barriers that have to be overcome if microgrids are to make a major contribution to the UK energy supply. The focus of this study is a microgrid of domestic users powered by small Combined Heat and Power generators and photovoltaics. Focusing on the energy balance between the generation and load, it is found that the optimum combination of the generators in the microgrid- consisting of around 1.4 kWp PV array per household and 45% household ownership of micro-CHP generators- will maintain energy balance on a yearly basis if supplemented by energy storage of 2.7 kWh per household. We find that there is no fundamental technological reason why microgrids cannot contribute an appreciable part of the UK energy demand. Indeed, an estimate of cost indicates that the microgrids considered in this study would supply electricity at a cost comparable with the present electricity supply if the current support mechanisms for photovoltaics were maintained. Combining photovoltaics and micro-CHP and a small battery requirement gives a microgrid that is independent of the national electricity network. In the short term, this has particular benefits for remote communities but more wide-ranging possibilities open up in the medium to long term. Microgrids could meet the need to replace current generation nuclear and coal fired power stations, greatly reducing the demand on the transmission and distribution network.