Modelling effects of prolonged conditioning on dormancy and germination of Striga hermonthica

The impact of environment on the germination biology of the parasite was studied in the laboratory with seeds conditioned at various water potentials, urea concentrations and at 17.5 to 37.5°C for up to 133 days. Maximum germination was observed at 20 to 25°C. Water stress and urea suppressed maximum germination. The final percentage germination response to period of conditioning showed a non-linear relationship and suggests the release of seeds from dormancy during the initial period and later on dormancy induction. Germination percentage increased with increase in conditioning period to a threshold and remained stable for variable periods followed by a decline with further extension of conditioning time. The decline in germination finally terminated in zero germination in most treatments before the end of experimentation. The investigated factors of temperature, water potential and urea showed clear effects on the expression of dormancy pattern of the parasite. The effects of water potential and urea were viewed as modifying a primary response of seeds to temperature during conditioning. The changes in germinability potential during conditioning were consistent with the hypothesis that dormancy periods are normally distributed within seed populations and that loss of primary dormancy precedes induction of secondary dormancy. Hence an additive mathematical model of loss of primary dormancy and induction of secondary as affected by environment was developed as: G = {[Φ-1 (Kp+ (po+pnN+pwW) (T-Tb) t)]-[Φ-1 (Ks+ ((swW+sa)+sorT)t)]}[Φ-1(aT2+bT+c+cwW)].

A structural time series approach to modelling multiple and resurgent meat scares in Italy

This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.

Trophic interactions in a changing world: modelling aboveground-belowground interactions

The rate and scale of human-driven changes can exert profound impacts on ecosystems, the species that make them up and the services they provide that sustain humanity. Given the speed at which these changes are occurring, one of society's major challenges is to coexist within ecosystems and to manage ecosystem services in a sustainable way. The effect of possible scenarios of global change on ecosystem services can be explored using ecosystem models. Such models should adequately represent ecosystem processes above and below the soil surface (aboveground and belowground) and the interactions between them. We explore possibilities to include such interactions into ecosystem models at scales that range from global to local. At the regional to global scale we suggest to expand the plant functional type concept (aggregating plants into groups according to their physiological attributes) to include functional types of aboveground-belowground interactions. At the scale of discrete plant communities, process-based and organism-oriented models could be combined into "hybrid approaches" that include organism-oriented mechanistic representation of a limited number of trophic interactions in an otherwise process - oriented approach. Under global change the density and activity of organisms determining the processes may change non-linearly and therefore explicit knowledge of the organisms and their responses should ideally be included. At the individual plant scale a common organism-based conceptual model of aboveground-belowground interactions has emerged. This conceptual model facilitates the formulation of research questions to guide experiments aiming to identify patterns that are common within, but differ between, ecosystem types and biomes. Such experiments inform modelling approaches at larger scales. Future ecosystem models should better include this evolving knowledge of common patterns of aboveground-belowground interactions. Improved ecosystem models are necessary toots to reduce the uncertainty in the information that assists us in the sustainable management of our environment in a changing world. (C) 2004 Elsevier GmbH. All rights reserved.

Monthly average temperature modelling

This research is associated with the goal of the horticultural sector of the Colombian southwest, which is to obtain climatic information, specifically, to predict the monthly average temperature in sites where it has not been measured. The data correspond to monthly average temperature, and were recorded in meteorological stations at Valle del Cauca, Colombia, South America. Two components are identified in the data of this research: (1) a component due to the temporal aspects, determined by characteristics of the time series, distribution of the monthly average temperature through the months and the temporal phenomena, which increased (El Nino) and decreased (La Nina) the temperature values, and (2) a component due to the sites, which is determined for the clear differentiation of two populations, the valley and the mountains, which are associated with the pattern of monthly average temperature and with the altitude. Finally, due to the closeness between meteorological stations it is possible to find spatial correlation between data from nearby sites. In the first instance a random coefficient model without spatial covariance structure in the errors is obtained by month and geographical location (mountains and valley, respectively). Models for wet periods in mountains show a normal distribution in the errors; models for the valley and dry periods in mountains do not exhibit a normal pattern in the errors. In models of mountains and wet periods, omni-directional weighted variograms for residuals show spatial continuity. The random coefficient model without spatial covariance structure in the errors and the random coefficient model with spatial covariance structure in the errors are capturing the influence of the El Nino and La Nina phenomena, which indicates that the inclusion of the random part in the model is appropriate. The altitude variable contributes significantly in the models for mountains. In general, the cross-validation process indicates that the random coefficient model with spatial spherical and the random coefficient model with spatial Gaussian are the best models for the wet periods in mountains, and the worst model is the model used by the Colombian Institute for Meteorology, Hydrology and Environmental Studies (IDEAM) to predict temperature.

Modelling heterotachy in phylogenetic inference by reversible-jump Markov chain Monte Carlo

The rate at which a given site in a gene sequence alignment evolves over time may vary. This phenomenon-known as heterotachy-can bias or distort phylogenetic trees inferred from models of sequence evolution that assume rates of evolution are constant. Here, we describe a phylogenetic mixture model designed to accommodate heterotachy. The method sums the likelihood of the data at each site over more than one set of branch lengths on the same tree topology. A branch-length set that is best for one site may differ from the branch-length set that is best for some other site, thereby allowing different sites to have different rates of change throughout the tree. Because rate variation may not be present in all branches, we use a reversible-jump Markov chain Monte Carlo algorithm to identify those branches in which reliable amounts of heterotachy occur. We implement the method in combination with our 'pattern-heterogeneity' mixture model, applying it to simulated data and five published datasets. We find that complex evolutionary signals of heterotachy are routinely present over and above variation in the rate or pattern of evolution across sites, that the reversible-jump method requires far fewer parameters than conventional mixture models to describe it, and serves to identify the regions of the tree in which heterotachy is most pronounced. The reversible-jump procedure also removes the need for a posteriori tests of 'significance' such as the Akaike or Bayesian information criterion tests, or Bayes factors. Heterotachy has important consequences for the correct reconstruction of phylogenies as well as for tests of hypotheses that rely on accurate branch-length information. These include molecular clocks, analyses of tempo and mode of evolution, comparative studies and ancestral state reconstruction. The model is available from the authors' website, and can be used for the analysis of both nucleotide and morphological data.

Computer modelling and estimation of recruitment patterns of non-branching coral colonies at three sites in Wakatobi Marine Park, S.E. Sulawesi, Indonesia; implications for coral reef conservation

We have studied growth and estimated recruitment of massive coral colonies at three sites, Kaledupa, Hoga and Sampela, separated by about 1.5 km in the Wakatobi Marine National Park, S.E. Sulawesi, Indonesia. There was significantly higher species richness (P<0.05), coral cover (P<0.05) and rugosity (P<0.01) at Kaledupa than at Sampela. A model for coral reef growth has been developed based on a rational polynomial function, where dx/dt is an index of coral growth with time; W is the variable (for example, coral weight, coral length or coral area), up to the power of n in the numerator and m in the denominator; a1……an and b1…bm are constants. The values for n and m represent the degree of the polynomial, and can relate to the morphology of the coral. The model was used to simulate typical coral growth curves, and tested using published data obtained by weighing coral colonies underwater in reefs on the south-west coast of Curaçao [‘Neth. J. Sea Res. 10 (1976) 285’]. The model proved an accurate fit to the data, and parameters were obtained for a number of coral species. Surface area data was obtained on over 1200 massive corals at three different sites in the Wakatobi Marine National Park, S.E. Sulawesi, Indonesia. The year of an individual's recruitment was calculated from knowledge of the growth rate modified by application of the rational polynomial model. The estimated pattern of recruitment was variable, with little numbers of massive corals settling and growing before 1950 at the heavily used site, Sampela, relative to the reef site with little or no human use, Kaledupa, and the intermediate site, Hoga. There was a significantly greater sedimentation rate at Sampela than at either Kaledupa (P<0.0001) or Hoga (P<0.0005). The relative mean abundance of fish families present at the reef crests at the three sites, determined using digital video photography, did not correlate with sedimentation rates, underwater visibility or lack of large non-branching coral colonies. Radial growth rates of three genera of non-branching corals were significantly lower at Sampela than at Kaledupa or at Hoga, and there was a high correlation (r=0.89) between radial growth rates and underwater visibility. Porites spp. was the most abundant coral over all the sites and at all depths followed by Favites (P<0.04) and Favia spp. (P<0.03). Colony ages of Porites corals were significantly lower at the 5 m reef flat on the Sampela reef than at the same depth on both other reefs (P<0.005). At Sampela, only 2.8% of corals on the 5 m reef crest are of a size to have survived from before 1950. The Scleractinian coral community of Sampela is severely impacted by depositing sediments which can lead to the suffocation of corals, whilst also decreasing light penetration resulting in decreased growth and calcification rates. The net loss of material from Sampela, if not checked, could result in the loss of this protective barrier which would be to the detriment of the sublittoral sand flats and hence the Sampela village.

Modelling the effect of fatty acids in odour development of cooked meat in vitro: part I - sensory perception

We have investigated the contribution of muscle components to the development of cooked meat odour in an aqueous model system using trained taste panels. Reaction mixtures were prepared with oleic, linoleic and linolenic acids with or without cysteine and ribose in a buffer with or without ferrous sulphate. Odour profiles were assessed and triangular tests were used to determine the ability of panellists to discriminate between mixtures. The presence of sugar and amino acid was highly detectable by panellists independently of the fatty acid considered (P < 0.001). However, the presence of C18:3 made differences. more obvious between mixtures than the presence of C18:1 or C18:2. `Meaty' notes were only associated with cysteine and ribose. `Fishy' notes were only apparent in C18:3 mixtures with or without sugar and amino acid, although the presence of cysteine and ribose decreased the perception. The addition of Fe+ +, a pro-oxidant present in the muscle, produced a reduction in the score of the attributes although the pattern was the same as when Fe was not used in the mixtures. Only `fishy' notes that were exclusively perceived in C18:3 mixtures showed a higher score in the presence of iron. Iron also produced a better discrimination in C18:3 mixtures, which were closely related to `grassy' notes in the presence of cysteine and ribose. (C) 2002 Published by Elsevier Science Ltd.

Reduced-order modelling of a high-voltage oscillatory impulse generator

The design of high-voltage equipment encompasses the study of oscillatory surges caused by transients such as those produced by switching. By obtaining a model, the response of which reconstructs that observed in the actual system, simulation studies and critical tests can be carried out on the model rather than on the equipment itself. In this paper, methods for the construction of simplified models are described and it is shown how the use of a complex model does not necessarily result in superior response pattern reconstruction.

Effects of Thomson-scattering geometry on white-light imaging of an interplanetary shock: synthetic observations from forward magnetohydrodynamic modelling

Stereoscopic white-light imaging of a large portion of the inner heliosphere has been used to track interplanetary coronal mass ejections. At large elongations from the Sun, the white-light brightness depends on both the local electron density and the efficiency of the Thomson-scattering process. To quantify the effects of the Thomson-scattering geometry, we study an interplanetary shock using forward magnetohydrodynamic simulation and synthetic white-light imaging. Identifiable as an inclined streak of enhanced brightness in a time–elongation map, the travelling shock can be readily imaged by an observer located within a wide range of longitudes in the ecliptic. Different parts of the shock front contribute to the imaged brightness pattern viewed by observers at different longitudes. Moreover, even for an observer located at a fixed longitude, a different part of the shock front will contribute to the imaged brightness at any given time. The observed brightness within each imaging pixel results from a weighted integral along its corresponding ray-path. It is possible to infer the longitudinal location of the shock from the brightness pattern in an optical sky map, based on the east–west asymmetry in its brightness and degree of polarisation. Therefore, measurement of the interplanetary polarised brightness could significantly reduce the ambiguity in performing three-dimensional reconstruction of local electron density from white-light imaging.

Modelling fire and the terrestrial carbon balance

Four CO2 concentration inversions and the Global Fire Emissions Database (GFED) versions 2.1 and 3 are used to provide benchmarks for climate-driven modeling of the global land-atmosphere CO2 flux and the contribution of wildfire to this flux. The Land surface Processes and exchanges (LPX) model is introduced. LPX is based on the Lund-Potsdam-Jena Spread and Intensity of FIRE (LPJ-SPITFIRE) model with amended fire probability calculations. LPX omits human ignition sources yet simulates many aspects of global fire adequately. It captures the major features of observed geographic pattern in burnt area and its seasonal timing and the unimodal relationship of burnt area to precipitation. It simulates features of geographic variation in the sign of the interannual correlations of burnt area with antecedent dryness and precipitation. It simulates well the interannual variability of the global total land-atmosphere CO2 flux. There are differences among the global burnt area time series from GFED2.1, GFED3 and LPX, but some features are common to all. GFED3 fire CO2 fluxes account for only about 1/3 of the variation in total CO2 flux during 1997–2005. This relationship appears to be dominated by the strong climatic dependence of deforestation fires. The relationship of LPX-modeled fire CO2 fluxes to total CO2 fluxes is weak. Observed and modeled total CO2 fluxes track the El Niño–Southern Oscillation (ENSO) closely; GFED3 burnt area and global fire CO2 flux track the ENSO much less so. The GFED3 fire CO2 flux-ENSO connection is most prominent for the El Niño of 1997–1998, which produced exceptional burning conditions in several regions, especially equatorial Asia. The sign of the observed relationship between ENSO and fire varies regionally, and LPX captures the broad features of this variation. These complexities underscore the need for process-based modeling to assess the consequences of global change for fire and its implications for the carbon cycle.

Modelling the high-latitude ionosphere for time-varying plasma convection

The paper discusses how variations in the pattern of convective plasma flows should beincluded in self-consistent time-dependent models of the coupled ionosphere-thermosphere system. The author shows how these variations depend upon the mechanism by which the solar wind flow excites the convection. The modelling of these effects is not just of relevance to the polar ionosphere. This is because the influence of convection is not confined to high latitudes: the resultant heating and composition changes in the thermosphere are communicated to lower latitudes by the winds which are also greatly modified by the plasma convection. These thermospheric changes alter the global distribution of plasma by modulatingthe rates of the chemical reactions which areresponsible for the loss of plasma. Hence the modelling of these high-latitude processes is of relevanceto the design and operation of HF communication, radar and navigation systems worldwide.

The status and challenge of global fire modelling

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what essons may be learned from FireMIP.

MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS

Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.