Soil CO(2) emission estimated by different interpolation techniques

Soil CO(2) emissions are highly variable, both spatially and across time, with significant changes even during a one-day period. The objective of this study was to compare predictions of the diurnal soil CO(2) emissions in an agricultural field when estimated by ordinary kriging and sequential Gaussian simulation. The dataset consisted of 64 measurements taken in the morning and in the afternoon on bare soil in southern Brazil. The mean soil CO(2) emissions were significantly different between the morning (4.54 mu mol m(-2) s(-1)) and afternoon (6.24 mu mol m(-2) s(-1)) measurements. However, the spatial variability structures were similar, as the models were spherical and had close range values of 40.1 and 40.0 m for the morning and afternoon semivariograms. In both periods, the sequential Gaussian simulation maps were more efficient for the estimations of emission than ordinary kriging. We believe that sequential Gaussian simulation can improve estimations of soil CO(2) emissions in the field, as this property is usually highly non-Gaussian distributed.

Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane

The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Sacchetrum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index (LA!), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300). The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies.

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and interannually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R(2) = 0.80 and R(2) = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages. (C) 2010 Elsevier Inc. All rights reserved.

Incompatibility of Macroscopic Local Realism with Quantum Mechanics in Measurements with Macroscopic Uncertainties

We show that quantum mechanics predicts a contradiction with local hidden variable theories for photon number measurements which have limited resolving power, to the point of imposing an uncertainty in the photon number result which is macroscopic in absolute terms. We show how this can be interpreted as a failure of a new premise, macroscopic local realism.

Intuition: Myth or a Decision-making Tool?

Faced with today’s ill-structured business environment of fast-paced change and rising uncertainty, organizations have been searching for management tools that will perform satisfactorily under such ambiguous conditions. In the arena of managerial decision making, one of the approaches being assessed is the use of intuition. Based on our definition of intuition as a non-sequential information-processing mode, which comprises both cognitive and affective elements and results in direct knowing without any use of conscious reasoning, we develop a testable model of integrated analytical and intuitive decision making and propose ways to measure the use of intuition.

Spatial Technologies: What use are they to managers of environmental lands?

This paper reviews the potential use of three types of spatial technology to land managers, namely satellite imagery, satellite positioning systems and supporting computer software. Developments in remote sensing and the relative advantages of multispectral and hyperspectral images are discussed. The main challenge to the wider use of remote sensing as a land management tool is seen as uncertainty whether apparent relationships between biophysical variables and spectral reflectance are direct and causal, or artefacts of particular images. Developments in satellite positioning systems are presented in the context of land managers’ need for position estimates in situations where absolute precision may or may not be required. The role of computer software in supporting developments in spatial technology is described. Spatial technologies are seen as having matured beyond empirical applications to the stage where they are useful and reliable land management tools. In addition, computer software has become more user-friendly and this has facilitated data collection and manipulation by semi-expert as well as specialist staff.

Adverse effects of cannabis

Cannabis is the most widely used illicit drug in many developed societies. Its health and psychological effects are not well understood and remain the subject of much debate, with opinions on its risks polarised along the lines of proponents' views on what its legal status should be. An unfortunate consequence of this polarisation of opinion has been the absence of any consensus on what health information the medical profession should give to patients who are users or potential users of cannabis. There is conflicting evidence about many of the effects of cannabis use, so we summarise the evidence on the most probable adverse health and psychological consequences of acute and chronic use. This uncertainty, however, should not prevent medical practitioners from advising patients about the most likely ill-effects of their cannabis use. Here we make some suggestions about the advice doctors can give to patients who use, or are contemplating the use, of this drug.

A state-contingent production approach to principal-agent problems with an application to point-source pollution control

Two basic representations of principal-agent relationships, the 'state-space' and 'parameterized distribution' formulations, have emerged. Although the state-space formulation appears more natural, analytical studies using this formulation have had limited success. This paper develops a state-space formulation of the moral-hazard problem using a general representation of production under uncertainty. A closed-form solution for the agency-cost problem is derived. Comparative-static results are deduced. Next we solve the principal's problem of selecting the optimal output given the agency-cost function. The analysis is applied to the problem of point-source pollution control. (C) 1998 Published by Elsevier Science S.A. All rights reserved.

Risk premiums and benefit measures for generalized-expected-utility theories

Standard tools for the analysis of economic problems involving uncertainty, including risk premiums, certainty equivalents and the notions of absolute and relative risk aversion, are developed without making specific assumptions on functional form beyond the basic requirements of monotonicity, transitivity, continuity, and the presumption that individuals prefer certainty to risk. Individuals are not required to display probabilistic sophistication. The approach relies on the distance and benefit functions to characterize preferences relative to a given state-contingent vector of outcomes. The distance and benefit functions are used to derive absolute and relative risk premiums and to characterize preferences exhibiting constant absolute risk aversion (CARA) and constant relative risk aversion (CRRA). A generalization of the notion of Schur-concavity is presented. If preferences are generalized Schur concave, the absolute and relative risk premiums are generalized Schur convex, and the certainty equivalents are generalized Schur concave.

Cost-effectiveness analysis of humanitarian relief interventions: visceral leishmaniasis treatment in the Sudan

Spending by aid agencies on emergencies has quadrupled over the last decade, to over US$ 6 billion. To date, cost-effectiveness has seldom been considered in the prioritization and evaluation of emergency interventions. The sheer volume of resources spent on humanitarian aid and the chronicity of many humanitarian interventions call for more attention to be paid to the issue of 'value for money'. In this paper we present data from a major humanitarian crisis, an epidemic of visceral leishmaniasis (VL) in war-torn Sudan. The special circumstances provided us, in retrospect, with unusually accurate data on excess mortality, costs of the intervention and its effects, thus allowing us to express cost-effectiveness as the cost per Disability Adjusted Life Year (DALY) averted. The cost-effectiveness ratio, of US$ 18.40 per DALY (uncertainty range between US$ 13.53 and US$ 27.63), places the treatment of VL in Sudan among health interventions considered 'very flood value for money' (interventions of less than US$ 25 per DALY). We discuss the usefulness of this analysis to the internal management of the VL programme, the procurement of funds for the programme, and more generally, to priority setting in humanitarian relief interventions. We feel that in evaluations of emergency interventions attempts could be made more often to perform cost-effectiveness analyses, including the use of DALYs, provided that the outcomes of these analyses are seen in the broad context of the emergency situation and its consequences on the affected population. This paper provides a first contribution to what is hoped to become an international database of cost-effectiveness studies of health outcome such as the DALY.

Response to price and production risk: The case of Australian wheat

A model of Australian wheat grower supply response was specified under the constraints of price and yield uncertainty, risk aversion, partial adjustment, and quadratic costs. The model was solved to obtain area planted. The results of estimation indicate that risk arising from prices and climate have had a significant influence on producer decision making. The coefficient of relative risk aversion and short-run and long-run elasticities of supply with respect to price were calculated. Wheat growers' risk premium, expected at the start of the season for exposed price and yield risk, was 2.8 percent of revenue or 10.4 percent of profit as measured by producer surplus. (C) 2000 John Wiley & Sons, Inc.

Effects of preferred hand and sex on the perception of tactile simultaneity

The effects of handedness, sex and the influence of hand placement in extrapersonal space on temporal information processing was investigated by measuring thresholds for perceiving the simultaneity of pairs of tactile stimuli. Simultaneity thresholds of preferred right handed and left handed university students with left hemisphere speech representation were compared using unimanual and bimanual stimulation at three hand placements (midline, lateral and crossed). In unimanual conditions two fingers of one hand were stimulated (single hemisphere), whereas in the bimanual conditions one finger of each hand was stimulated (cross hemispheres). Bimanual minus unimanual thresholds provided an estimate of interhemisphere transmission time (IHTT) regardless of hand placement. The effects of hemispace varied with the type of stimulation. With unimanual stimulation, overall thresholds were longer at the midline placement, however, with bimanual stimulation, thresholds were longer when the hands were spatially separated (crossed and/or uncrossed). Left handers' IHTTs were 8 ms faster than those of right handers. IHTTs in males were faster than females with hands placed in lateral (by 10.8 ms) or crossed (by 9.8 ms) but not midline positions. It was concluded that the cerebral hemispheres are equally capable of discriminating temporal intervals, but that the left hemisphere predominates when there is uncertainty about location of stimulation.

The relationship between radiation-induced chemical processes and transverse relaxation times in polymer gel dosimeters

The effects of ionizing radiation in different compositions of polymer gel dosimeters are investigated using FT-Raman spectroscopy and NMR T-2 relaxation times. The dosimeters are manufactured from different concentrations of comonomers (acrylamide and N,N'-methylene-bis-acrylamide) dispersed in different concentrations of an aqueous gelatin matrix. Results are analysed using a model of fast exchange of magnetization between three proton pools. The fraction of protons in each pool is determined using the known chemical composition of the dosimeter and FT-Raman spectroscopy. Based on these results, the physical and chemical processes in interplay in the dosimeters are examined in view of their effect on the changes in T-2 The precipitation of growing macroradicals and the scavenging of free radicals by gelatin are used to explain the rate of polymerization. The model describes the changes in T-2 as a function of the absorbed dose up to 50 Gy for the different compositions. This is expected to aid the theoretical design of new, more efficient dosimeters, since it was demonstrated that the optimum dosimeter (i.e, with the lowest dose resolution) must have a range of relaxation times which match the range of T-2 values which can be determined with the lowest uncertainty using an MRI scanner.

Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

Stochastic structure and individual-tree growth models

The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.