Estimation of water retention and availability in soils of Rio Grande do Sul

Dispersed information on water retention and availability in soils may be compiled in databases to generate pedotransfer functions. The objectives of this study were: to generate pedotransfer functions to estimate soil water retention based on easily measurable soil properties; to evaluate the efficiency of existing pedotransfer functions for different geographical regions for the estimation of water retention in soils of Rio Grande do Sul (RS); and to estimate plant-available water capacity based on soil particle-size distribution. Two databases were set up for soil properties, including water retention: one based on literature data (725 entries) and the other with soil data from an irrigation scheduling and management system (239 entries). From the literature database, pedotransfer functions were generated, nine pedofunctions available in the literature were evaluated and the plant-available water capacity was calculated. The coefficient of determination of some pedotransfer functions ranged from 0.56 to 0.66. Pedotransfer functions generated based on soils from other regions were not appropriate for estimating the water retention for RS soils. The plant-available water content varied with soil texture classes, from 0.089 kg kg-1 for the sand class to 0.191 kg kg-1 for the silty clay class. These variations were more related to sand and silt than to clay content. The soils with a greater silt/clay ratio, which were less weathered and with a greater quantity of smectite clay minerals, had high water retention and plant-available water capacity.

Transformation kernel density estimation of actuarial loss functions

[cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a dades d’assegurances i es mostra com calcular el Valor en Risc.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Pedotransfer functions related to spatial variability of water retention attributes for lowland soils

The estimation of non available soil variables through the knowledge of other related measured variables can be achieved through pedotransfer functions (PTF) mainly saving time and reducing cost. Great differences among soils, however, can yield non desirable results when applying this method. This study discusses the application of developed PTFs by several authors using a variety of soils of different characteristics, to evaluate soil water contents of two Brazilian lowland soils. Comparisons are made between PTF evaluated data and field measured data, using statistical and geostatistical tools, like mean error, root mean square error, semivariogram, cross-validation, and regression coefficient. The eight tested PTFs to evaluate gravimetric soil water contents (Ug) at the tensions of 33 kPa and 1,500 kPa presented a tendency to overestimate Ug 33 kPa and underestimate Ug1,500 kPa. The PTFs were ranked according to their performance and also with respect to their potential in describing the structure of the spatial variability of the set of measured values. Although none of the PTFs have changed the distribution pattern of the data, all resulted in mean and variance statistically different from those observed for all measured values. The PTFs that presented the best predictive values of Ug33 kPa and Ug1,500 kPa were not the same that had the best performance to reproduce the structure of spatial variability of these variables.

On making causal claims: A review and recommendations

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity--which includes omitted variables, omitted selection, simultaneity, common methods bias, and measurement error--renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation is confounded, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66 % and up to 90 % of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Impact of incorrect assumptions on the covariance structure of random effects and/or residuals in nonlinear mixed models for repeated measures data

In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assumptions on the true structure of the random effects covariance matrix and the true correlation pattern of residuals, over the performance of an estimation method for nonlinear mixed models. The procedure under study is the well known linearization method due to Lindstrom and Bates (1990), implemented in the nlme library of S-Plus and R. Its performance is studied in terms of bias, mean square error (MSE), and true coverage of the associated asymptotic confidence intervals. Ignoring other criteria like the convenience of avoiding over parameterised models, it seems worst to erroneously assume some structure than do not assume any structure when this would be adequate.

Optimal estimation of two-qubit pure-state entanglement

We present optimal measuring strategies for an estimation of the entanglement of unknown two-qubit pure states and of the degree of mixing of unknown single-qubit mixed states, of which N identical copies are available. The most general measuring strategies are considered in both situations, to conclude in the first case that a local, although collective, measurement suffices to estimate entanglement, a nonlocal property, optimally.

Estimation of temperature effects on fission barriers

An accurate mass formula at finite temperature has been used to obtain a more precise estimation of temperature effects on fission barriers calculated within the liquid drop model.

Main-Channel Slopes of Selected Streams in Iowa for Estimation of Flood-Frequency Discharges, November 2004

This report describes a statewide study conducted to develop main-channel slope (MCS) curves for 138 selected streams in Iowa with drainage areas greater than 100 square miles. MCS values determined from the curves can be used in regression equations for estimating flood frequency discharges. Multi-variable regression equations previously developed for two of the three hydrologic regions defined for Iowa require the measurement of MCS. Main-channel slope is a difficult measurement to obtain for large streams using 1:24,000-scale topographic maps. The curves developed in this report provide a simplified method for determining MCS values for sites located along large streams in Iowa within hydrologic Regions 2 and 3. The curves were developed using MCS values quantified for 2,058 selected sites along 138 selected streams in Iowa. A geographic information system (GIS) technique and 1:24,000-scale topographic data were used to quantify MCS values for the stream sites. The sites were selected at about 5-mile intervals along the streams. River miles were quantified for each stream site using a GIS program. Data points for river-mile and MCS values were plotted and a best-fit curve was developed for each stream. An adjustment was applied to all 138 curves to compensate for differences in MCS values between manual measurements and GIS quantification. The multi-variable equations for Regions 2 and 3 were developed using manual measurements of MCS. A comparison of manual measurements and GIS quantification of MCS indicates that manual measurements typically produce greater values of MCS compared to GIS quantification. Median differences between manual measurements and GIS quantification of MCS are 14.8 and 17.7 percent for Regions 2 and 3, respectively. Comparisons of percentage differences between flood-frequency discharges calculated using MCS values of manual measurements and GIS quantification indicate that use of GIS values of MCS for Region 3 substantially underestimate flood discharges. Mean and median percentage differences for 2- to 500-year recurrence-interval flood discharges ranged from 5.0 to 5.3 and 4.3 to 4.5 percent, respectively, for Region 2 and ranged from 18.3 to 27.1 and 12.3 to 17.3 percent for Region 3. The MCS curves developed from GIS quantification were adjusted by 14.8 percent for streams located in Region 2 and by 17.7 percent for streams located in Region 3. Comparisons of percentage differences between flood discharges calculated using MCS values of manual measurements and adjusted-GIS quantification for Regions 2 and 3 indicate that the flood-discharge estimates are comparable. For Region 2, mean percentage differences for 2- to 500-year recurrence-interval flood discharges ranged between 0.6 and 0.8 percent and median differences were 0.0 percent. For Region 3, mean and median differences ranged between 5.4 to 8.4 and 0.0 to 0.3 percent, respectively. A list of selected stream sites presented with each curve provides information about the sites including river miles, drainage areas, the location of U.S. Geological Survey stream flowgage stations, and the location of streams Abstract crossing hydro logic region boundaries or the Des Moines Lobe landforms region boundary. Two examples are presented for determining river-mile and MCS values, and two techniques are presented for computing flood-frequency discharges.

Automated DNA extraction using the QIAsymphony platform: Estimation of DNA recovery from simulated forensic stains

The aim of this study was to evaluate the forensic protocol recently developed by Qiagen for the QIAsymphony automated DNA extraction platform. Samples containing low amounts of DNA were specifically considered, since they represent the majority of samples processed in our laboratory. The analysis of simulated blood and saliva traces showed that the highest DNA yields were obtained with the maximal elution volume available for the forensic protocol, that is 200 ml. Resulting DNA extracts were too diluted for successful DNA profiling and required a concentration. This additional step is time consuming and potentially increases inversion and contamination risks. The 200 ml DNA extracts were concentrated to 25 ml, and the DNA recovery estimated with real-time PCR as well as with the percentage of SGM Plus alleles detected. Results using our manual protocol, based on the QIAamp DNA mini kit, and the automated protocol were comparable. Further tests will be conducted to determine more precisely DNA recovery, contamination risk and PCR inhibitors removal, once a definitive procedure, allowing the concentration of DNA extracts from low yield samples, will be available for the QIAsymphony.

Malaria haplotype frequency estimation.

We present a Bayesian approach for estimating the relative frequencies of multi-single nucleotide polymorphism (SNP) haplotypes in populations of the malaria parasite Plasmodium falciparum by using microarray SNP data from human blood samples. Each sample comes from a malaria patient and contains one or several parasite clones that may genetically differ. Samples containing multiple parasite clones with different genetic markers pose a special challenge. The situation is comparable with a polyploid organism. The data from each blood sample indicates whether the parasites in the blood carry a mutant or a wildtype allele at various selected genomic positions. If both mutant and wildtype alleles are detected at a given position in a multiply infected sample, the data indicates the presence of both alleles, but the ratio is unknown. Thus, the data only partially reveals which specific combinations of genetic markers (i.e. haplotypes across the examined SNPs) occur in distinct parasite clones. In addition, SNP data may contain errors at non-negligible rates. We use a multinomial mixture model with partially missing observations to represent this data and a Markov chain Monte Carlo method to estimate the haplotype frequencies in a population. Our approach addresses both challenges, multiple infections and data errors.

Measurement Errors Should Always Be Incorporated in Phylogenetic Comparative Analysis

The evolution of continuous traits is the central component of comparative analyses in phylogenetics, and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models, and we explore the effects of random errors in trait measurement on the accuracy of model selection. We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein-Uhlenbeck (OU) and accelerating/decelerating rates (ACDC). Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favour of more parameter-rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection. Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support. Importantly, as measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.

The efficiency of different estimation methods of hydro-physical limits

The soil water available to crops is defined by specific values of water potential limits. Underlying the estimation of hydro-physical limits, identified as permanent wilting point (PWP) and field capacity (FC), is the selection of a suitable method based on a multi-criteria analysis that is not always clear and defined. In this kind of analysis, the time required for measurements must be taken into consideration as well as other external measurement factors, e.g., the reliability and suitability of the study area, measurement uncertainty, cost, effort and labour invested. In this paper, the efficiency of different methods for determining hydro-physical limits is evaluated by using indices that allow for the calculation of efficiency in terms of effort and cost. The analysis evaluates both direct determination methods (pressure plate - PP and water activity meter - WAM) and indirect estimation methods (pedotransfer functions - PTFs). The PTFs must be validated for the area of interest before use, but the time and cost associated with this validation are not included in the cost of analysis. Compared to the other methods, the combined use of PP and WAM to determine hydro-physical limits differs significantly in time and cost required and quality of information. For direct methods, increasing sample size significantly reduces cost and time. This paper assesses the effectiveness of combining a general analysis based on efficiency indices and more specific analyses based on the different influencing factors, which were considered separately so as not to mask potential benefits or drawbacks that are not evidenced in efficiency estimation.