Soil organic carbon and fertility interactions affected by a tillage chronosequence in a Brazilian Oxisol

No-till (NT) adoption is an essential tool for development of sustainable agricultural systems, and how NT affects the soil organic C (SOC) dynamics is a key component of these systems. The effect of a plow tillage (PT) and NT age chronosequence on SOC concentration and interactions with soil fertility were assessed in a variable charge Oxisol, located in the South Center quadrant of Parana State, Brazil (50 degrees 23`W and 24 degrees 36`S). The chronosequence consisted of the following six sites: (i) native field (NF); (ii) PT of the native field (PNF-1) involving conversion of natural vegetation to cropland; (iii) NT for 10 years (NT-10); (iv) NT for 20 years (NT-20); (v) NT for 22 years (NT-22); and (vi) conventional tillage for 22 years (CT-22) involving PT with one disking after summer harvest and one after winter harvest to 20 cm depth plus two harrow disking. Soil samples were collected from five depths (0-2.5; 2.5-5; 5-10; 10-20; and 20-40 cm) and SOC, pH (in H(2)O and KCl), Delta pH, potential acidity, exchangeable bases, and cation exchangeable capacity (CEC) were measured. An increase in SOC concentration positively affected the pH, the negative charge and the CEC and negatively impacted potential acidity. Regression analyses indicated a close relationship between the SOC concentration and other parameters measured in this study. The regression fitted between SOC concentration and CEC showed a close relationship. There was an increase in negative charge and CEC with increase in SOC concentration: CEC increased by 0.37 cmol(c) kg(-1) for every g of C kg(-1) soil. The ratio of ECEC:SOC was 0.23 cmol(c) kg(-1) for NF and increased to 0.49 cmol(c) kg(-1) for NT-22. The rates of P and K for 0-10 cm depth increased by 9.66 kg ha(-1) yr(-1) and 17.93 kg ha(-1) yr(-1), respectively, with NF as a base line. The data presented support the conclusion that long-term NT is a useful strategy for improving fertility of soils with variable charge. (C) 2008 Elsevier B.V. All rights reserved.

Discharge-calcium concentration relationships in streams of the Amazon and Cerrado of Brazil: soil or land use controlled

Stream discharge-concentration relationships are indicators of terrestrial ecosystem function. Throughout the Amazon and Cerrado regions of Brazil rapid changes in land use and land cover may be altering these hydrochemical relationships. The current analysis focuses on factors controlling the discharge-calcium (Ca) concentration relationship since previous research in these regions has demonstrated both positive and negative slopes in linear log(10)discharge-log(10)Ca concentration regressions. The objective of the current study was to evaluate factors controlling stream discharge-Ca concentration relationships including year, season, stream order, vegetation cover, land use, and soil classification. It was hypothesized that land use and soil class are the most critical attributes controlling discharge-Ca concentration relationships. A multilevel, linear regression approach was utilized with data from 28 streams throughout Brazil. These streams come from three distinct regions and varied broadly in watershed size (< 1 to > 10(6) ha) and discharge (10(-5.7)-10(3.2) m(3) s(-1)). Linear regressions of log(10)Ca versus log(10)discharge in 13 streams have a preponderance of negative slopes with only two streams having significant positive slopes. An ANOVA decomposition suggests the effect of discharge on Ca concentration is large but variable. Vegetation cover, which incorporates aspects of land use, explains the largest proportion of the variance in the effect of discharge on Ca followed by season and year. In contrast, stream order, land use, and soil class explain most of the variation in stream Ca concentration. In the current data set, soil class, which is related to lithology, has an important effect on Ca concentration but land use, likely through its effect on runoff concentration and hydrology, has a greater effect on discharge-concentration relationships.

Number of options in a movement sequence affects learners' behavior in a self-controlled practice condition

Self controlling practice implies a process of decision making which suggests that the options in a self controlled practice condition could affect learners The number of task components with no fixed position in a movement sequence may affect the (Nay learners self control their practice A 200 cm coincident timing track with 90 light emitting diodes (LEDs)-the first and the last LEDs being the warning and the target lights respectively was set so that the apparent speed of the light along the track was 1 33 m/sec Participants were required to touch six sensors sequentially the last one coincidently with the lighting of the tar get light (timing task) Group 1 (n=55) had only one constraint and were instructed to touch the sensors in any order except for the last sensor which had to be the one positioned close to the target light Group 2 (n=53) had three constraints the first two and the last sensor to be touched Both groups practiced the task until timing error was less than 30 msec on three consecutive trials There were no statistically significant differences between groups in the number of trials needed to reach the performance criterion but (a) participants in Group 2 created fewer sequences corn pared to Group 1 and (b) were more likely to use the same sequence throughout the learning process The number of options for a movement sequence affected the way learners self-controlled their practice but had no effect on the amount of practice to reach criterion performance.

Maternal decision on obtaining prenatal care: a study in Brazil

Objective: to address the social aspects of pregnancy and the views of pregnant women regarding prenatal assistance in Brazil. Design: this qualitative study was focused on describing the Social Representations of prenatal care held by pregnant women. The discourse of the collective subject (DCS) framework was used to analyse the data collected, within the theoretical background of social representations, as proposed and developed by Serge Moscovici. Participants and setting: 21 pregnant women who were users of the publicly funded Brazilian unified health-care system and resided in the area served by its family health programme in a low- to middle-income neighbourhood on the outskirts of Campo Grande, the capital of the state of Mato Grosso do Sul, in southwestern Brazil. Data were collected by conducting in-depth, face-to-face interviews from January to October 2006. Findings: all participants were married. Formal education of the participants was less than five years in four cases, between five and eight years in six cases, and greater than 11 years in 10 cases. Nine participants had informal jobs and earned up to US$ 200 per month, four paricipants had administrative jobs and earned over US$ 500 per month, and eight participants did not work. No specific racial/ethnic background predominated. Lack of adherence to prenatal care allowed for the identification of two DCS themes: `organisation of prenatal care services` and `lifestyle features`. Key conclusions: the respondents were found to have negative feelings about pregnancy which manifest as many fears, including the fear of harming their children`s health, of being punished during labour, and of being reprimanded by health-care professionals for overlooking their prenatal care, in addition to the insecurity felt towards the infant and self. Implications for practice: the findings reveal that communication between pregnant women and healthcare professionals has been ineffective and that prenatal care has not been effective for the group interviewed-features that are likely to be found among other low- to middle-income groups living elsewhere in Brazil. (C) 2009 Elsevier Ltd. All rights reserved.

Financial distress, financial constraint and investment decision: Evidence from Brazil

This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Contribution of soil organic carbon to the ion exchange capacity of tropical soils

Highly weathered soils represent about 3 billion ha of the tropical region. Oxisols represent about 60% of the Brazilian territory (more than 5 million km 2), in areas of great agricultural importance. Soil organic carbon (SOC) can be responsible for more than 80% of the cation exchange capacity (CEC) of highly weathered soils, such as Oxisols and Ultisols. The objective of this study was to estimate the contribution of the SOC to the CEC of Brazilian soils from different orders. Surface samples (0.0 to 0.2 m) of 30 uncultivated soils (13 Oxisols, 6 Ultisols, 5 Alfisols, 3 Entisols, I Histosol, 1 Inceptisol. and I Molisol), under native forests and from reforestation sites from Sao Paulo State, Brazil, were collected in order to obtain a large variation of (electro)chemical, physical, and mineralogical soil attributes. Total content of SOC was quantified by titulometric and colorimetric methods. Effective cation exchange capacity (ECEC) was obtained by two methods: the indirect method-summation-estimated the ECECi from the sum of basic cations (Ca+ Mg+ K+ Na) and exchangeable Al; and the direct ECECd obtained by the compulsive exchange method, using unbuffered BaCl2 solution. The contribution of SOC to the soil CEC was estimated by the Bennema statistical method. The amount of SOC var ied from 6.6 g kg(-1) to 213.4 g kg(-1). while clay contents varied from 40 g kg(-1) to 716 g kg(-1). Soil organic carbon contents were strongly associated to the clay contents, suggesting that clay content was the primary variable in controling the variability of SOC contents in the samples. Cation exchange capacity varied from 7.0 mmol(c) kg(-1) to 137.8 mmol(c) kg(-1) and had a positive Correlation with SOC. The mean contribution (per grain) of the SOC (1.64 mmol(c)) for the soil CEC was more than 44 times higher than the contribution of the clay fraction (0.04 mmol(c),). A regression model that considered the SOC content as the only significant variable explained 60% of the variation in the soil total CEC. The importance of SOC was related to soil pedogenetic process, since its contribution to the soil CEC was more evident in Oxisols with predominance of Fe and Al (oxihydr)oxides in the mineral fraction or in Ultisols, that presented illuviated clay. The influence of SOC in the sign and in the magnitude of the net charge of soils reinforce the importance of agricultural management systems that preserve high levels of SOC, in order to improve their sustainability.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

Determining forest structural attributes using an inverted geometric-optical model in mixed eucalypt forests, Southeast Queensland, Australia

The Montreal Process indicators are intended to provide a common framework for assessing and reviewing progress toward sustainable forest management. The potential of a combined geometrical-optical/spectral mixture analysis model was assessed for mapping the Montreal Process age class and successional age indicators at a regional scale using Landsat Thematic data. The project location is an area of eucalyptus forest in Emu Creek State Forest, Southeast Queensland, Australia. A quantitative model relating the spectral reflectance of a forest to the illumination geometry, slope, and aspect of the terrain surface and the size, shape, and density, and canopy size. Inversion of this model necessitated the use of spectral mixture analysis to recover subpixel information on the fractional extent of ground scene elements (such as sunlit canopy, shaded canopy, sunlit background, and shaded background). Results obtained fron a sensitivity analysis allowed improved allocation of resources to maximize the predictive accuracy of the model. It was found that modeled estimates of crown cover projection, canopy size, and tree densities had significant agreement with field and air photo-interpreted estimates. However, the accuracy of the successional stage classification was limited. The results obtained highlight the potential for future integration of high and moderate spatial resolution-imaging sensors for monitoring forest structure and condition. (C) Elsevier Science Inc., 2000.

Biodiversity: bridging the gap between condition and conservation

The aim of this study is to create a two-tiered assessment combining restoration and conservation, both needed for biodiversity management. The first tier of this approach assesses the condition of a site using a standard bioassessment method, AUSRIVAS, to determine whether significant loss of biodiversity has occurred because of human activity. The second tier assesses the conservation value of sites that were determined to be unimpacted in the first step against a reference database. This ensures maximum complementarity without having to set a priori target areas. Using the reference database, we assign site-specific and comparable coefficients for both restoration (Observed/Expected taxa with > 50% probability of occurrence) and conservation values (O/E taxa with < 50%, rare taxa). In a trial on 75 sites on rivers around Sydney, NSW, Australia we were able to identify three regions: (1) an area that may need restoration; (2) an area that had a high conservation value and; (3) a region that was identified as having significant biodiversity loss but with high potential to respond to rehabilitation and become a biodiversity hotspot. These examples highlight the use of the new framework as a comprehensive system for biodiversity assessment.

Screening for leprosy in immigrants: A decision analysis model

Almost all leprosy cases reported in industrialized countries occur amongst immigrants or refugees from developing countries where leprosy continues to be an important health issue. Screening for leprosy is an important question for governments in countries with immigration and refugee programmes. A decision analysis framework is used to evaluate leprosy screening. The analysis uses a set of criteria and parameters regarding leprosy screening, and available data to estimate the number of cases which would be detected by a leprosy screening programme of immigrants from countries with different leprosy prevalences, compared with a policy of waiting for immigrants who develop symptomatic clinical diseases to present for health care. In a cohort of 100,000 immigrants from high leprosy prevalence regions (3.6/10,000), screening would detect 32 of the 42 cases which would arise in the destination country over the 14 years after migration; from medium prevalence areas (0.7/10,000) 6.3 of the total 8.1 cases would be detected, and from low prevalence regions (0.2/10,600) 1.8 of 2.3 cases. Using Australian data, the migrant mix would produce 74 leprosy cases from 10 years intake; screening would detect 54, and 19 would be diagnosed subsequently after migration. Screening would only produce significant case-yield amongst immigrants from regions or social groups with high leprosy prevalence. Since the number of immigrants to Australia from countries of higher endemnicity is not large routine leprosy screening would have a small impact on case incidence.