Alfredo José Barreto Luiz

Statistical Inference on Associated Fertility Life Table Parameters Using Jackknife Technique: Computational Aspects

Abstract Knowledge of population growth potential is crucial for studying population dynamics and for establishing management tactics for pest control. Estimation of population growth can be achieved with fertility life tables because they synthesize data on reproduction and mortality of a population. The five main parameters associated with a fertility life table are as follows: (1) the net reproductive rate (Ro), (2) the intrinsic rate of increase (rm), 3) the mean generation time (T), (4) the doubling time (Dt), and (5) the finite rate of increase (λ). Jackknife and bootstrap techniques are used to calculate the variance of the rm estimate, which can be extended to the other parameters of life tables. Those methods are computer-intensive, their application requires the development of efficient algorithms, and their implementation is based on a programming language that encompasses quickness and reliability. The objectives of this article are to discuss statistical and computational aspects related to estimation of life table parameters and to present a SAS program that uses jackknife to estimate parameters for fertility life tables. The SAS program presented here allows the calculation of confidence intervals for all estimated parameters, as well as provides one-sided and two-sided t-tests to perform pairwise or multiple comparison between groups, with their respective P values.

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in Sao Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Determinação do período crítico para prevenção da interferência de plantas daninhas na cultura de soja: uso do modelo "broken-stick"

Visando a fornecer subsidios para programas de manejo de plantas daninhas em culturas agricolas, foi conduzido um experimento de campo em Botucatu, SP. O objetivo foi determinar, atraves do procedimento estatistico de analise de regressao, o periodo critico para prevencao da interferencia (PCPI) de plantas daninhas de folha larga na produtividade da cultura de soja. Foi utilizado o delineamento experimental em blocos casualizados, com 3 repeticoes. A cultura foi mantida na presenca das plantas daninhas de folha larga por diferentes periodos. O periodo critico determinado foi de 21 a 30 dias apos a emergencia da cultura, segundo ajuste dos dados de produtividade ao modelo Broken-Stick. No entanto, o periodo critico determinado indica que o controle das plantas daninhas pode ser realizado, uma unica vez, atraves do uso de metodo momentâneo, sem efeito residual.

Antioxidant defenses and biochemical changes in the neotropical fish pacu, Piaractus mesopotamicus: responses to single and combined copper and hypercarbia exposure.

This study investigated the potentially detrimental effects of copper and elevated aquatic CO(2) (hypercarbia), alone or in combination, on pacu, Piaractus mesopotamicus. Fish were exposed for 48 h to control (no copper addition in normocarbia), to 400 μg Cu(2+)L(-1), to hypercarbic (1% CO(2); PCO(2) = 6.9 mm Hg) water and to 400 μg Cu(2+)L(-1) + hypercarbia. In liver the single factors caused an increase in lipid hydroperoxide concentration that was not observed when the factors were combined. Copper exposure elicited increased hepatic superoxide dismutase activity, irrespective of aquatic CO(2) level. On the other hand, the effects of copper on hepatic glutathione peroxidase activity were dependent on water CO(2) levels. The two stressors combined did not affect hepatic catalase activity. Hypercarbic water caused a decline in plasma glucose concentration, but this was not observed when hypercarbia was combined with copper exposure. Copper caused a decrease in branchial Na(+)/K(+)-ATPase activity that was independent of water CO(2) level. Copper caused an increase in branchial metallothionein concentration that was independent of water CO(2) level. Thus, branchial metallothionein and Na(+)/K(+)-ATPase were effective biomarkers of copper exposure that were not affected by water CO(2) level.

On the Effectiveness of Candlestick Chart Analysis for the Brazilian Stock Market

Abstract Several techniques have been developed in pursuit of understanding the behavior of the financial market, in an attempt to predict the asset pricing behavior. The candlestick chart created in the 18th century is one of these techniques. In 2006, Greg Morris conducted a study on the effectiveness of this technique for the U.S. capital market. However, no similar work was done for the Brazilian market. In this paper, the behavior of part of the Brazilian capital market was studied using sixteen candlestick patterns. We considered the data series of ten stocks between 2005 and 2009, totaling approximately 40% of Ibovespa (Sao Paulo Stock Exchange Index) turnover. The frequency of confirmation of each pattern was measured along seven exchange sessions after occurrence of such pattern, and results were compared to those presented by Morris. Additionally, adjustments of the observed proportions of hits were tested for their statistical significance. Results found in the frequential analysis showed a discrepancy in relation to Morriss study. Likewise, in statistical analysis few patterns have confirmed the behavior expected of them. In at least one case the trend expressed by data, although significant, was contrary to the original interpretation of the pattern. Therefore, direct application of patterns developed for other markets, times or actions is not recommended. Such results do not allow for an affirmation that candlestick patterns have the power to predict future behavior of stocks traded in the Ibovespa stock market. However, we found statistically significant evidence of the predictive ability of some patterns, which may indicate that the technique must be adapted to the market where it is intended to be used. The main contributions of this paper are a partial replication of Morris’ study for a set of stocks traded in the Brazilian market, and a statistical analysis of the effectiveness of candlestick patterns as predictors of the behavior of those stocks.Click here and insert your abstract text.

Avaliação de áreas potenciais ao cultivo de biomassa para produção de energia e uma contribuição de sensoriamento remoto e sistemas de informações geográficas

A humanidade sempre utilizou a biomassa para fins energeticos, porem com o aumento da demanda, ameacas a seguranca energetica e os danos para o ambiente e para a saude decorrentes da utilizacao dos combustiveis fosseis, a importância desta fonte renovavel vem sendo resgatada. Neste cenario, o planejamento agroenergetico passa a ser de grande importância para paises tropicais como o Brasil, pois permite viabilizar a producao de biomassa para energia onde esta e requerida, de forma sustentavel, sem deixar de considerar os aspectos sociais e ambientais. Desta forma, o presente artigo tem por objetivo apresentar importantes questoes relacionadas ao planejamento energetico regional, como forma de contribuir para solucoes e politicas publicas relacionadas a producao de energia de biomassa. Ademais, e apresentada a contribuicao das tecnicas de Sensoriamento Remoto e Sistemas de Informacoes Geograficas para avaliacoes de areas potenciais ao cultivo de culturas bioenergeticas, alem de um modelo conceitual que demonstra como essas tecnicas podem constituir-se em ferramentas de apoio a tomada de decisao estrategica nesta area de bioenergia. Adicionalmente, algumas dificuldades e limitacoes para o planejamento territorial e agroenergetico tambem foram relacionadas.

Computational methods for agricultural research : advances and applications

This book brings computing solutions to ancient practices and modern concerns, sowing the seeds for a sustainable, constant food supply, utilizing cutting-edge computational techniques--Provided by publisher.

Diagnóstico rápido e dialogado em estudos de desenvolvimento rural sustentável

The main objective of this research was to test the hypothesis of whether family agriculture, as defined herewith, would indeed be predominant in the Taquara Branca watershed, in the State of Sao Paulo, Brazil, site of a larger sustainable development assessment study. Data on 33 of 106 landholdings in the watershed were obtained by directly applied cross-section questionnaires. The variables for multivariate analysis were first selected through a unidimensional analysis, when some questions were selected with basis on answer variability. Multivariate statistics was used (PROC CLUSTER, SAS) to define and analyze the groupings. Twenty two respondents (out of the 33) were involved in agricultural activities, comprising, then, the universe of the study. Cluster analysis allowed the identification of three natural groups among the 22 landholdings. It was possible to classify each group, according to their characteristics, as follows: group 1 ¾ twelve typical family landholdings; group 2 ¾ six non-family, small landholdings; and group 3 - four non-family, large landholdings. Although not predominant in terms of area, typical family agriculture was found to be the more frequent (36%) in the watershed.

Inference on Arthropod Demographic Parameters: Computational Advances Using R

ABSTRACT We developed a computer program for life table analysis using the open source, free software programming environment R. It is useful to quantify chronic nonlethal effects of treatments on arthropod populations by summarizing information on their survival and fertility in key population parameters referred to as fertility life table parameters. Statistical inference on fertility life table parameters is not trivial because it requires the use of computationally intensive methods for variance estimation. Our codes present some advantages with respect to a previous program developed in Statistical Analysis System. Additional multiple comparison tests were incorporated for the analysis of qualitative factors; a module for regression analysis was implemented, thus, allowing analysis of quantitative factors such as temperature or agrochemical doses; availability is granted for users, once it was developed using an open source, free software programming environment. To illustrate the descriptive and inferential analysis implemented in lifetable.R, we present and discuss two examples: 1) a study quantifying the influence of the proteinase inhibitor berenil on the eucalyptus defoliator Thyrinteina arnobia (Stoll) and 2) a study investigating the influence of temperature on demographic parameters of a predaceous ladybird, Hippodamia variegata (Goeze).