Seroprevalence and sociocultural conditionants of Chagas disease in school aged children of marginal zones of Asunción

Chagas disease is becoming a public health problem in Latin America due to the wide distribution, the high prevalence, the magnitude of the damage caused and the difficulties to control it. In Paraguay, the disease is mainly distributed in the departments of Paraguari, Cordillera and Central. Prevalence in marginal zones, where migrations from rural populations and endemic areas make possible the urbanization of the disease, has no been studied yet. This is a descriptive study with a cross-sectional sampling and a probabilistic system recruitment carried out in school aged children from marginal zones of Asuncion to determine the prevalence of Chagas' disease. Serological methods, parasite isolation and questionnaires were used to achieve the goals. Nine hundred and fifty three children were studied to determine the prevalence of Chagas' disease in marginal zones which was 1.4%.

Seroprevalence of human Trypanosoma cruzi infection in diferent geografic zones of Chiapas, Mexico

A serologic survey was carried out in four different geographic zones of Chiapas, Mexico. A total of 1,333 samples were collected from residents of thirteen communities located on the Coast, Central Mountain, Lacandon Forest and a zone called Mesochiapas. One hundred and fifty one seropositive individuals (11.3%) were identified. Human Trypanosoma cruzi infection was influenced by geography. In the Lacandon Forest and Central Mountains there was a higher seroprevalence 32.1 and 13.8% respectively, than on the coast (1.2%). In Mesochiapas there were no seropositive individuals among the 137 persons tested. An active transmission is probably continuing because seropositive cases (13.8%) were detected in children under 10 years of age. The vector recognized on the Coast was Triatoma dimidiata while in the Lacandon Forest it was Rhodnius prolixus.

Space-time variation of the relative abundance of Limnoperna fortunei in deep zones of São Gonçalo Channel, Rio Grande do Sul, Brazil

This work describes the spatial-temporal variation of the relative abundance and size of Limnoperna fortunei (Dunker, 1857) collected in São Gonçalo Channel through bottom trawl with a 0.5 cm mesh, at depths between 3 and 6 m. The estimative of mean relative abundance (CPUE) ranged from 2,425.3 individuals per drag (ind./drag) in the spring to 21,715.0 ind./drag in the fall, with an average of 9,515.3 ind./drag throughout the year. The estimated mean density of L. fortunei for the deep region of São Gonçalo Channel ranged from 1.2 to 10.3 ind./m², and it was recorded a maximum density of 84.9 ind./m² in the fall of 2008. The method of sampling using bottom trawl enabled the capture of L. fortunei under the soft muddy bottom of the channel, in different sizes ranging from 0.4 to 3.2 cm. This shows that the structure of the L. fortunei adult population under the bottom of the São Gonçalo Channel is composed mostly of small individuals (<1.4 cm), which represent up to 74% of the population collected.

Risk Zones of Human Leishmaniases in the Western Mediterranean Basin: Correlations between Vector Sand Flies, Bioclimatology and Phytosociology

Correspondence analysis was applied to sand fly sampling in 865 stations from the Western Mediterranean basin. The position of each of 24 species was determined with respect to the bioclimatic belts. Thus, the multidimensional analyses manifest clear correlations between bioclimatic belts and their expression in the area, the phytosociological groupings, and vector species of visceral and cutaneous leishmaniases. The transfer of these data to usual maps allows to delimit the geographical distribution of these diseases in the Western Mediterranean basin and contributes to the determination, in a rational manner, of the high risk zones.

Determination of management zones from normalized and standardized equivalent produtivity maps in the soybean culture

Through the site-specific management, the precision agriculture brings new techniques for the agricultural sector, as well as a larger detailing of the used methods and increase of the global efficiency of the system. The objective of this work was to analyze two techniques for definition of management zones using soybean yield maps, in a productive area handled with localized fertilization and other with conventional fertilization. The sampling area has 1.74 ha, with 128 plots with site-specific fertilization and 128 plots with conventional fertilization. The productivity data were normalized by two techniques (normalized and standardized equivalent productivity), being later classified in management zones. It can be concluded that the two methods of management zones definition had revealed to be efficient, presenting similarities in the data disposal. Due to the fact that the equivalent standardized productivity uses standard score, it contemplates a better statistics justification.

Management zones in agriculture acording to the soil and landscape variables

The study of spatial variability of soil and plants attributes, or precision agriculture, a technique that aims the rational use of natural resources, is expanding commercially in Brazil. Nevertheless, there is a lack of mathematical analysis that supports the correlation of these independent variables and their interactions with the productivity, identifying scientific standards technologically applicable. The aim of this study was to identify patterns of soil variability according to the eleven physical and seven chemical indicators in an agricultural area. It was used two multivariate techniques: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). According to the HCA, the area was divided into five management zones: zone 1 with 2.87ha, zone 2 with 0.8ha, zone 3 with 1.84ha, zone 4 with 1.33ha and zone 5 with 2.76ha. By the PCA, it was identified the most important variables within each zone: V% for the zone 1, CTC in the zone 2, levels of H+Al in the zone 4 and sand content and altitude in the zone 5. The zone 3 was classified as an intermediate zone with characteristics of all others. According to the results it is concluded that it is possible to separate into groups (management zones) samples with the same patterns of variability by the multivariate statistical techniques.

Management zones definition using soil chemical and physical attributes in a soybean area

Several equipments and methodologies have been developed to make available precision agriculture, especially considering the high cost of its implantation and sampling. An interesting possibility is to define management zones aim at dividing producing areas in smaller management zones that could be treated differently, serving as a source of recommendation and analysis. Thus, this trial used physical and chemical properties of soil and yield aiming at the generation of management zones in order to identify whether they can be used as recommendation and analysis. Management zones were generated by the Fuzzy C-Means algorithm and their evaluation was performed by calculating the reduction of variance and performing means tests. The division of the area into two management zones was considered appropriate for the present distinct averages of most soil properties and yield. The used methodology allowed the generation of management zones that can serve as source of recommendation and soil analysis; despite the relative efficiency has shown a reduced variance for all attributes in divisions in the three sub-regions, the ANOVA did not show significative differences among the management zones.

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

Redundant variables and the quality of management zones

ABSTRACT Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran’s bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.