Detection of soybean planted areas through orbital images based on culture spectral dynamics

The soybean is important to the economy of Brazil, so the estimation of the planted area and the production with higher antecedence and reliability becomes essential. Techniques related to Remote Sensing may help to obtain this information at lower cost and less subjectivity in relation to traditional surveys. The aim of this study is to estimate the planted area with soybean culture in the crop of 2008/2009 in cities in the west of the state of Paraná, in Brazil, based on the spectral dynamics of the culture and through the use of the specific system of analysis for images of Landsat 5/TM satellite. The obtained results were satisfactory, because the classification supervised by Maximum Verisimilitude - MaxVer along with the techniques of the specific system of analysis for satellite images has allowed an estimate of soybean planted area (soybean mask), obtaining values ​​of the metrics of Global Accuracy with an average of 79.05% and Kappa Index over 63.50% in all cities. The monitoring of a reference area was of great importance for determining the vegetative phase in which the culture is more different from the other targets, facilitating the choice of training samples (ROIs) and avoiding misclassifications.

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.

Hypertension and clustering of cardiovascular risk factors in a community in Southeast Brazil: the Bambuí Health and Ageing Study

OBJECTIVE - A population-based prospective study was analysed to: a) determine the prevalence of hypertension; b) investigate the clustering of other cardiovascular risk factors and c) verify whether older differed from younger adults in the pattern of clustering. METHODS - The data comprised a representative sample of the population of Bambuí, Brazil. Multiple logistic regression was used to investigate the independent association between hypertension and selected factors. RESULTS - A total of 820 younger adults (82.5%) and 1494 older adults (85.9%) participated in this study. The overall prevalence of hypertension was 24.8% (SE=1.4 %), being higher in women (26.9±1.5%) than in men (22.0± 1.7%) (p=0.033). Hypertension was positively and significantly associated with physical inactivity, overweight, hypercholesterolemia hyperglycemia and hypertriglyceridemia. The coexistence of hypertension with 4 or more of these risk factors occurred 6 times more than expected by chance, after adjusting for age and sex (OR=6.3; 95%CI: 3.4-11.9). The pattern of risk factor clustering in hypertensive individuals differed with age. CONCLUSION - Our results reinforce the need to increase detection and treatment of hypertension and to approach patients' global risk profiles.

Household-based malaria control in a highly endemic area of Africa (Tanzania): determinants of transmission and disease and indicators for monitoring - Kilombero Malaria Project

The Kilombero Malaria Project (KMP) attemps to define opperationally useful indicators of levels of transmission and disease and health system relevant monitoring indicators to evaluate the impact of disease control at the community or health facility level. The KMP is longitudinal community based study (N = 1024) in rural Southern Tanzania, investigating risk factors for malarial morbidity and developing household based malaria control strategies. Biweekly morbidity and bimonthly serological, parasitological and drug consumption surveys are carried out in all study households. Mosquito densities are measured biweekly in 50 sentinel houses by timed light traps. Determinants of transmission and indicators of exposure were not strongly aggregated within households. Subjective morbidity (recalled fever), objective morbidity (elevated body temperature and high parasitaemia) and chloroquine consumption were strongly aggregated within a few households. Nested analysis of anti-NANP40 antibody suggest that only approximately 30% of the titer variance can explained by household clustering and that the largest proportion of antibody titer variability must be explained by non-measured behavioral determinants relating to an individual's level of exposure within a household. Indicators for evaluation and monitoring and outcome measures are described within the context of health service management to describe control measure output in terms of community effectiveness.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

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.

Phytogeographical origin of Madeiran common beans based on phaseolin patterns

The objective of this work was to determine the geographic origin of the Madeiran common bean (Phaseolus vulgaris) gene pool. Phaseolin patterns of 50 accessions representing the diversity of common bean collected in Madeira, Portugal, and conserved in the ISOPlexis Germplasm Bank, were analysed using the Experion automated electrophoresis system, based on lab-on-a-chip technology. Five common bean standard varieties with typical phaseolin patterns were used to determine the phytogeographical origin of the Madeiran common bean accessions. Ninety two percent of the accessions exhibited a phaseolin pattern consistent with the one of common bean types belonging to the Andean gene pool, while the origin of the remaining 8% of the accessions was indistinguishable. The application of a similarity coefficient of 85%, based on Pearson correlations, increases the number of accessions with uncertain pattern. The analytical approach used permitted the determination of the origin of the common bean gene pool, which is Andean in 98% of the cases, and clustering of the observed variability among the Madeiran common beans.

Identification of acetates in elasmopalpulus lignosellus pheromone glands using a newly created mass spectral database and kóvats retention indices

Based on a specially created mass spectral database utilizing 23 tetradecenyl and 22 hexadecenyl acetate standards along with Kóvats retention indices obtained on a very polar stationary phase [poly (biscyanopropyl siloxane)] (SP 2340), (Z)-9-hexadecenyl acetate, (Z)-11-hexadecenyl acetate and (E)-8-hexadecenyl acetate were identified in active pheromone extracts of Elasmopalpus lignosellus. This identification was more efficient than our previous study using gas chromatography/mass spectrometry with a dimethyl disulfide derivative where we could only identify the first two acetates. The acetate composition of the pheromone gland differed from region to region in Brazil and from that from the Tifton (GA, USA) population, suggesting polymorphism or a different sub-species.

Influence of data acquisition geometry on soybean spectral response simulated by the prosail model

View angle and directional effects significantly affect reflectance and vegetation indices, especially when daily images collected by large field-of-view (FOV) sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) are used. In this study, the PROSAIL radiative transfer model was chosen to evaluate the impact of the geometry of data acquisition on soybean reflectance and two vegetation indices (Normalized Difference Vegetation Index - NDVI and Enhanced Vegetation Index -EVI) by varying biochemical and biophysical parameters of the crop. Input values for PROSAIL simulation were based on the literature and were adjusted by the comparison between simulated and real satellite soybean spectra acquired by the MODIS/Terra and hyperspectral Hyperion/Earth Observing-One (EO-1). Results showed that the influence of the view angle and view direction on reflectance was stronger with decreasing leaf area index (LAI) and chlorophyll concentration. Because of the greater dependence on the near-infrared reflectance, the EVI was much more sensitive to viewing geometry than NDVI presenting larger values in the backscattering direction. The contrary was observed for NDVI in the forward scattering direction. In relation to the LAI, NDVI was much more isotropic for closed soybean canopies than for incomplete canopies and a contrary behavior was verified for EVI.

Clustering of psychiatric and somatic illnesses in the general population: multimorbidity and socioeconomic correlates

The distribution of psychiatric disorders and of chronic medical illnesses was studied in a population-based sample to determine whether these conditions co-occur in the same individual. A representative sample (N = 1464) of adults living in households was assessed by the Composite International Diagnostic Interview, version 1.1, as part of the São Paulo Epidemiological Catchment Area Study. The association of sociodemographic variables and psychological symptoms regarding medical illness multimorbidity (8 lifetime somatic conditions) and psychiatric multimorbidity (15 lifetime psychiatric disorders) was determined by negative binomial regression. A total of 1785 chronic medical conditions and 1163 psychiatric conditions were detected in the population concentrated in 34.1 and 20% of respondents, respectively. Subjects reporting more psychiatric disorders had more medical illnesses. Characteristics such as age range (35-59 years, risk ratio (RR) = 1.3, and more than 60 years, RR = 1.7), being separated (RR = 1.2), being a student (protective effect, RR = 0.7), being of low educational level (RR = 1.2) and being psychologically distressed (RR = 1.1) were determinants of medical conditions. Age (35-59 years, RR = 1.2, and more than 60 years, RR = 0.5), being retired (RR = 2.5), and being psychologically distressed (females, RR = 1.5, and males, RR = 1.4) were determinants of psychiatric disorders. In conclusion, psychological distress and some sociodemographic features such as age, marital status, occupational status, educational level, and gender are associated with psychiatric and medical multimorbidity. The distribution of both types of morbidity suggests the need of integrating mental health into general clinical settings.