Potential impact of the global climate changes on the spatial distribution of areas of risk for the occurrence of eucalyptus rust in Brazil

Rust, caused by Puccinia psidii, is one of the most important diseases affecting eucalyptus in Brazil. This pathogen causes disease in mini-clonal garden and in young plants in the field, especially in leaves and juvenile shoots. Favorable climate conditions for infection by this pathogen in eucalyptus include temperature between 18 and 25 ºC, together with at least 6-hour leaf wetness periods, for 5 to 7 consecutive days. Considering the interaction between the environment and the pathogen, this study aimed to evaluate the potential impact of global climate changes on the spatial distribution of areas of risk for the occurrence of eucalyptus rust in Brazil. Thus, monthly maps of the areas of risk for the occurrence of this disease were elaborated, considering the current climate conditions, based on a historic series between 1961 and 1990, and the future scenarios A2 and B2, predicted by IPCC. The climate conditions were classified into three categories, according to the potential risk for the disease occurrence, considering temperature (T) and air relative humidity (RH): i) high risk (18 < T < 25 ºC and RH > 90%); ii) medium risk (18 < T < 25 ºC and RH < 90%; T< 18 or T > 25 ºC and RH > 90%); and iii) low risk (T < 18 or T > 25 ºC and RH < 90%). Data about the future climate scenarios were supplied by GCM Change Fields. In this study, the simulation model Hadley Centers for Climate Prediction and Research (HadCm3) was adopted, using the software Idrisi 32. The obtained results led to the conclusion that there will be a reduction in the area favorable to eucalyptus rust occurrence, and such a reduction will be gradual for the decades of 2020, 2050 and 2080 but more marked in scenario A2 than in B2. However, it is important to point out that extensive areas will still be favorable to the disease development, especially in the coldest months of the year, i.e., June and July. Therefore, the zoning of areas and periods of higher occurrence risk, considering the global climate changes, becomes important knowledge for the elaboration of predicting models and an alert for the integrated management of this disease.

Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.

POPULATION STRUCTURE AND SPATIAL DISTRIBUTION OF Bauhinia cheilantha (Bong.) Steud. IN TWO FRAGMENTS AT DIFFERENT REGENERATION STAGES IN THE CAATINGA, IN SERGIPE, BRAZIL

ABSTRACTThis study aimed to analyze the vertical and diameter structure and the spatial distribution pattern of Bauhinia cheilantha in two Caatinga fragments in Sergipe, Brazil, at different regeneration stages. Thirty plots were demarcated in area I (Canindé de São Francisco and Poço Redondo), which has vegetation regeneration, and 25 plots in area II (Porto da Folha) with preserved vegetation, both having 400 m2. All B. cheilanthaindividuals had their height and circumference (circumference at breast height > 6 cm) measured. Possible differences in height and diameter at breast height were tested in the two populations by using Student’s T-test. The distribution pattern of species was calculated through Payandeh’s index. We sampled 154 B. cheilantha individuals, equivalent to 33.3% of the plots in area I and in 1,027 individuals in area II, totaling 100% frequency. Height and the diameter of the two populations were statistically different, where AI achieved all values lower than AII. The spatial distribution pattern of B. cheilantha found in both areas was aggregate, with values of 11.85 and 9.00, respectively. Thus, it became clear that the population in AII is at a more advanced successional status than AI, due to its longer conservation time.

Spatial variability of leaf wetness duration in a 'Niagara Rosada' vineyard

Despite considerable efforts to develop accurate electronic sensors to measure leaf wetness duration (LWD), little attention has been given to studies about how is LWD variability in different positions of the crop canopy. In order to evaluate the influence of 'Niagara Rosada' (Vitis labrusca) grapevine structure on the spatial variability of LWD, the objective of this study was to determine the canopy position of the ‘Niagara Rosada’ table grape with longer LWD and its correlation with measured standard LWD over turfgrass. LWD was measured in four different canopy positions of the vineyard (sensors deployed at 45º with the horizontal): at the top of the plants, with sensors facing southwest and northeast (Top-SW and Top-NE), and at the grape bunches height, with sensors facing southwest and northeast (Bottom-SW and Bottom-NE). No significant difference was observed between the top (1.6 m) and the bottom (1.0 m) of the canopy and also between the southwest and northeast face of the plants. The relationship between standard LWD over turfgrass and crop LWD in different positions of the grape canopy showed a define correlation, with R² ranging from 0.86 to 0.89 for all period, from 0.72 to 0.77 for days without rain, and from 0.89 to 0.91 for days with rain.

In search of Lost Childhood

The article is located at the Daily Sun's editorial section's subsection "Post-Log."

Search Engine Optimization Methods & Search Engine Indexing for CMS Applications

Search engine optimization & marketing is a set of processes widely used on websites to improve search engine rankings which generate quality web traffic and increase ROI. Content is the most important part of any website. CMS web development is now become very essential for most of organizations and online businesses to develop their online system and websites. Every online business using a CMS wants to get users (customers) to make profit and ROI. This thesis comprises a brief study of existing SEO methods, tools and techniques and how they can be implemented to optimize a content base website. In results, the study provides recommendations about how to use SEO methods; tools and techniques to optimize CMS based websites on major search engines. This study compares popular CMS systems like Drupal, WordPress and Joomla SEO features and how implementing SEO can be improved on these CMS systems. Having knowledge of search engine indexing and search engine working is essential for a successful SEO campaign. This work is a complete guideline for web developers or SEO experts who want to optimize a CMS based website on all major search engines.

Terrain forms and spatial variability of soil properties in an area cultivatedwith citrus

The technique of precision agriculture and soil-landscape allows delimiting areas for localized management, allowing a localized application of agricultural inputs and thereby may contribute to preservation of natural resources. Therefore, the objective of this work was to characterize the spatial variability of chemical properties and clay content in the context of soil-landscape relationship in a Latosol (Oxisol) under cultivation of citrus. Soil samples were collected at a depth of 0.0-0.2 m in an area of 83.5 ha planted with citrus, as a 50-m intervals grid, with 129 points in concave terrain and 206 points in flat terrain, totaling 335 points. Values for the variables that express the chemical characteristics and clay content of soil properties were analyzed with descriptive statistics and geostatistical modeling of semivariograms for making maps of kriging. The values of range and kriging maps indicated higher variability in the shape of concave topography (top segment) compared with the shape of flat topography (slope and hillside segments below). The identification of different forms of terrain proved to be efficient in understanding the spatial variability of chemical properties and clay content of soil under cultivation of citrus.

Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

GPS receivers for georeferencing of spatial variability of soil attributes

The characterization of the spatial variability of soil attributes is essential to support agricultural practices in a sustainable manner. The use of geostatistics to characterize spatial variability of these attributes, such as soil resistance to penetration (RP) and gravimetric soil moisture (GM) is now usual practice in precision agriculture. The result of geostatistical analysis is dependent on the sample density and other factors according to the georeferencing methodology used. Thus, this study aimed to compare two methods of georeferencing to characterize the spatial variability of RP and GM as well as the spatial correlation of these variables. Sampling grid of 60 points spaced 20 m was used. For RP measurements, an electronic penetrometer was used and to determine the GM, a Dutch auger (0.0-0.1 m depth) was used. The samples were georeferenced using a GPS navigation receiver, Simple Point Positioning (SPP) with navigation GPS receiver, and Semi-Kinematic Relative Positioning (SKRP) with an L1 geodetic GPS receiver. The results indicated that the georeferencing conducted by PPS did not affect the characterization of spatial variability of RP or GM, neither the spatial structure relationship of these attributes.

Spatial variability of soil physical properties in two management systems in sugarcane crop

The aim of this study was to characterize the spatial variability of soil bulk density (Bd), soil moisture content (θ) and total porosity (Tp) in two management systems of sugarcane harvesting, with or without burning, in a Haplustox soil, in the 0-0.20 m layer. The study area is located in Rio Brilhante, state of Mato Grosso do Sul, Brazil, in Eldorado Sugar Mill. The plots have presented 180 m length, and 145.6 m width, totaling 90 points distributed in the form of a grid of nine rows by ten columns, with points spaced 20 m from its neighbor. Soil samples were collected at 0-0.20 m layer in 2007/2008 and 2008/2009 crops. The harvest with burning system had a higher density compared to mechanized harvest, in the two study periods. The moisture content as well as the porosity increased proportionally with the decrease of the density of the harvest burning system compared to the mechanized.

Spatial variability of the rainfall erosive potential in the State of Mato Grosso do Sul, Brazil

Information about rainfall erosivity is important during soil and water conservation planning. Thus, the spatial variability of rainfall erosivity of the state Mato Grosso do Sul was analyzed using ordinary kriging interpolation. For this, three pluviograph stations were used to obtain the regression equations between the erosivity index and the rainfall coefficient EI30. The equations obtained were applied to 109 pluviometric stations, resulting in EI30 values. These values were analyzed from geostatistical technique, which can be divided into: descriptive statistics, adjust to semivariogram, cross-validation process and implementation of ordinary kriging to generate the erosivity map.Highest erosivity values were found in central and northeast regions of the State, while the lowest values were observed in the southern region. In addition, high annual precipitation values not necessarily produce higher erosivity values.

Spatial variability of noise level in agricultural machines

The knowledge of the spatial variability of noise levels and the build of kriging maps can help the evaluation of the salubrity of environments occupied by agricultural workers. Therefore, the objective of this research was to characterize the spatial variability of the noise level generated by four agricultural machines, using geostatistics, and to verify if the values are within the limits of human comfort. The evaluated machines were: harvester, chainsaw, brushcutter and tractor. The data were collected at the height of the operator's ear and at different distances. Through the results, it was possible to verify that the use of geostatistics, by kriging technique, made it possible to define areas with different levels for the data collected. With exception of the harvester, all of machines presented noise levels above than 85 dB (A) near to the operator, demanding the use of hearing protection.

Spatial variability of yield and other parameters associated with pear trees

In the last few years, precision agriculture has become commonly used with many crops, particularly cereals, and there is also interest in precision horticulture. Pear is a seasonal fruit and well appreciated by Brazilian people, although it is mostly imported. Brazilian farmers are nowadays trying to increase pear production. Thus, this research aimed at mapping the yield of pear trees in order to study the spatial variability of yield as well as its comparison with spatial variability of soil and plant attributes. The experimental field had 146 pear trees, variety 'Pêra d'água', distributed on a 1.24 ha. Four harvests were performed according to the fruit ripening and from each tree; only the ripe fruits were harvested. In each harvest, all the fruits were weighed and the total yield was obtained based on the sum of each harvest. The soil attributes analyzed were P, K, Ca, Mg, pH in CaCl2, C, Cu, Zn, Fe, Mn and base saturation, and the plant attributes were fruit length, diameter and yield. Yield had low correlation with soil and plant attributes. An index of spatial variability was suggested in this study and helped in classifying levels of spatial dependence of the various soil and plant attributes: very low (fruit length); low (P, fruit diameter), medium (Mg, pH, Cu, Zn, Fe), high (Ca, K, base saturation and yield), and very high (Mn and C).

Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.