Effect of population densities of Heterodera glycines race 3 on leaf area, photosynthesis and yield of soybean

The effect of Heterodera glycines on photosynthesis, leaf area and yield of soybean (Glycine max) was studied in two experiments carried out under greenhouse condition. Soybean seeds were sown in 1.5 l (Experiment 1) or 5.0 l (Experiment 2) clay pots filled with a mixture of field soil + sand (1:1) sterilized with methyl bromide. Eight days after sowing, seedlings were thinned to one per pot, and one day later inoculated with 0; 1.200; 3.600; 10.800; 32.400 or 97.200 J2 juveniles of H. glycines. Experiment 1 was carried out during the first 45 days of the inoculation while Experiment 2 was conducted during the whole cycle of the crop. Measurements of photosynthetic rate, stomatic conductance, chlorophyll fluorescence, leaf color, leaf area, and chlorophyll leaf content were taken at ten-day intervals throughout the experiments. Data on fresh root weight, top dry weight, grain yield, number of eggs/gram of roots, and nematode reproduction factor were obtained at the end of the trials. Each treatment was replicated ten times. There was a marked reduction in both photosynthetic rate and chlorophyll content, as well as an evident yellowing of the leaves of the infected plants. Even at the lowest Pi, the effects of H. glycines on the top dry weight or grain yield were quite severe. Despite the parasitism, soybean yield was highly correlated with the integrated leaf area and, accordingly, the use of this parameter was suggested for the design of potential damage prediction models that include physiological aspects of nematode-diseased plants.

Critical-point yield model to estimate yield damage caused by Cercospora zea-maydis in corn

A model to estimate damage caused by gray leaf spot of corn (Cercospora zea-maydis) was developed from experimental field data gathered during the summer seasons of 2000/01 and during the second crop season [January-seedtime] of 2001, in the southwest of Goiás state. Three corn hybrids were grown over two seasons and on two sites, resulting in 12 experimental plots. A disease intensity gradient (lesions per leaf) was generated through application, three times over the season, of five different doses of the fungicide propiconazol. From tasseling onward, disease intensity on the ear leaf (El), and El - 1, El - 2, El + 1, and El + 2, was evaluated weekly. A manual harvest at the physiological ripening stage was followed by grain drying and cleaning. Finally, grain yield in kg.ha-1 was estimated. Regression analysis, performed between grain yield and all combinations of the number of lesions on each leaf type, generated thirty linear equations representing the damage function. To estimate losses caused by different disease intensities at different corn growth stages, these models should first be validated. Damage coefficients may be used in determining the economic damage threshold.

Consumer Search Behavior in High Involvement Purchases

The aim of this study is to investigate the consumer search behavior in high involvement purchases. The results of this research provide the descriptive analysis of the information search phase which is a part of the decision-making process. The study focuses on customer’s choice of the information sources, motivation behind it and different factors that influence the search behavior. Particular attention is paid to the purchase categorization and the differences in information search between products and services. The qualitative research method is chosen for this study. The data is gathered through ten theme interviews. Each participant of the interview describes his/her own search behavior in a product and a service case. The results indicate that consumer search behavior vary according to the purchase categorization, demographic, individual and situational factors. Moreover, the above-mentioned factors influence the purpose and position of the information search phase in a five-step decision making model.

In search of true friendship

The present text proposes a discussion on the concept of true friendship. The argument is grounded mostly on Aristotle's Nicomachean Ethics, Owen Flanagan's ethics as human ecology, and on contemporary authors' works about the Greek philosopher's concept of friendship. Given that human beings flourish through 1) exercising capacities, 2) being moral, and 3) having true friendships, difficulties to establish the level of trust required by true friendships turns the search itself (for them) morally valid.

Critical-point model to estimate yield loss caused by Asian soybean rust

ABSTRACTA model to estimate yield loss caused by Asian soybean rust (ASR) (Phakopsora pachyrhizi) was developed by collecting data from field experiments during the growing seasons 2009/10 and 2010/11, in Passo Fundo, RS. The disease intensity gradient, evaluated in the phenological stages R5.3, R5.4 and R5.5 based on leaflet incidence (LI) and number of uredinium and lesions/cm2, was generated by applying azoxystrobin 60 g a.i/ha + cyproconazole 24 g a.i/ha + 0.5% of the adjuvant Nimbus. The first application occurred when LI = 25% and the remaining ones at 10, 15, 20 and 25-day intervals. Harvest occurred at physiological maturity and was followed by grain drying and cleaning. Regression analysis between the grain yield and the disease intensity assessment criteria generated 56 linear equations of the yield loss function. The greatest loss was observed in the earliest growth stage, and yield loss coefficients ranged from 3.41 to 9.02 kg/ha for each 1% LI for leaflet incidence, from 13.34 to 127.4 kg/ha/1 lesion/cm2 for lesion density and from 5.53 to 110.0 kg/ha/1 uredinium/cm2 for uredinium density.

Impact of anthracnose on the yield of soybean subjected to chemical control in the north region of Brazil

ABSTRACT Losses due to soybean anthracnose, caused by Colletotrichum truncatum, have not been systematically quantified in the field, and the efficacy of chemical control of this disease is not known. This study shows an estimate of losses associated with the disease in soybean crops in the north of the country. Two trials with cv. M9144 RR were carried out in commercial fields in Tocantins State in the 2010/2011 and 2011/2012 growing seasons, in randomized blocks, with four replicates. Foliar applications were performed on plants at R1/R2 and R5.2 stages, employing CO2-pressurized equipment and application volume of 200 L ha-1. Nine fungicides and one untreated control were compared, and the disease gradients in the two seasons were obtained. The percentage of infected pods was calculated at the R6 stage. Grain yield ranged from 3,288 to 3,708 kg/ha in the untreated plots in 2010/2011 and 2011/2012, respectively, and from 3,282 to 4,110 kg/ha in the treated plots. In the 2010/2011 season, only azoxystrobin + cyproconazole significantly reduced the disease incidence, compared to untreated control plots, not differing from the remaining treatments. In the 2011/2012 season, there were no significant differences between treated and untreated plots. Highly significant correlations (p < 0.01) were found between yield and soybean anthracnose incidence on pods in both years (r = -0.85). For each 1% increment in the disease incidence, c. 90 kg/ha of soybean grain were lost. The current study determined that significant losses due to anthracnose occur in commercial crops in the north of the country and highlighted the limitation of chemical control as anthracnose management method.

The Effect of Dividend Yield to Stocks’ Performance on Bullish and Bearish Finnish Stock Market

This study examines the relationship between dividend yield and stock return over bullish and bearish Finnish stock market by testing for alpha and beta shifts across bull and bear markets. In addition, this study examines if various factors, such as a standard deviation of dividends, firm size and profitability have an effect on the size, of the firms’ dividends and systematic risk of the stocks. We divide stocks into five portfolios on the basis of their past average dividend yields and investigate if the highest yielding portfolios outperform the lowest yielding portfolios during the different market conditions. As a result, high yielding stocks were most stable during the examination period and offered downside protection on bear markets. However, a strategy of forming portfolios with past dividend yields led to negative alphas even in bull markets. Standard deviation of dividends, firm size and profitability were found to have no effect on the size of dividends and systematic risk of the stocks.

Growth and yield prediction using the modified Buckman model

A model to manage even-aged stands was developed using a modification of the Buckman model. Data from Eucalyptus urophylla and Eucalyptus cloeziana stands located in the Northern region of Minas Gerais State, Brazil were used in the formulation of the system. The proposed model generated precise and unbiased estimates in non-thinned stands.

Use data from permanent sampling points in growth and yield modeling

This study was carried to evaluate the efficiency of the Bitterlich method in growth and yield modeling of the even-aged Eucalyptus stands. 25 plots were setup in Eucalyptus grandis cropped under a high bole system in the Central Western Region of Minas Gerais, Brazil. The sampling points were setup in the center of each plot. The data of four annual mesurements were colleted and used to adjust the three model types using the age, the site index and the basal area as independent variables. The growths models were fitted for volume and mass of trees. The efficiency of the Bitterlich method was confirmed for generating the data for growth and yield modeling.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

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.

Soil salinization and maize and cowpea yield in the crop rotation system using saline waters

The use of saline water and the reuse of drainage water for irrigation depend on long-term strategies that ensure the sustainability of socio-economic and environmental impacts of agricultural systems. In this study, it was evaluated the effects of irrigation with saline water in the dry season and fresh water in the rainy season on the soil salt accumulation yield of maize and cowpea, in a crop rotation system. The experiment was conducted in the field, using a randomized complete block design, with five replications. The first crop was installed during the dry season of 2007, with maize irrigated with water of different salinities (0.8, 2.2, 3.6 and 5.0 dS m-1). The maize plants were harvested at 90 days after sowing (DAS), and vegetative growth, dry mass of 1000 seeds and grain yield were evaluated. The same plots were utilized for the cultivation of cowpea, during the rainy season of 2008. At the end of the crop, cycle plants of this species were harvested, being evaluated the vegetative growth and plant yield. Soil samples were collected before and after maize and cowpea cultivation. The salinity of irrigation water above 2.2 dS m-1 reduced the yield of maize during the dry season. The high total rainfall during the rainy season resulted in leaching of salts accumulated during cultivation in the dry season, and eliminated the possible negative effects of salinity on cowpea plants. However, this crop showed atypical behavior with a significant proportion of vegetative mass and low pod production, which reduced the efficiency of this strategy of crop rotation under the conditions of this study.

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.