Nymph developmental time and survivorship, adult longevity, reproduction and body weight of Dichelops melacanthus (Dallas) feeding on natural and artificial diets

The biology of nymphs and adults of the neotropical pentatomid, Dichelops melacanthus (Dallas), feeding on the natural foods, soybean, Glycine max (L.) Merrill immature pods, and corn, Zea mays L. immature seeds, and on an artificial dry diet, was studied in the laboratory. Nymph developmental time was shorter on the natural foods (ca. 21-22 days) than on the artificial diet (28 days), and most nymphs reached adulthood on the food plants (55% on soybean and 73% on corn) than on the artificial diet (40%). Fresh body weight at adult emergence was similar and higher for females raised as nymphs on the natural foods, compared to females from nymphs raised on the artificial diet; for males, weights were similar on all foods. Mean (female and male) survivorship up to day 20, decreased from 55% on soybean to 40% on corn, down to 0% on the artificial diet. Total longevity for females was higher on soybean, while for males was similar on all foods. About three times more females oviposited on soybean than on corn, but fecundity/female was similar on both foods. On the artificial diet, only one out of 30 females oviposited. Fresh body weight of adults increased significantly during the first week of adult life, and at the end of the 3rd week, weight gain was similar on all foods.

The adequacy of artificial oviposition substrates for laboratory rearing of Piezodorus guildinii (Westwood) (Heteroptera, Pentatomidae)

Studies in the laboratory tested the suitability of synthetic wool string, cotton string, cheesecloth, and commercial cotton ball as artificial oviposition substrates for the small green stink bug, Piezodorus guildinii (Westwood) (Heteroptera, Pentatomidae). In confined cages, 54% of total egg masses was laid on synthetic wool string, 31% on cotton string, and only 15% on cheesecloth. In an additional test, the best substrate selected, synthetic wool string, received 92% of egg masses compared to 8% on the commonly used substrate, cotton ball. Synthetic wool string received the most egg masses of any size, in particular those in the range 11-20 eggs/mass. Because the eggs of P. guildinii are laid in two parallel double rows, the egg masses fit the wool string perfectly.

Selection of artificial hosts for oviposition by wild Anastrepha obliqua (Macquart) (Diptera, Tephritidae): influence of adult food and effect of experience

Several factors influence the selection of oviposition substrates by insects. The aim of the present work was to find answers to the following questions related to the oviposition behavior of Anastrepha obliqua. Can carbohydrates (glucose or sucrose) present in the adult diet have influence on the female preference for an oviposition substrate with similar composition? Can the previous experience with a host containing one of mentioned carbohydrates interfere in further selection of oviposition substrates? The results showed that the kind of carbohydrate present in the adult diet did not affect the female preference for an artificial oviposition substrate, neither when it was presented by itself nor in combination with brewer's yeast. The effect of experience in the oviposition behavior was observed when there was a previous contact with artificial oviposition substrates containing yeast and sucrose. The data are discussed in terms of the behavioral plasticity presented by this species in relation to feeding and oviposition behaviors.

Temporal Geobiotic Mapping: a conceptual mapping technique toward visualising geobiotic areas in cross-section

Geobiota are defined by taxic assemblages (i.e., biota) and their defining abiotic breaks, which are mapped in cross-section to reveal past and future biotic boundaries. We term this conceptual approach Temporal Geobiotic Mapping (TGM) and offer it as a conceptual approach for biogeography. TGM is based on geological cross-sectioning, which creates maps based on the distribution of biota and known abiotic factors that drive their distribution, such as climate, topography, soil chemistry and underlying geology. However, the availability of abiotic data is limited for many areas. Unlike other approaches, TGM can be used when there is minimal data available. In order to demonstrate TGM, we use the well-known area in the Blue Mountains, New South Wales (NSW), south-eastern Australia and show how surface processes such as weathering and erosion affect the future distribution of a Moist Basalt Forest taxic assemblage. Biotic areas are best represented visually as maps, which can show transgressions and regressions of biota and abiota over time. Using such maps, a biogeographer can directly compare animal and plant distributions with features in the abiotic environment and may identify significant geographical barriers or pathways that explain biotic distributions.

Use of artificial substrates of different colors for oviposition by the brown stink bug Euschistus heros (Hemiptera, Pentatomidae)

Aiming to contribute to a rearing methodology for the brown stink bug, Euschistus heros, in the laboratory, we evaluated oviposition on artificial substrates of different colors. During six days, oviposition was evaluated daily, by counting the total number of eggs, number of clutches, and eggs/clutch. Females laid 12,463 eggs, in 1,677 clutches, resulting in an average of 7.28 ± 0.44 eggs/clutch. Black, brown, and green felt had the most eggs and clutches. The results demonstrated that many colors are suitable as oviposition substrate for E. heros, providing information for the mass rearing of this insect.

New method for rearing Spodoptera frugiperda in laboratory shows that larval cannibalism is not obligatory

New method for rearing Spodoptera frugiperda in laboratory shows that larval cannibalism is not obligatory. Here we show, for the first time, that larvae of the fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera, Noctuidae), can be successfully reared in a cohort-based manner with virtually no cannibalism. FAW larvae were reared since the second instar to pupation in rectangular plastic containers containing 40 individuals with a surprisingly ca. 90% larval survivorship. Adult females from the cohort-based method showed fecundity similar to that already reported on literature for larvae reared individually, and fertility higher than 99%, with the advantage of combining economy of time, space and material resources. These findings suggest that the factors affecting cannibalism of FAW larvae in laboratory rearings need to be reevaluated, whilst the new technique also show potential to increase the efficiency of both small and mass FAW rearings.

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

O conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.

Rugosidade da superfície do solo sob diferentes sistemas de manejo e influenciada por chuva artificial

A rugosidade da superfície do solo é influenciada pelo manejo, formada em especial pelo tipo de preparo e reduzida pela ação da chuva, principalmente. O objetivo deste estudo foi avaliar a influência de diferentes sistemas de manejo do solo e da aplicação de chuva artificial na rugosidade da superfície do solo. Os tratamentos estudados resultaram da combinação de três sistemas de manejo do solo, semeadura direta (SD), preparo convencional (PC) e cultivo mínimo (CM), com três doses de resíduo vegetal seco de soja (Glycine max L. Merrill): 0, 2 e 4 Mg ha-1. Nas unidades experimentais foram aplicadas sete chuvas, com intensidade de precipitação pluvial de 60 mm h-1 e duração de 60 min cada, totalizando 420 mm de lâmina de chuva. A rugosidade foi avaliada imediatamente antes e após o preparo do solo e imediatamente após a aplicação de cada uma das sete chuvas artificiais. Obtiveram-se valores do índice de rugosidade ao acaso entre 1,88 e 5,41 mm na semeadura direta; entre 3,88 e 8,30 mm no preparo convencional; e entre 8,99 e 17,45 mm no cultivo mínimo. Concluiu-se que: as operações de preparo do solo aumentaram a rugosidade da sua superfície, em geral; o cultivo mínimo foi o sistema de preparo do solo que proporcionou os maiores valores de rugosidade ao acaso; e nos tratamentos semeadura direta com cobertura do solo a ação da chuva não promoveu decaimento do microrrelevo do solo.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Occurrence and Structure of Arbuscular Mycorrhizal Fungal Communities in Cassava after Cultivation of Cover Crops as Observed by the “PCR-DGGE” Technique

ABSTRACT Cassava (Manihot esculenta Crantz) is a highly mycotrophic crop, and prior soil cover may affect the density of arbuscular mycorrhizal fungi (AMFs), as well as the composition of the AMFs community in the soil. The aim of this study was to evaluate the occurrence and the structure of AMFs communities in cassava grown after different cover crops, and the effect of the cover crop on mineral nutrition and cassava yield under an organic farming system. The occurrence and structure of the AMFs community was evaluated through polymerase chain reaction (PCR) and denaturing gradient gel electrophoresis (DGGE). A randomized block experimental design was used with four replications. Six different cover crop management systems before cassava were evaluated: black oats, vetch, oilseed radish, intercropped oats + vetch, intercropped oats + vetch + oilseed radish, plus a control (fallow) treatment mowed every 15 days. Oats as a single crop or oats intercropped with vetch or with oilseed radish increased AMFs inoculum potential in soil with a low number of propagules, thus benefiting mycorrhizal colonization of cassava root. The treatments did not affect the structure of AMFs communities in the soil since the AMFs communities were similar in cassava roots in succession to different cover crops. AMFs colonization was high despite high P availability in the soil. The cassava crop yield was above the regional average, and P levels in the leaves were adequate, regardless of which cover crop treatments were used. One cover crop cycle prior to the cassava crop was not enough to observe a significant response in variables, P in plant tissue, crop yield, and occurrence and structure of AMFs communities in the soil. In the cassava roots in succession, the plant developmental stage affected the groupings of the structure of the AMF community.

INTELIGÊNCIA ARTIFICIAL: SISTEMAS ESPECIALISTAS NO GERENCIAMENTO DA INFORMAÇÃO

O principal objetivo deste artigo é propor um modelo para sistemas de gerenciamento da informação baseado em técnicas de inteligência artificial. O modelo propõe uma arquitetura de sistema especialista para gerenciamento da informação, sugerindo a utilização de um analisador semântico embutido na interface do usuário final. A abordagem enfatiza a dificuldade em se obter informações com precisão e qualidade, para apoiar tomadores de decisões, e a necessidade de prover os usuários finais com mecanismos, poderosos, capazes de analisar, selecionar e direcionar-lhes informações, de acordo com as necessidades e urgências de cada um

Characterization for plant height and flowering date in the biological species oat

The use of wild oat races in artificial hybridization with cultivated oat (Avena sativa L.) has been used as a way of increasing the variability. This work aimed to identify the variability for plant height and flowering date of groups of cultivated oat genotypes, wild introductions of A. fatua L. and segregating populations of natural crosses between A. sativa and A. fatua. Wide genetic variability was observed for both traits in the groups and between them. The wild group of A. fatua L. showed high plants with early maturity, but in the segregating group there was reduced plant height and early maturity. The wild introductions of A. fatua L. studied in this work can be used in oat breeding programs to increase genetic variability by transferring specific characters into the cultivated germ plasm.

Comportamento de fungos e de sementes de feijoeiro durante o teste de envelhecimento artificial

O teste de envelhecimento artificial, recomendado para avaliar o vigor de lotes de sementes, apresenta variabilidade em seus resultados; a ação dos fungos é considerada uma das causas dessa variabilidade. Este trabalho objetivou verificar os efeitos de diferentes períodos de envelhecimento artificial, no comportamento fisiológico de sementes do feijoeiro e dos fungos Aspergillus spp., Penicillium spp., Fusarium oxysporum e Colletotrichum lindemuthianum, inoculados artificialmente. Foram conduzidos testes de sanidade, germinação, tetrazólio, emergência, condutividade elétrica e lixiviação de potássio. As respostas obtidas, dependentes da duração do período de envelhecimento, indicaram efeitos da espécie fúngica presente. Concluiu-se que o teste de envelhecimento artificial associa a expressão de causas fisiológicas e sanitárias, o que prejudica a interpretação dos dados obtidos; a presença de fungos, principalmente de Aspergillus spp., pode ser considerada como capaz de interferir de modo negativo no desempenho das sementes envelhecidas artificialmente.