Pattern of attack of a galling insect reveals an unexpected preference-performance linkage on medium-sized resources

Pattern of attack of a galling insect reveals an unexpected preference-performance linkage on medium-sized resources. The Plant Vigor Hypothesis (PVH) predicts oviposition preference and higher offspring performance on longer and fast-growing shoots, and although several studies have tested its predictions, long-term studies concerning the patterns of host selection by galling species are still lacking. The PVH was tested in this study using Bauhinia brevipes (Fabaceae) as the host of a leaf gall midge, Asphondylia microcapillata (Diptera, Cecidomyiidae) during three consecutive years. Shoots were collected from the same 80 plants between 2001 and 2003 and shoot length, number of healthy and galled leaves, gall number, and mortality factors were recorded. Nearly 600 galls were found on the 5,800 shoots collected. Medium-sized shoots supported from 46 to 70% of all galls, with greater gall survival rate in 2002 and 2003. A decrease in parasitism rate coupled with an increase in gall predation lead to a constant similar gall survivorship rate in all years (x = 22.7%). Although gall abundance varied among years (122 in 2001, 114 in 2002 and 359 in 2003) preference for longer shoots was not observed because the percentage of galled shoots and galled leaves were higher on medium shoot length classes in all years. The observed distribution of gall abundance and galled shoots were always greater than the expected distribution on medium shoot length classes. These findings do not support the PVH, and show that A. microcapillata can maximize the female preference and larval performance on medium-sized shoots of B. brevipes.

Does the aggressiveness of the prey modify the attack behavior of the predator Supputius cincticeps (Stål) (Hemiptera, Pentatomidae)?

Does the aggressiveness of the prey modify the attack behavior of the predator Supputius cincticeps (Stål) (Hemiptera, Pentatomidae)? The stink bug Supputius cincticeps (Stål) (Hemiptera, Pentatomidae) is a predator found in several Brazilian regions, which possesses desirable attributes as a natural control agent and in biological control programs. The aim of this study was to test if the attack behavior and predation success of S. cincticeps were affected by prey species. Larvae of Tenebrio molitor (L.) (Coleoptera, Tenebrionidae), Spodoptera frugiperda (J. E. Smith) (Lepidoptera, Noctuidae), and Thyrinteina arnobia (Stoll) (Lepidoptera, Geometridae) were offered to S. cincticeps in laboratory bioassays where predatory attack and prey defensive behaviors were observed for 2-hour periods. The attack behavior of S. cincticeps changed with the prey species offered. More than 25% of T. molitor and S. frugiperda larvae were immediately attacked, but T. arnobia was not immediately attacked by S. cincticeps. Successful attack (i.e., successful insertion of the predator stylets into the prey) depends on the region of the body attacked, with a greater proportion of successful attacks in the anterior than in the median or posterior regions. Larvae of T. arnobia and S. frugiperda displayed a sequence of abrupt head and body movements in response to S. cincticeps attack. Attempts of predation were more successful on T. molitor and S. frugiperda than on T. arnobia. Information about the differential attack behavior of S. cincticeps on different prey species is important for designing successful biological control programs using this hemipteran predator.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

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 %.

Factors affecting attack rate of whitefly on the eggplant

The objective of this study was to determine the effects of weather, predators and parasitoids, canopy height and plant age, leaf chemical composition, levels of leaf N and K and leaf trichomes on the intensity of Bemisia tabaci attack on Solanum melongena. A higher density of nymph and whitefly adults was recorded at the base and medium of the plant compared to the apex. A higher number of eggs was observed on the medium part than on the apical and base part of the plants dossel. An increase in the density of whitefly is associated with an increase in temperature.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

Factors affecting the attack rate of Bemisia tabaci on cucumber

The objective of this work was to determine the effects of rainfall, temperature, predators, parasitoids, plant age, leaf chemical composition, levels of leaf nitrogen and potassium, besides density of leaf trichomes, on attack intensity of Bemisia tabaci biotype B on the Cucumis sativus. An increase in the number of whitefly adults and nymphs per leaf was observed with plant aging. A higher number of whitefly adults per leaf and eggs cm-2 was verified in the apical part than in the middle and bottom part of the plants canopy. However, the higher number of whitefly nymphs was observed in the mid-part than in the apical and bottom part of the plant canopy. The incidence of whitefly nymphs was negatively affected with foliar nitrogen. Pentacosane and octacosane positively affected whitefly adults and the first compound also affected the nymphs of this pest species.

Whitefly, aphids and thrips attack on cabbage

The objective of this work was to investigate the relationships between predators and parasitoids, leaf chemical composition, levels of leaf nitrogen and potassium, total rainfall, relative humidity, daylight and median temperature on the intensity of whitefly, aphid, and thrips attack on cabbage. Whitefly, aphids and thrips population tended to proliferate in the final stage of plant or reached a peak population about 40 days after plantation. The whitefly and thrips tended to increase with an increase in the median temperature. A dependence of Cheiracanthium inclusum and Adialytus spp. populations on whitefly and aphids populations, respectively, was observed. No significant effect was detected between K and nonacosane leaf content and aphid population. However, an increase in leaf N content was followed by a decrease of this insect population. No significant relation was observed between leaf N, K and nonacosane and whitefly and thrips populations. Highest nonacosane levels were observed in plants 40 days after transplant, and relative humidity correlated negatively with nonacosane. Natural enemies, especially the parasitoid Adialytus spp. and the spiders can be useful controlling agents of the whitefly and aphids in cabbage. Median temperature can increase whitefly and thrips populations.

Leaf-cutting ant attack in initial pine plantations and growth of defoliated plants

The objective of this work was to evaluate the natural attack by Acromyrmex crassispinus in initial Pinus taeda plantations without control measures against ants, as well as the effect of defoliation in seedlings of P. taeda. Evaluations of the attack of leaf-cutting ants on P. taeda plantations were done monthly in the first six months, then 9 and 12 months after planting. The percentages of plants that were naturally attacked by ants were registered. The effect of defoliation was evaluated by artificial defoliation, simulating the natural patterns of attack by A. crassispinus on P. taeda seedlings. The natural attack of A. crassispinus was greater during the first months after planting, being more intense in the first 30 days. Artificial defoliation indicated that there were no significant losses in diameter and height in plants with less than 75% defoliation. However, there were significant losses in diameter and height in plants with 100% defoliation, independently of the cut of the apical meristem, and also plant death. The control of leaf-cutting ants in P. taeda plantings, in which A. crassispinus is the most frequent leaf-cutting ant, should be intense only at the beginning of planting, since the most severe attacks occur during this time.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Harvesting and wood transport planning with SNAP III program (Scheduling and Network Analysis Program) in a pine plantation in Southeast Brazil

The objective of this study was to verify the potential of SNAP III (Scheduling and Network Analysis Program) as a support tool for harvesting and wood transport planning in Brazil harvesting subsystem definition and establishment of a compatible route were assessed. Initially, machine operational and production costs were determined in seven subsystems for the study area, and quality indexes, construction and maintenance costs of forest roads were obtained and used as SNAP III program input data. The results showed, that three categories of forest road occurrence were observed in the study area: main, secondary and tertiary which, based on quality index, allowed a medium vehicle speed of about 41, 30 and 24 km/hours and a construction cost of about US$ 5,084.30, US$ 2,275.28 and US$ 1,650.00/km, respectively. The SNAP III program used as a support tool for the planning, was found to have a high potential tool in the harvesting and wood transport planning. The program was capable of defining efficiently, the harvesting subsystem on technical and economical basis, the best wood transport route and the forest road to be used in each period of the horizon planning.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Experimental evaluation of the performance of a wireless sensor network in agricultural environments

The aim of this study was to perform an experimental study to evaluate the proper operation distance between the nodes of a wireless sensor network available on the market for different agricultural crops (maize, physic nut, eucalyptus). The experimental data of the network performance offers to farmers and researchers information that might be useful to the sizing and project of the wireless sensor networks in similar situations to those studied. The evaluation showed that the separation of the nodes depends on the type of culture and it is a critical factor to ensure the feasibility of using WSN. In the configuration used, sending packets every 2 seconds, the battery life was about four days. Therefore, the autonomy may be increased with a longer interval of time between sending packets.

Silting in the dense reservoir network of the Pereira de Miranda catchment

This study aims to analyze the impacts of the reservoir network within Pereira de Miranda - CE catchment (also called Pentecoste) over sediment transport and storage capacity of the system. The survey of the "damming" was carried out using satellite images. We identified 502 erosion units, derived from overlaying maps of the Universal Soil Loss Equation parameters, which allowed the estimation of localized erosion in the basin and identification of areas potentially generating sediment. In order to estimate silting in Pentecoste reservoir, different system structure scenarios were considered. An average erosion rate of 59 t ha-1year-1 was estimated. According to the model, the silting of Pentecoste reservoir may vary from 1.1 to 2.6% per decade, depending on the scenario considered. It is also observed that the reservoirs upstream can retain up to 58% of the sediment that would reach the Pentecoste reservoir. Very small reservoirs with a capacity of up to 100,000 m³, although representing only 1.83% of the system water availability, are able to retain almost 8% of total sediment produced.