Field data and prediction models of pesticide spray drift on coffee crop

The objective of this work was to generate drift curves from pesticide applications on coffee plants and to compare them with two European drift-prediction models. The used methodology is based on the ISO 22866 standard. The experimental design was a randomized complete block with ten replicates in a 2x20 split-plot arrangement. The evaluated factors were: two types of nozzles (hollow cone with and without air induction) and 20 parallel distances to the crop line outside of the target area, spaced at 2.5 m. Blotting papers were used as a target and placed in each of the evaluated distances. The spray solution was composed of water+rhodamine B fluorescent tracer at a concentration of 100 mg L-1, for detection by fluorimetry. A spray volume of 400 L ha-1 was applied using a hydropneumatic sprayer. The air-induction nozzle reduces the drift up to 20 m from the treated area. The application with the hollow cone nozzle results in 6.68% maximum drift in the nearest collector of the treated area. The German and Dutch models overestimate the drift at distances closest to the crop, although the Dutch model more closely approximates the drift curves generated by both spray nozzles.

Draft prediction models for soil engaging tines in two soils of Rio Grande do Sul, Brazil

The draft forces of soil engaging tines and theoretical analysis compared to existing mathematical models, have yet not been studied in Rio Grande do Sul soils. From the existing models, those which can get the closest fitting draft forces to real measure on field have been established for two of Rio Grande do Sul soils. An Albaqualf and a Paleudult were evaluated. From the studied models, those suggested by Reece, so called "Universal Earthmoving Equation", Hettiaratchi and Reece, and Godwin and Spoor were the best fitting ones, comparing the calculated results with those measured "in situ". Allowing for the less complexity of Reece's model, it is suggested that this model should be used for modeling draft forces prediction for narrow tines in Albaqualf and Paleudut.

CROSS-COUNTRY STUDY ON THE DETERMINANTS OF BANK FINANCIAL DISTRESS

ABSTRACTBank failures affect owners, employees, and customers, possibly causing large-scale economic distress. Thus, banks must evaluate operational risks and develop early warning systems. This study investigates bank failures in the Organization for Economic Co-operation and Development, the North America Free Trade Area (NAFTA), the Association of Southeast Asian Nations, the European Union, newly industrialized countries, the G20, and the G8. We use financial ratios to analyze and explore the appropriateness of prediction models. Results show that capital ratios, interest income compared to interest expenses, non-interest income compared to non-interest expenses, return on equity, and provisions for loan losses have significantly negative correlations with bank failure. However, loan ratios, non-performing loans, and fixed assets all have significantly positive correlations with bank failure. In addition, the accuracy of the logistic model for banks from NAFTA countries provides the best prediction accuracy regarding bank failure.

Comparison of capture methods for the diagnosis of adult anopheline populations from State of Mato Grosso, Brazil

INTRODUCTION: The present study compares human landing catches of primary malaria vectors with two alternative methods of capture: the Shannon trap and the Mosquito magnet. METHODS: This study used regression models to adjust capture data to a negative binominal distribution. RESULTS: Capture numbers and relative percentages obtained from the three methods vary strongly between species. The highest overall captures were obtained for Anopheles triannulatus with captures for the Shannon trap and the Mosquito magnet measuring more than 330% higher than captures obtained by human landings. For Anopheles darlingi, captures by the Shannon trap and the Mosquito magnet were about 14% and 26% of human landing catches, respectively. Another species with malaria transmission potential that was not sampled by human landing captures weascaptured by the Shannon trap and the Mosquito magnet (Anopheles oswaldoi). Both alternative sampling techniques can predict the human landing of Anopheles triannulatus, but without proportionality. Models for Anopheles darlingi counts, after totaling daily captures, are significant and proportional, but prediction models are more reliable when using the Shannon trap compared with the Mosquito magnet captures. CONCLUSIONS: These alternative capture methods can be partially recommended for the substitution of human landing captures or, at least, as complementary forms of monitoring for malarial mosquitoes.

Potential of Spectroradiometry to Classify Soil Clay Content

ABSTRACT Diffuse reflectance spectroscopy (DRS) is a fast and cheap alternative for soil clay, but needs further investigation to assess the scope of application. The purpose of the study was to develop a linear regression model to predict clay content from DRS data, to classify the soils into three textural classes, similar to those defined by a regulation of the Brazilian Ministry of Agriculture, Livestock and Food Supply. The DRS data of 412 soil samples, from the 0.0-0.5 m layer, from different locations in the state of Rio Grande do Sul, Brazil, were measured at wavelengths of 350 to 2,500 nm in the laboratory. The fitting of the linear regression model developed to predict soil clay content from the DRS data was based on a R2 value of 0.74 and 0.75, with a RMSE of 7.82 and 8.51 % for the calibration and validation sets, respectively. Soil texture classification had an overall accuracy of 79.0 % (calibration) and 80.9 % (validation). The heterogeneity of soil samples affected the performance of the prediction models. Future studies should consider a previous classification of soil samples in different groups by soil type, parent material and/or sampling region.

Bud dormancy in apple trees after thermal fluctuations

The objective of this work was to evaluate the effect of heat waves on the evolution of bud dormancy, in apple trees with contrasting chilling requirements. Twigs of 'Castel Gala' and 'Royal Gala' were collected in orchards in Papanduva, state of Santa Catarina, Brazil, and were exposed to constant (3°C) or alternating (3 and 15°C for 12/12 hours) temperature, combined with zero, one or two days a week at 25°C. Two additional treatments were evaluated: constant temperature (3°C), with a heat wave of seven days at 25°C, in the beginning or in the middle of the experimental period. Periodically, part of the twigs was transferred to 25°C for daily budburst evaluation of apical and lateral buds. Endodormancy (dormancy induced by cold) was overcome with less than 330 chilling hours (CH) of constant cold in 'Castel Gala' and less than 618 CH in 'Royal Gala'. A daily 15°C-temperature cycle did not affect the endodormancy process. Heat waves during endodormancy resulted in an increased CH to achieve bud requirements. The negative effect of high temperature depended on the lasting of this condition. Chilling was partly cancelled during dormancy when the heat wave lasted 36 continuous hours or more. Therefore, budburst prediction models need adjustments, mainly for regions with mild and irregular winters, such as those of Southern Brazil.

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.

The use of ALOS/PALSAR data for estimating sugarcane productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

Models and applications for risk assessment and prediction of Asian soybean rust epidemics

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.

Application of mathematical models for the prediction of adsorption isotherms in solid mixture for mango powder refreshment

Solid mixtures for refreshment are already totally integrated to the Brazilian consumers' daily routine, because of their quick preparation method, yield and reasonable price - quite lower if compared to 'ready-to-drink' products or products for prompt consumption, what makes them economically more accessible to low-income populations. Within such a context, the aim of this work was to evaluate the physicochemical and mineral composition, as well as the hygroscopic behavior of four different brands of solid mixture for mango refreshment. The BET, GAB, Oswim and Henderson mathematical models were built through the adjustment of experimental data to the isotherms of adsorption. Results from the physiochemical evaluation showed that the solid mixtures for refreshments are considerable sources of ascorbic acid and reductor sugar; and regarding mineral compounds, they are significant sources of calcium, sodium and potassium. It was also verified that the solid mixtures for refreshments of the four studied brands are considered highly hygroscopic.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Reconsolidation of the soil surface after tillage discontinuity, with and without cultivation, related to erosion and its prediction with RUSLE

Site-specific regression coefficient values are essential for erosion prediction with empirical models. With the objective to investigate the surface-soilconsolidation factor, Cf, linked to the RUSLE's prior-land-use subfactor, PLU, an erosion experiment using simulated rainfall on a 0.075 m m-1 slope, sandy loam Paleudult soil, was conducted at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul (EEA/UFRGS), in Eldorado do Sul, State of Rio Grande do Sul, Brazil. Firstly, a row-cropped area was excluded from cultivation (March 1995), the existing crop residue removed from the field, and the soil kept clean-tilled the rest of the year (to get a degraded soil condition for the intended purpose of this research). The soil was then conventional-tilled for the last time (except for a standard plot which was kept continuously cleantilled for comparison purposes), in January 1996, and the following treatments were established and evaluated for soil reconsolidation and soil erosion until May 1998, on duplicated 3.5 x 11.0 m erosion plots: (a) fresh-tilled soil, continuously in clean-tilled fallow (unit plot); (b) reconsolidating soil without cultivation; and (c) reconsolidating soil with cultivation (a crop sequence of three corn- and two black oats cycles, continuously in no-till, removing the crop residues after each harvest for rainfall application and redistributing them on the site after that). Simulated rainfall was applied with a Swanson's type, rotating-boom rainfall simulator, at 63.5 mm h-1 intensity and 90 min duration, six times during the two-and-half years of experimental period (at the beginning of the study and after each crop harvest, with the soil in the unit plot being retilled before each rainfall test). The soil-surface-consolidation factor, Cf, was calculated by dividing soil loss values from the reconsolidating soil treatments by the average value from the fresh-tilled soil treatment (unit plot). Non-linear regression was used to fit the Cf = e b.t model through the calculated Cf-data, where t is time in days since last tillage. Values for b were -0.0020 for the reconsolidating soil without cultivation and -0.0031 for the one with cultivation, yielding Cf-values equal to 0.16 and 0.06, respectively, after two-and-half years of tillage discontinuation, compared to 1.0 for fresh-tilled soil. These estimated Cf-values correspond, respectively, to soil loss reductions of 84 and 94 %, in relation to soil loss from the fresh-tilled soil, showing that the soil surface reconsolidated intenser with cultivation than without it. Two distinct treatmentinherent soil surface conditions probably influenced the rapid decay-rate of Cf values in this study, but, as a matter of a fact, they were part of the real environmental field conditions. Cf-factor curves presented in this paper are therefore useful for predicting erosion with RUSLE, but their application is restricted to situations where both soil type and particular soil surface condition are similar to the ones investigate in this study.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

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.