Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

INCLUDING RISK IN ECONOMIC FEASIBILITY ANALYSIS:A STOCHASTIC SIMULATION MODEL FOR BLUEBERRY INVESTMENT DECISIONS IN CHILE

ABSTRACT The traditional method of net present value (NPV) to analyze the economic profitability of an investment (based on a deterministic approach) does not adequately represent the implicit risk associated with different but correlated input variables. Using a stochastic simulation approach for evaluating the profitability of blueberry (Vaccinium corymbosum L.) production in Chile, the objective of this study is to illustrate the complexity of including risk in economic feasibility analysis when the project is subject to several but correlated risks. The results of the simulation analysis suggest that the non-inclusion of the intratemporal correlation between input variables underestimate the risk associated with investment decisions. The methodological contribution of this study illustrates the complexity of the interrelationships between uncertain variables and their impact on the convenience of carrying out this type of business in Chile. The steps for the analysis of economic viability were: First, adjusted probability distributions for stochastic input variables (SIV) were simulated and validated. Second, the random values of SIV were used to calculate random values of variables such as production, revenues, costs, depreciation, taxes and net cash flows. Third, the complete stochastic model was simulated with 10,000 iterations using random values for SIV. This result gave information to estimate the probability distributions of the stochastic output variables (SOV) such as the net present value, internal rate of return, value at risk, average cost of production, contribution margin and return on capital. Fourth, the complete stochastic model simulation results were used to analyze alternative scenarios and provide the results to decision makers in the form of probabilities, probability distributions, and for the SOV probabilistic forecasts. The main conclusion shown that this project is a profitable alternative investment in fruit trees in Chile.

Variables cognitivas y metacognitivas en la resolución de problemas de química: propuesta de estrategias didácticas

This paper presents and classifies the cognitive and metacognitive variables involved in the processes that students execute in problem solving. Moreover, it shows how these variables affect the students success in problem solving. These variables are classified in: piagetian and neo-piagetian, representational, metacognitive and transfer of learning. In the first group of variables it is discussed formal reasoning ability and other neo-piagetian factors. In the second group of variables it is analysed mental models and external representations. Implications for chemistry education are collected as a proposal of didactic strategies in the classroom.

Least squares regression with errors in both variables: case studies

Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS). In this work, the use of orthogonal distance regression (ODR) is discussed as an alternative approach in order to take into account the error in the x variable. Four examples are presented to illustrate deviation between the results from both regression methods. The examples studied show that, in some situations, ODR coefficients must substitute for those of OLS, and, in other situations, the difference is not significant.

Influencia de variables de formulación en la viscosidad de emulsiones de surfactante aniónico-aceite-agua

Phase transition and viscosity behavior of emulsified systems were studied after modifying their physicochemical formulation. Effects of concentration and nature of salts and n-alcohols, and water/oil relation on the rheological properties of emulsions were also studied. Pre-equilibrated systems were emulsified according to an agitation procedure, and the viscosity (cP) was measured at different shear rates ranging from 1 to 300 s-1. The phase behavior, as well as the emulsion type based on electrolytic conductivity, was observed. Several interpretations of the flow and viscosity curves of emulsions were made through the estimation of rheological parameters such as consistency index "k" and behavior index "n".

RELATIONSHIP BETWEEN CLIMATE VARIABLES, TRUNK GROWTH RATE AND WOOD DENSITY OF Eucalyptus grandis W. Mill ex Maiden TREES

ABSTRACT Climatic conditions stimulates the cambial activity of plants, and cause significant changes in trunk diameter growth and wood characteristics. The objective of this study was to evaluate the influence of climate variables in the diameter growth rate of the stem and the wood density of Eucalyptus grandis trees in different classes of the basal area. A total of 25 Eucalyptus trees at 22 months of age were selected according to the basal area distribution. Dendrometer bands were installed at the height of 1.30 meters (DBH) to monitor the diameter growth every 14 days, for 26 months. After measuring growth, the trees were felled and wood discs were removed at the DBH level to determine the radial density profile through x-ray microdensitometry and then re-scale the average values every 14 days. Climatic variables for the monitoring period were obtained and grouped every 14 days. The effect of the climate variables was determined by maximum and minimum growth periods in assessing trunk growth. These growth periods were related with precipitation, average temperature and relative air humidity. The re-scaled wood density values, calculated using the radial growth of the tree trunks measured accurately with steel dendrometers, enabled the determination of the relationship of small changes in wood density and the effect of the climatic variations and growth rate of eucalyptus tree trunks. A high sensitivity of the wood density to variation in precipitation levels was found.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

The increasing demand of consumer markets for the welfare of birds in poultry house has motivated many scientific researches to monitor and classify the welfare according to the production environment. Given the complexity between the birds and the environment of the aviary, the correct interpretation of the conduct becomes an important way to estimate the welfare of these birds. This study obtained multiple logistic regression models with capacity of estimating the welfare of broiler breeders in relation to the environment of the aviaries and behaviors expressed by the birds. In the experiment, were observed several behaviors expressed by breeders housed in a climatic chamber under controlled temperatures and three different ammonia concentrations from the air monitored daily. From the analysis of the data it was obtained two logistic regression models, of which the first model uses a value of ammonia concentration measured by unit and the second model uses a binary value to classify the ammonia concentration that is assigned by a person through his olfactory perception. The analysis showed that both models classified the broiler breeder's welfare successfully.

Method of numerical correction of errors occasioned by delay of records during the monitoring of environmental variables of interest for animal production

Thermal and air conditions inside animal facilities change during the day due to the influence of the external environment. For statistical and geostatistical analyses to be representative, a large number of points spatially distributed in the facility area must be monitored. This work suggests that the time variation of environmental variables of interest for animal production, monitored within animal facility, can be modeled accurately from discrete-time records. The aim of this study was to develop a numerical method to correct the temporal variations of these environmental variables, transforming the data so that such observations are independent of the time spent during the measurement. The proposed method approached values recorded with time delays to those expected at the exact moment of interest, if the data were measured simultaneously at the moment at all points distributed spatially. The correction model for numerical environmental variables was validated for environmental air temperature parameter, and the values corrected by the method did not differ by Tukey's test at 5% significance of real values recorded by data loggers.

Evaluation of tractive performance of four agricultural tractors in laterally inclined terrain

The present work aimed to evaluate the tractive performance of four agricultural tractors with auxiliary front traction in function of six lateral inclinations in a lateral track of trials, which belongs to the Agronomic Sciences Faculty from the São Paulo State University, Campus of Botucatu. The lateral inclinations were 0; 5; 10; 15; 20 and 25 degrees. In all of these situations, the tractors operated with predetermined load of an imposed traction to the pulled tractor of 40 kN in the inclination of 0 degrees. Hence a delineation in random blocks was used, considering six inclinations and four tractors, and with three repetitions for each treatment. The analyzed variables were slippage, tractive force, hourly fuel consumption, and speed. It was concluded that the pneumatic tire configurations influenced in the tractive performance of the tractors, as they increased the lateral inclinations of the terrain.

Tabular cusum control charts of chemical variables applied to the control of surface water quality

The aim of this study was to evaluate the application possibility of tabular CUSUM control charts in the quality control of chemical variables in surface water. It was performed bibliographic and field research to collect water samples from 2003 to 2009, totaling 30 samples, some monthly and others semi-annual in order to observe the variables that regulate water quality. It was found that these charts may be applied to control the quality of river water; showing to be effective in the perception of changes during the process, especially for small samples (n=1) which there is no repetition as in this research. It was also concluded that the Mandurim River does not presents significant levels of pollution.

Management zones in agriculture acording to the soil and landscape variables

The study of spatial variability of soil and plants attributes, or precision agriculture, a technique that aims the rational use of natural resources, is expanding commercially in Brazil. Nevertheless, there is a lack of mathematical analysis that supports the correlation of these independent variables and their interactions with the productivity, identifying scientific standards technologically applicable. The aim of this study was to identify patterns of soil variability according to the eleven physical and seven chemical indicators in an agricultural area. It was used two multivariate techniques: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). According to the HCA, the area was divided into five management zones: zone 1 with 2.87ha, zone 2 with 0.8ha, zone 3 with 1.84ha, zone 4 with 1.33ha and zone 5 with 2.76ha. By the PCA, it was identified the most important variables within each zone: V% for the zone 1, CTC in the zone 2, levels of H+Al in the zone 4 and sand content and altitude in the zone 5. The zone 3 was classified as an intermediate zone with characteristics of all others. According to the results it is concluded that it is possible to separate into groups (management zones) samples with the same patterns of variability by the multivariate statistical techniques.

Trickle irrigation: effects on papaya crop

The objective of this study was to evaluate growth and yield of papaya cv. Sunrise solo under trickle irrigation system configurations. A random block design was used with six treatments and four replications. Treatments were: T1- a 32 L h-1 micro sprinkler; T2 - a 43 L h-1 micro sprinkler and T3 - a 60 L h-1 micro sprinkler for four plants; T4 - Drip system with four emitters per plant on one lateral line per crop row; T5 - Drip system with eight emitters per plant on two laterals line per crop row; T6 - Drip system with four emitters per plant on one lateral line distributed as pig tail. The dripper flow rate was 4 L h-1and they were apart each other 0.50 m. Crop growth variables were measured every two weeks. Production variables were evaluated during harvest. The treatment that presented superiority was irrigated by micro sprinkler system with flow rate of 43 L h-1.

Weather variables, water balance, growth, and agro industrial yield of sugarcane

The objective of this study was to evaluate the meteorological variables, water deficiency, growth, and agro-industrial yield of sugarcane varieties: RB72454, RB863129, RB867515, RB92579, RB93509, RB931003, RB951541, and RB971755, in rainfed crop in two harvests in the Rio Largo-AL region. The meteorological variables were obtained in an automatic station and water balance was done by Thornthwaite & Mather method. During the study period, the air temperature ranged from 16.6 to 35.9 ºC. In the first production cycle rained 1,806 mm and the crop evapotranspiration was 1,775 mm. In the second cycle, the rainfall totaled 1,632 mm and the crop evapotranspiration was 1,290 mm. The average water excess of two production cycles was 689 mm and the water deficit totaled 665 mm. The average agricultural productivity in the plant was 86.8 t ha-1, in the first ratoon was 75.2 t ha-1 and the agro-industrial yield average was 12.9 and 10.9 tons of sugar per hectare in the plant and first ratoon, respectively. The air temperature was not limiting to the growth of sugarcane and the rainfall was higher than the crop evapotranspiration, but due to poor distribution of the rains there was water deficit. The most productive varieties were RB93509, RB92579, and RB863129.

Redundant variables and the quality of management zones

ABSTRACT Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran’s bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.