Adubação nitrogenada e potássica em goiabeiras ‘paluma’: I. efeito na produtividade e na qualidade dos frutos para industrialização

The nutrition of the orchards is the major factor of productivity, being necessary to know the proper doses of fertilizers and their influence on fruit quality attributes for industrialization. This study evaluated the effects of different doses of nitrogen and potassium on the productivity of guava trees and also on the values of pH, soluble solids (SS), titratable acidity (TA) and pulp/kernel ratio of guavas. The experiment was conducted at Vista Alegre do Alto, SP in an irrigated 'Paluma'guava orchard, 7 years old, managed with pruning during three consecutive cycles of production. The soil of the area was dystrophic Ultisol. The experimental design was the randomized blocks, in factorial, with four nitrogen doses (0, 0.5, 1 and 2 kg of N plant(-1)) and four of potassium (0, 0.55, 1.1 and 2.2 kg of K2O plant(-1)), with three replications. Nitrogen fertilization increased productivity and the pH of the fruit, being explained by the quadratic polynomial regression models; reduced linearly the pulp/kernel ratio and do not influenced the SS and TA values. On the other hand, potassium fertilization and N x K interaction had no significant effects on productivity and the other characteristics evaluated.

Aplicação de modelos de regressão aleatória utilizando diferentes estruturas de dados

A total of 3.035 lactations of Holstein cows from four farms in the Southeast, to check the influence of data structure of milk yield on the genetic parameters. Four dataset with different structures were tested, weekly controls (CW) with 122.842 controls, monthly controls (CM) 30.883, bimonthly controls (CB) with 15,837 and quarterly controls (CQ) with 12,702. The random regression model was used and was considered as random additive genetic and permanent environment effects, fixed effects of the contemporary groups (herd-year-month of test-day) and age of cow (linear and quadratic effects). Heritability estimates showed similar trends among the data files analyzed, with the greatest similarity between dataset CS, CM and CB. The dataset submitted all the CB estimates of genetic parameters analyzed with the same trend and similar magnitude to the CS and CM dataset, allowing the claim that there was no influence of the data structure on estimates of covariance components for the dataset CS, CM and CB. Thus, milk recording could be accomplished in a CB structure.

A linguagem do consenso nos marcos da regressão cultural: o fim da consciência e a construção do neocontratualismo de Jürgen Habermas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influência dos fatores organizacionais no sucesso de projetos no setor automotivo

Pós-graduação em Engenharia de Produção - FEB

Crescimento, características nutricionais e fisiológicas de progênies de Eucalyptus grandis W. Hill. Ex Maiden sob diferentes doses de fósforo

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Temperatura, luz e tolerância à dessecção na germinação de sementes de açaí-do-Amazonas

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Relação ideal dos aminoácidos essenciais para mantença, crescimento e produção de aves

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model