Análise do investimento em climatização para bovinos de leite em sistema de alojamento free-stall

The difficulty in adapting European dairy cows breeds in Brazil affect considerably the milk production sector. Brazilian climatic conditions are not totally favorable and the development of new tecnologies is needed for the animals express their genetic potential, as well as their best feed conversion. An economical analysis of the applied investment in the free-stall climatization equipment in dairy housing, for estimating studies related to profit, possibility of return investment as well as time for this return is necessary. The objective of this research was to evaluate the influence of climatization investment in the milk production process and analyze the economical aspect of this investment. There were used 470 high productive dairy cows with genetic and morphologic homogeneous characteristics, and analyzed in two similar periods. Investment calculations were done using Excell®. The results were satisfactory and the invested capital was proved to return to the producer in a short term, 57 days.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.