Sunflower Meal and Supplementation of an Enzyme Complex in Layer Diets

ABSTRACTThe objective of this experiment was to evaluate the performance of 64- to 79-wk-old laying hens fed diets supplemented with an enzyme complex (EC) and containing increasing sunflower meal (SFM) levels. A total of 384 Hy-Line Brown layers were distributed according to a randomized block design in a 4 × 2 factorial arrangement (four levels of SFM, and inclusion or not of EC), with eight replicates of six birds each unit. The levels of SFM inclusion were 0, 8, 16 and 24%, utilized in two distinct diets. Diets were calculated to meet all the nutritional requirements of birds, except for the nutrients that would be made available by the nutritional matrix of the enzyme complex, with or without utilization of EC. The parameters analyzed were feed intake (g/bird/day), egg production (%/bird/day), egg weight, egg mass (g/bird/day), feed conversion ratio per egg mass, feed conversion ratio per dozen eggs, body weight gain, egg components (yolk, albumen and eggshell) and the economic efficiency index (EEI). There was no interaction between EC addition and the SFM levels in the diet. The addition of EC in the diets of laying hens did not affect egg productive or components parameters. The increase in the SFM levels in the diet presented quadratic effect on egg production and feed conversion ratio per dozen eggs, with calculated optimal sunflower meal inclusion levels of 6.72% and 5.83%, respectively, for each parameter. The best economic efficiency per dozen eggs was obtained with the diet with 16.0% SFM and EC inclusion, whereas per egg mass with the diet with of 24.0% SFM and no EC addition

GAUSSIAN SPATIAL LINEAR MODEL OF SOYBEAN YIELD USING BOOTSTRAP METHODS

ABSTRACT This study aims to quantify the uncertainties associated to the parameters of a Gaussian spatial linear model (GSLM) and the assumption of normality residuals in the modeling of the spatial dependence of the soybean yield as a function of soil chemical attributes. The spatial bootstrap methods were used to determine the point and interval estimators associated with the model parameters. Hypothesis tests were carried out on the significance of the model parameters and the quantile-quantile probability plot was elaborated to verify the data normality. The uncertainties associated to the parameters of the spatial dependence structure were quantified and the potassium content, phosphorus content and soil pH covariates were significant to explain the soybean yield mean. These covariates were used in the elaboration of a new model, which provided the elaboration of a contour map of soybean yield. Analysis of the quantile-quantile plot indicated that soybean yield data follow a normal probability distribution