The effects of a series of balanced dietary protein levels on egg production and egg quality parameters of laying hens from 18 through 74 wk of age were investigated. One hundred forty-four pullets (Bovans) were randomly assigned to individual cages with separate feeders including 3 different protein level series of isocaloric diets. Diets were separated into 4 phases of 18-22, 23-32, 33-44, and 45-74 wk of age. The high protein (H) series contained 21.62, 19.05, 16.32, and 16.05% CP, respectively. Medium protein (M) and low protein (L) series were 2 and 4% lower in balanced dietary protein. The results clearly demonstrated that the balanced dietary protein level was a limiting factor for BW, ADFI, egg weight, hen day egg production (HDEP), and feed per kilogram of eggs. Feeding with the L series resulted in lower ADFI and HDEP (90.33% peak production) and more feed per kilogram of eggs compared with the H or M series (HDEP; 93.23 and 95.68% peak production, monthly basis). Egg weight responded in a linear manner to balanced dietary protein level (58.78, 55.94, and 52.73 g for H, M, and L, respectively). Feed intake of all hens, but especially those in the L series, increased considerably after wk 54 when the temperature of the house decreased due to winter conditions. Thus, hens fed the L series seemed particularly dependent on house temperature to maintain BW, ADFI, and HDEP. For egg quality parameters, percent yolk, Haugh units, and egg specific gravity were similar regardless of diets. Haugh units were found to be greatly affected by the variation of housing temperature (P = 0.025). Maximum performance cannot always be expected to lead to maximum profits. Contrary to the idea of a daily amino acid requirement for maximum performance, these results may be used to determine profit-maximizing levels of balanced dietary protein based on the cost of protein and returns from different possible protein levels that may be fed.

A given data set can be analyzed many ways, but only one is the correct analysis based on the design actually used when running the experiment. This work gives a tutorial-like illustration of the effects of the presence of a regression variable (or covariate) on the recorded responses in an experiment set up as a standard factorial design and shows how the analysis results are to be adjusted for the presence of covariates. An underlying assumption of a factorial model is that each of the treatments (e.g., diets) is randomly allocated to different subjects (hens). When many measurements (e.g., over time) are made on the same subject (hen), this independence assumption is violated; in these cases, the design is an example from the class of repeated measures designs. The difference in analysis between factorial designs and repeated measures designs is also discussed. Then, the 2 concepts are merged wherein the results for a repeated measures analysis have to be adjusted for the presence of covariates. The paper concludes with analyses on the results of egg production responses from an experiment in which repeated measurements were made on the same hens and in which an unanticipated temperature covariate was present.

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