Abstract

A variance statistic was used to partition the total variance into that attributable to each step of a TME N assay procedure. Estimation of the TME N of wheat was used as an example. The variance statistic can also be used to optimize the design of a TME N experiment with respect to cost of the experiment and desired accuracy of the result. Experimental design optimization is accomplished by providing a functional relationship between the accuracy of the estimate and the number of replicates of feed, the number of birds used in the experiment, and the cost of each step. The variance statistic is also a useful tool for identifying and removing outliers and highly variable measurements. This feature was demonstrated with the chosen example data. Gross energy of the feed will explain approximately 50% of the variance of the TME N estimate depending on how many replicates are evaluated. Nitrogen content of the feed sample will explain approximately 40% of the total variance. It is recommended to replicate this measurement as many times as possible. Ten replicates were recommended for the example data. The energy content of excreta from fed birds represented the next largest source of variance, at approximately 4% of the total variance, respectively. If within-bird variance is large, better homogenization of the sample and more replicates are recommended. If among-bird variance is significantly different, more birds should be used. Nitrogen content of excreta from fed birds represented less than 2.5% of the total variance. Energy and nitrogen content of excreta from unfed birds combined represented less than 2% of the total variance, suggesting that the number of unfed birds and the amount of excreta sub-samples may be reduced without adversely affecting the accuracy of the TME N estimate. Variance due to the amount of excreta collected from the fed birds, and variance due to the amount of feed consumed by the birds, are expected to be small. This result suggested that force-feeding may not be necessary for accurate TME N estimates.

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