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Development of Feed Composition Tables Using a Statistical Screening Procedure by H. Tran; A. Schlageter-Tello; A. Caprez; P.S. Miller; M.B. Hall; W.P. Weiss; P.J. Kononoff is a Agricultural and Biological Sciences article available to read on EtoBox.

What is Development of Feed Composition Tables Using a Statistical Screening Procedure about?

Millions of feed composition records generated annually by testing laboratories are valuable assets that can be used to benefit the animal nutrition community. However, it is challenging to manage, handle, and process feed composition data that originate from multiple sources, lack standardized feed names, and contain outliers. Efficient methods that consolidate and screen such data are needed to develop feed composition databases with accurate means and standard deviations (SD). Considering the interest of the animal science community in data management and the importance of feed composition tables for the animal industry, the objective was to develop a set of procedures to construct accurate feed composition tables from large data sets. A published statistical procedure, designed to screen feed composition data, was employed, modified, and programmed to operate using Python and SAS. The 2.76 million data received from 4 commercial feed testing laboratories were used to develop procedures and to construct tables summarizing feed composition. Briefly, feed names and nutrients across laboratories were standardized, and erroneous and duplicated records were removed. Histogram, univar

Who reads Development of Feed Composition Tables Using a Statistical Screening Procedure?

Author

H. Tran; A. Schlageter-Tello; A. Caprez; P.S. Miller; M.B. Hall; W.P. Weiss; P.J. Kononoff

Publisher

American Dairy Science Association; Elsevier Ltd; Elsevier BV (ISSN 0022-0302)

Published

2020

Language

EN

Field

Agricultural and Biological Sciences (Life Sciences)