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Development of feed composition tables using a statistical screening procedure.

H Tran¹, A Schlageter-Tello², A Caprez³

¹Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583; National Animal Nutrition Program, University of Kentucky, Lexington 40546; Land O'Lakes Inc., Arden Hills, MN 55126.

Journal of Dairy Science

|March 2, 2020

Summary

This summary is machine-generated.

Developing efficient data processing methods is crucial for accurate animal feed composition databases. This study presents a robust procedure to consolidate and screen millions of feed records, improving data quality for animal nutrition.

Keywords:

clustering database nutrient principal component analysis

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Area of Science:

Animal Nutrition and Data Science
Utilizing large-scale datasets for improved animal feed analysis.

Background:

Millions of annual feed composition records are valuable but challenging to manage due to varied sources and naming conventions.
Accurate feed composition tables are essential for the animal nutrition industry and animal producers.
Existing data management methods struggle with standardization and outlier detection in feed composition data.

Purpose of the Study:

To develop and implement procedures for constructing accurate feed composition tables from large, multi-source datasets.
To standardize feed names and nutrients, and remove erroneous or duplicate records.
To create a robust tool for processing and updating feed composition data.

Main Methods:

Modified and programmed a published statistical procedure using Python and SAS for data screening.
Standardized feed names and nutrients across four commercial laboratories' data.
Employed histogram, univariate, principal component analyses, and clustering to identify and remove outliers and subgroup feeds.

Main Results:

Processed 2.76 million records, resulting in a final database of 1.48 million records across 174 feeds.
Effectively classified feed by-products, forages, and oilseeds into distinct sub-populations.
Developed procedures demonstrated robustness in constructing and updating large feed datasets.

Conclusions:

The developed procedures provide a robust tool for constructing and updating large feed composition datasets.
This approach enhances data accuracy and can be utilized by various stakeholders in the animal industry.
Automated data processing and analysis improve the reliability of feed composition tables.